Search

Series List Filter

What is Machine Learning and Why It Matters: Everything You Need to Know

If you are a machine learning enthusiast and stay in touch with the latest developments, you would have definitely come across the news “Machine learning identifies links between the world's oceans”. Wait, we all know how complex it would be to analyse a concept such as oceans and their behaviour which would undoubtedly involve billions of data points associated with many critical parameters such as wind velocities, temperatures, earth’s rotation and many such. Doesn’t this piece of information gives you a glimpse of the wondrous possibilities of machine learning and its potential uses? And this is just a drop in the ocean!As you move across this post, you would get a comprehensive idea of various aspects that you ought to know about machine learning.What is Machine Learning and Why It Matters?Machine learning is a segment of artificial intelligence. It is designed to make computers learn by themselves and perform operations without human intervention, when they are exposed to new data. It means a computer or a system designed with machine learning will identify, analyse and change accordingly and give the expected output when it comes across a new pattern of data, without any need of humans.The power behind machine learning’s self-identification and analysis of new patterns, lies in the complex and powerful ‘pattern recognition’ algorithms that guide them in where to look for what. Thus, the demand for machine learning programmers who have extensive knowledge on working with complex mathematical calculations and applying them to big data and AI is growing year after year.Machine learning, though a buzz word only since recent times, has conceptually been in existence since World War II when Alan Turing’s Bombe, an enigma deciphering machine was introduced to the world. However, it's only in the past decade or so that there has been such great progress made in context to machine learning and its uses, driven mainly by our quest for making this world more futuristic  with lesser human intervention and more precision. Pharma, education technology, industries, science and space, digital inventions, maps and navigation, robotics – you name the domain and you will have instances of machine learning innovations made in it.The Timeline of Machine Learning and the Evolution of MachinesVoice activated home appliances, self-driven cars and online marketing campaigns are some of the applications of machine learning that we experience and enjoy the benefit of in our day to day life. However, the development of such amazing inventions date back to decades. Many great mathematicians and futuristic thinkers were involved in the foundation and development of machine learning.A glimpse of the timeline of machine learning reveals many hidden facts and the efforts of great mathematicians and scientists to whom we should attribute all the fruits that we are enjoying today.1812- 1913: The century that laid the foundation of machine learningThis age laid the mathematical foundation for the development of machine learning. Bayes’ theorem and Markovs Chains took birth during this period.Late 1940s: First computers Computers were recognised as machines that can ‘store data’. The famous Manchester Small-Scale Experimental Machine (nicknamed 'The Manchester Baby') belongs to this era.1950: The official Birth of Machine LearningDespite many researches and theoretical studies done prior to this year, it was the year 1950 that is always remembered as the foundation of the machine learning that we are witnessing today. Alan Turing, researcher, mathematician, computer genius and thinker, submitted a paper where he mentioned something called ‘imitation game’ and astonished the world by questioning “Can Machines Think?”. His research grabbed the attention of the BBC which took an exclusive interview with Alan.1951: The First neural networkThe first artificial neural network was built by Marvin Minsky and Dean Edmonds this year. Today, we all know that artificial neural networks play a key role in the thinking process of computers and machines. This should be attributed to the invention made by these two scientists.1974: Coining of the term ‘Machine Learning’Though there were no specific terms till then for the things that machines did by thinking on their own, it was in 1974 that the term ‘machine learning’ was termed. Other words such as artificial intelligence, informatics and computational intelligence were also proposed the same year.1996: Machine beats man in a game of chessIBM developed its own computer called Deep Blue, that can think. This machine beat the world famous champion in chess, Garry Kasparov. It was then proved to the world that machines can really think like humans.2006-2017: Backpropagation, external memory access and AlphaGoBack propagation is an important technique that machines use for image recognition. This technique was developed in this period of time.Besides in 2014, a neural network developed by DeepMind, a British based company, developed a neural network that can access external memory and get things done.In 2016, AlphaGo was designed by DeepMind researchers. It beat the world famous Go players Lee Sedol and Ke Jie and proved that machines have come a long way.What’s next?Scientists are talking about ‘singularity’ –a phenomenon that would occur if humans develop a humanoid robot that could think better than humans and will recreate itself. So far, we have been witnessing how AI is entering our personal lives too in the form of voice activated devices, smart systems and many more. The results of this singularity – we shall have to wait and watch!Basics of Machine LearningTo put it simply, machine learning involves learning by machines. It means computers learn and there are many concepts, methods, algorithms and processes involved in making this happen. Let us try to understand some of the more important machine learning terms.Three concepts – artificial intelligence, machine learning and deep learning – are often thought to be synonymous. Though they belong to the same family, conceptually they are different.Machine LearningIt implies that machines can ‘learn on their own’ and give the output without any need of programming explicitly.Artificial IntelligenceThis term means machines can ‘think on their own’ just like humans and take decisions.Deep LearningThis involves creation of artificial neural networks which can think and act based on algorithms.How do machines learn?Quite simply, machines learn just like humans do. Humans learn from their training, experiences and through teachers. Sometimes they use knowledge that is fed into their brains, or sometimes take decisions by analysing the current situation using their past experiences.Similarly, machines learn from the inputs given to them which tell them which is right and which is wrong. Then they are given data that they would have to analyse based on the training they have received so far. In some other cases, they do not have any idea of which is right or wrong, but just take the decision based on their own experiences. We will analyse the various concepts of learning and the methods involved.How Machine Learning Works?The process of machine learning occurs in five steps as shown in the following diagram.The steps are explained in simple words below:Gathering the data includes data collection from varied, rich and dense content of various formats and types. In real time, this includes feeding the data from different sources such as text files, word documents or excel sheets.Data preparation involves extracting the actual data out of the entire content fed. Only the data that really makes sense to the machine is used for processing. This step also involves checking for missing data, unwanted data and treatment of outliers.Training involves using an appropriate algorithm and modelling the data. The data filtered in the second step is split into two parts and a part of it is used as training data and the second part is used as reference data. The training data is used to create the model.Evaluating the model includes testing its accuracy. To verify its accuracy to the fullest, the model so developed is tested on such data which is not present in the data during the second step.Finally, the performance of the machine is improved by choosing a different model that suits the different type of data that is present altogether. This is the step where the machine thinks and rethinks in selecting the model best suited for various types of data.Examples of Machine LearningThe below examples will help you understand where machine learning is used in real time:Speech RecognitionVoice based searching and call rerouting are the best examples for speech recognition using machine learning. The principle lies in translating  spoken words into text and then segmenting them on the basis of their frequencies.Image RecognitionWe all use this in day to day life in sorting our pictures on our Google drive or Photos. The main technique that is used here is classifying the pictures based on the intensity (in case of black and white pictures) and measurement of intensities of red, blue and green for coloured images.HealthcareVarious diagnoses are increasingly made using machine learning these days. Here, various clinical parameters are input to the machine which makes a prognosis  and then predicts the disease status and other health parameters of the person under study.Financial ServicesMachine learning helps in predicting chances of financial fraud, customer’s credit habits, spending patterns etc. The financial and banking sector is also doing market analysis using machine learning techniques.Machine Learning – MethodsMachine learning is all about machines learning through the inputs provided. This learning is carried out in the following ways:Supervised LearningAs the name says, the machine learns under supervision. Let’s see how this is done:The entire process of learning takes place in the presence or supervision of a teacher.This mode of learning contains basic steps as follows:First, the machine is trained using a predefined data also called ‘labeled’ data.Then, the correct answer is fed into the computer which allows it to understand what the right and wrong answers should be.Lastly, the system is given a new set of data or unlabelled data, which it would now analyse using techniques such as classification and regression to predict the correct outcome for the current unlabelled data.Example:Consider a shape sorting game that kids play. A bunch of different shapes of wooden pieces are given to kids, say of square shape, triangular shape, circular shape and star shape. Assume that all blocks of a similar shape are of a unique colour. First, you teach the kids which shape is what  and then you ask them to do the sorting on their own.Similarly, in machine learning, you teach the machine through labelled data. Then, the machine is given some unknown data, which it analyses based on the previous labelled data and gives the correct outcome.In this case, if you observe, two techniques have been used.Classification: Based on colors.Regression: Based on shapes.As a further explanation,Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”.Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.Unsupervised LearningIn this type of learning, there is no previous knowledge, no previous training, nor a teacher to supervise. This learning is all instantaneous based on the data that is available at that given time.Example:Consider a kid playing with a mix of tomatoes and capsicums. They would sort them involuntarily based on their shape or color. This is an instantaneous reaction without any predefined set of attributes or training.A machine working on unsupervised learning would produce the results based on a similar mechanism. For this purpose, it uses two algorithms as explained below:Clustering: This involves grouping a cluster of data. For example, this is used in analysing the online customer’s purchase patterns and shopping habits.Association: This involves associating the given items based on the portion of their sizes. For example, analysing that people who bought large number of a given item would also prefer other similar items. Semi-supervised LearningThe name itself says the pattern of this algorithm.It is a hybrid mix of both supervised and unsupervised learning and uses both labelled data and unlabelled data to predict the results.In most occurrences, unlabelled data is given more in quantity than labelled data, because of cost considerations.For example, in a folder of thousands of photographs, the machine sorts pictures based on the maximum number of common features (unsupervised) and already defined names of persons in the pictures, if any(supervised)Reinforcement LearningIn reinforcement learning, there is no correct answer known to the system. The system learns from its own experience through a reinforcement agent. Since the answer is not known, the reinforcement agent decides what to do with the given task and for this it uses its experience from the current situation only.Example: In a robotic game that involves earning the hidden treasure, the algorithm focuses on bringing out the best outcome through trial and error method. Mainly three components are observed in this type of learning: the user, the environment and the action the user is performing. The algorithm adjusts itself accordingly to guide the user towards the best result that can be achieved.The diagram shown below summarizes the four types of learning we have learnt so far:Machine Learning – AlgorithmsMachine learning is rich in algorithms that allow programmers to pick one that best suits the context. Some of the machine learning algorithms are:Neural networksDecision treesRandom forestsSupport vector machinesNearest-neighbor mappingk-means clusteringSelf-organizing mapsExpectation maximizationBayesian networksKernel density estimationPrincipal component analysisSingular value decompositionMachine Learning Tools and LibrariesTo start the journey with machine learning, a learner should have knowledge of tools and libraries that are quintessential to designing machine learning code. Here is a list of such tools and libraries:ToolsProgramming LanguageMachine learning can be coded either using R programming language or Python. Of late, Python has become more popular due to its rich libraries, ease of learning and coding friendliness.IDEMachine learning is widely coded in Jupyter Notebook. It simplifies writing of Python code and embedding plots and charts. Google Colab is another free tool that you can choose for the same purpose.LibrariesScikit-LearnA very popular and beginner friendly library.Supports most of the standard algorithms from supervised and unsupervised learning.Offers models for data pre-processing and result analysis.Limited support for deep learning.TensorFlowSupports Neural networks and deep learning.Bulky compared to scikit learnOffers best computational efficiencySupports many classical algorithms of machine learning.PandasThe data gathering and preparation part of machine learning that we have seen in the stages involved in machine learning is taken care of by Pandas. This library:Gathers and prepares data that other libraries of machine learning can use at a later point in time.Gathers data from any type of data source such as text, SQL DB, MS Excel or JSON files.Contains many statistical functionalities that can be used to work on the data that’s gathered.NumPy and SciPyNumPy supports all array based and linear algebraic functions needed while working on data, while SciPy offers many scientific calculations. NumPy is more widely used in many real time applications of machine learning as compared to SciPy.MatplotlibThis is a machine learning library that has an extensive collection of plots and charts. This library is a collection of many other packages. Of them, Seaborn is the most popular and is widely used to work on data structures.PyTorch and KerasThese are known for their usage in Deep learning.PyTorch library is extensively used for Deep Learning. It is known for its amazingly speedy calculations and is very popular among deep learning programmers.Keras uses other libraries such as Tensor flow and is apt for developing neural networks.Machine Learning – ProcessesBesides algorithms, machine learning offers many tools and processes to pair best with big data. Various such processes and tools that are at hand for developers are:Data quality and managementGUIs that ease models and process flowsData exploration in an interactive modeVisualized outputs for modelsChoosing the best learning model by comparisonModel evaluation done automatically that identifies the best performersUser friendly model deployment and data-to-decision processMachine Learning Use CasesHere is a list of five use cases that are based on machine learning:PayPal: The online money transfers giant uses machine learning for detecting any suspicious activities related to financial transactions.Amazon: The company’s Alexa, the digital assistant, is the best example of speech processing application of machine learning. The online retailing giant is also using machine learning to display recommendation to its customers.Facebook: The social media company is using machine learning extensively to filter out spam posts and forwards, and to shred out poor quality content.IBM: The company’s self-driven vehicle uses machine learning in taking a decision whether to give the driving control to a human or computer.Kaspersky: The anti-virus manufacturing company is using machine learning to detect security breaches, or unknown malware threats and also for high quality endpoint security for businesses.Which Industries Use Machine Learning?As we have seen just now, machine learning is being adopted in many industries for the potential advantages it offers. Machine learning can be applied to any industry that deals with huge volumes of data, and which has many challenges to be answered. For instance, machine learning has been found to be extremely useful to organizations in the following domains which are  making the best use of the technology:PharmaceuticalsPharma industry spends billions of dollars on drug design and testing every year across the globe. Machine learning helps in cutting down such costs and to obtain results with accuracy just by entering the entire data of the drugs and their chemical compounds and comparing with various other parameters.Banks and Financial ServicesThis industry has two major needs to be addressed: attracting investor attention and increasing investments, and staying alert and preventing financial frauds and cyber threats. Machine learning does these two major tasks with ease and accuracy.Health Care and TreatmentsBy predicting the possible  diseases that could affect a patient, based on the medical, genetic and lifestyle data, machine learning helps patients stay alert to probable health threats that they may encounter. Wearable smart devices are an example of the machine learning applications in health care.Online SalesCompanies study the patterns that online shoppers are adopting through machine learning and use the results to display related ads, offers and discounts. Personalisation of internet shopping experience, merchandise supply panning and marketing campaigns are all based on the outcomes of machine learning results themselves.Mining, Oil and GasMachine learning helps in predicting accurately the best location of availability of minerals, gas, oil and other such natural resources, which would otherwise need huge investments, manpower and time.Government SchemesMany governments are taking the help of machine learning to study the interests and needs of their people. They are accordingly using the results in plans and schemes, both for the betterment of people and optimum usage of financial resources.Space Exploration and Science StudiesMachine learning greatly helps in studying stars, planets and finding out the secrets of other celestial bodies with far lesser investments and manpower. Scientists are also maximising the use of machine learning to discover various fascinating facts about the earth and its components.Future of Machine LearningCurrently, machine learning is entering our lives with baby steps. By the next decade, radical changes can be expected in machine learning and the way it impacts our lives. Customers have already started trusting the power and comfort of machine learning, and would definitely welcome more such innovations in the near future.Gartner says:Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing, or application.So, it would not be surprising if in the future, machine learning would:Make its entry in almost every aspect of human  lifeBe omnipresent in business and industries, irrespective of their sizeEnter  cloud based servicesBring drastic changes in CPU design keeping in mind the need for computational efficiencyAltogether change the shape of data, its processing and usageChange the way connected systems work and look  owing to the ever increasing data on the internet.ConclusionMachine learning is quite different in its own way. While many experts are raising concerns over the ever increasing dependence and presence of machine learning in our everyday lives, on the positive side, machine learning can work wonders. And the world is already witnessing its magic – in health care, finance industry, automotive industry, image processing and voice recognition and many other fields.While many of us worry that machines may take over the world, it is totally up to us, how we design effective, yet safe and controllable machines. There is no doubt that machine learning would change the way we do many things including education, business and health services making the world a safer and better place.
Rated 4.5/5 based on 3 customer reviews

What is Machine Learning and Why It Matters: Everything You Need to Know

9122
  • by Animikh Aich
  • 26th Apr, 2019
  • Last updated on 12th Sep, 2019
  • 15 mins read
What is Machine Learning and Why It Matters: Everything You Need to Know

If you are a machine learning enthusiast and stay in touch with the latest developments, you would have definitely come across the news “Machine learning identifies links between the world's oceans”. Wait, we all know how complex it would be to analyse a concept such as oceans and their behaviour which would undoubtedly involve billions of data points associated with many critical parameters such as wind velocities, temperatures, earth’s rotation and many such. Doesn’t this piece of information gives you a glimpse of the wondrous possibilities of machine learning and its potential uses? And this is just a drop in the ocean!

As you move across this post, you would get a comprehensive idea of various aspects that you ought to know about machine learning.

What is Machine Learning and Why It Matters?

Machine learning is a segment of artificial intelligence. It is designed to make computers learn by themselves and perform operations without human intervention, when they are exposed to new data. It means a computer or a system designed with machine learning will identify, analyse and change accordingly and give the expected output when it comes across a new pattern of data, without any need of humans.

The power behind machine learning’s self-identification and analysis of new patterns, lies in the complex and powerful ‘pattern recognition’ algorithms that guide them in where to look for what. Thus, the demand for machine learning programmers who have extensive knowledge on working with complex mathematical calculations and applying them to big data and AI is growing year after year.

What is ML and Why It Matters

Machine learning, though a buzz word only since recent times, has conceptually been in existence since World War II when Alan Turing’s Bombe, an enigma deciphering machine was introduced to the world. However, it's only in the past decade or so that there has been such great progress made in context to machine learning and its uses, driven mainly by our quest for making this world more futuristic  with lesser human intervention and more precision. Pharma, education technology, industries, science and space, digital inventions, maps and navigation, robotics – you name the domain and you will have instances of machine learning innovations made in it.

The Timeline of Machine Learning and the Evolution of Machines

Voice activated home appliances, self-driven cars and online marketing campaigns are some of the applications of machine learning that we experience and enjoy the benefit of in our day to day life. However, the development of such amazing inventions date back to decades. Many great mathematicians and futuristic thinkers were involved in the foundation and development of machine learning.

A glimpse of the timeline of machine learning reveals many hidden facts and the efforts of great mathematicians and scientists to whom we should attribute all the fruits that we are enjoying today.

Timeline of Machine Learning and Evolution of Machines

  • 1812- 1913: The century that laid the foundation of machine learning

This age laid the mathematical foundation for the development of machine learning. Bayes’ theorem and Markovs Chains took birth during this period.

  • Late 1940s: First computers 

Computers were recognised as machines that can ‘store data’. The famous Manchester Small-Scale Experimental Machine (nicknamed 'The Manchester Baby') belongs to this era.

  • 1950: The official Birth of Machine Learning

Despite many researches and theoretical studies done prior to this year, it was the year 1950 that is always remembered as the foundation of the machine learning that we are witnessing today. Alan Turing, researcher, mathematician, computer genius and thinker, submitted a paper where he mentioned something called ‘imitation game’ and astonished the world by questioning “Can Machines Think?”. His research grabbed the attention of the BBC which took an exclusive interview with Alan.

  • 1951: The First neural network

The first artificial neural network was built by Marvin Minsky and Dean Edmonds this year. Today, we all know that artificial neural networks play a key role in the thinking process of computers and machines. This should be attributed to the invention made by these two scientists.

  • 1974: Coining of the term ‘Machine Learning’

Though there were no specific terms till then for the things that machines did by thinking on their own, it was in 1974 that the term ‘machine learning’ was termed. Other words such as artificial intelligence, informatics and computational intelligence were also proposed the same year.

  • 1996: Machine beats man in a game of chess

IBM developed its own computer called Deep Blue, that can think. This machine beat the world famous champion in chess, Garry Kasparov. It was then proved to the world that machines can really think like humans.

  • 2006-2017: Backpropagation, external memory access and AlphaGo

Back propagation is an important technique that machines use for image recognition. This technique was developed in this period of time.

Besides in 2014, a neural network developed by DeepMind, a British based company, developed a neural network that can access external memory and get things done.

In 2016, AlphaGo was designed by DeepMind researchers. It beat the world famous Go players Lee Sedol and Ke Jie and proved that machines have come a long way.

  • What’s next?

Scientists are talking about ‘singularity’ –a phenomenon that would occur if humans develop a humanoid robot that could think better than humans and will recreate itself. So far, we have been witnessing how AI is entering our personal lives too in the form of voice activated devices, smart systems and many more. The results of this singularity – we shall have to wait and watch!

Basics of Machine Learning

To put it simply, machine learning involves learning by machines. It means computers learn and there are many concepts, methods, algorithms and processes involved in making this happen. Let us try to understand some of the more important machine learning terms.

Three concepts – artificial intelligence, machine learning and deep learning – are often thought to be synonymous. Though they belong to the same family, conceptually they are different.

Basics of Machine Learning

Machine Learning

It implies that machines can ‘learn on their own’ and give the output without any need of programming explicitly.

Artificial Intelligence

This term means machines can ‘think on their own’ just like humans and take decisions.

Deep Learning

This involves creation of artificial neural networks which can think and act based on algorithms.

How do machines learn?

Quite simply, machines learn just like humans do. Humans learn from their training, experiences and through teachers. Sometimes they use knowledge that is fed into their brains, or sometimes take decisions by analysing the current situation using their past experiences.

Similarly, machines learn from the inputs given to them which tell them which is right and which is wrong. Then they are given data that they would have to analyse based on the training they have received so far. In some other cases, they do not have any idea of which is right or wrong, but just take the decision based on their own experiences. We will analyse the various concepts of learning and the methods involved.

How Machine Learning Works?

The process of machine learning occurs in five steps as shown in the following diagram.

How Machine Learning Works

The steps are explained in simple words below:

  • Gathering the data includes data collection from varied, rich and dense content of various formats and types. In real time, this includes feeding the data from different sources such as text files, word documents or excel sheets.
  • Data preparation involves extracting the actual data out of the entire content fed. Only the data that really makes sense to the machine is used for processing. This step also involves checking for missing data, unwanted data and treatment of outliers.
  • Training involves using an appropriate algorithm and modelling the data. The data filtered in the second step is split into two parts and a part of it is used as training data and the second part is used as reference data. The training data is used to create the model.
  • Evaluating the model includes testing its accuracy. To verify its accuracy to the fullest, the model so developed is tested on such data which is not present in the data during the second step.
  • Finally, the performance of the machine is improved by choosing a different model that suits the different type of data that is present altogether. This is the step where the machine thinks and rethinks in selecting the model best suited for various types of data.

Examples of Machine Learning

The below examples will help you understand where machine learning is used in real time:

Machine Learning Examples

Speech Recognition

Voice based searching and call rerouting are the best examples for speech recognition using machine learning. The principle lies in translating  spoken words into text and then segmenting them on the basis of their frequencies.

Image Recognition

We all use this in day to day life in sorting our pictures on our Google drive or Photos. The main technique that is used here is classifying the pictures based on the intensity (in case of black and white pictures) and measurement of intensities of red, blue and green for coloured images.

Healthcare

Various diagnoses are increasingly made using machine learning these days. Here, various clinical parameters are input to the machine which makes a prognosis  and then predicts the disease status and other health parameters of the person under study.

Financial Services

Machine learning helps in predicting chances of financial fraud, customer’s credit habits, spending patterns etc. The financial and banking sector is also doing market analysis using machine learning techniques.

Machine Learning – Methods

Machine learning is all about machines learning through the inputs provided. This learning is carried out in the following ways:

Supervised Learning

As the name says, the machine learns under supervision. Let’s see how this is done:

  • The entire process of learning takes place in the presence or supervision of a teacher.
  • This mode of learning contains basic steps as follows:
    • First, the machine is trained using a predefined data also called ‘labeled’ data.
    • Then, the correct answer is fed into the computer which allows it to understand what the right and wrong answers should be.
  • Lastly, the system is given a new set of data or unlabelled data, which it would now analyse using techniques such as classification and regression to predict the correct outcome for the current unlabelled data.

Example:

Consider a shape sorting game that kids play. A bunch of different shapes of wooden pieces are given to kids, say of square shape, triangular shape, circular shape and star shape. Assume that all blocks of a similar shape are of a unique colour. First, you teach the kids which shape is what  and then you ask them to do the sorting on their own.

Similarly, in machine learning, you teach the machine through labelled data. Then, the machine is given some unknown data, which it analyses based on the previous labelled data and gives the correct outcome.

In this case, if you observe, two techniques have been used.

  • Classification: Based on colors.
  • Regression: Based on shapes.

As a further explanation,

  • Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”.
  • Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

Unsupervised Learning

  • In this type of learning, there is no previous knowledge, no previous training, nor a teacher to supervise. This learning is all instantaneous based on the data that is available at that given time.

Example:

Consider a kid playing with a mix of tomatoes and capsicums. They would sort them involuntarily based on their shape or color. This is an instantaneous reaction without any predefined set of attributes or training.

A machine working on unsupervised learning would produce the results based on a similar mechanism. For this purpose, it uses two algorithms as explained below:

  • Clustering: This involves grouping a cluster of data. For example, this is used in analysing the online customer’s purchase patterns and shopping habits.
  • Association: This involves associating the given items based on the portion of their sizes. For example, analysing that people who bought large number of a given item would also prefer other similar items. 

Semi-supervised Learning

The name itself says the pattern of this algorithm.

  • It is a hybrid mix of both supervised and unsupervised learning and uses both labelled data and unlabelled data to predict the results.
  • In most occurrences, unlabelled data is given more in quantity than labelled data, because of cost considerations.
  • For example, in a folder of thousands of photographs, the machine sorts pictures based on the maximum number of common features (unsupervised) and already defined names of persons in the pictures, if any(supervised)

Reinforcement Learning

In reinforcement learning, there is no correct answer known to the system. The system learns from its own experience through a reinforcement agent. Since the answer is not known, the reinforcement agent decides what to do with the given task and for this it uses its experience from the current situation only.

Example: In a robotic game that involves earning the hidden treasure, the algorithm focuses on bringing out the best outcome through trial and error method. Mainly three components are observed in this type of learning: the user, the environment and the action the user is performing. The algorithm adjusts itself accordingly to guide the user towards the best result that can be achieved.

The diagram shown below summarizes the four types of learning we have learnt so far:

Types of Machine Learning:- Supervised, Unsupervised, Semi-supervised and Reinforced Learning.

Machine Learning – Algorithms

Machine learning is rich in algorithms that allow programmers to pick one that best suits the context. Some of the machine learning algorithms are:

  • Neural networks
  • Decision trees
  • Random forests
  • Support vector machines
  • Nearest-neighbor mapping
  • k-means clustering
  • Self-organizing maps
  • Expectation maximization
  • Bayesian networks
  • Kernel density estimation
  • Principal component analysis
  • Singular value decomposition

Machine Learning Tools and Libraries

To start the journey with machine learning, a learner should have knowledge of tools and libraries that are quintessential to designing machine learning code. Here is a list of such tools and libraries:

Tools

Programming Language

Machine learning can be coded either using R programming language or Python. Of late, Python has become more popular due to its rich libraries, ease of learning and coding friendliness.

IDE

Machine learning is widely coded in Jupyter Notebook. It simplifies writing of Python code and embedding plots and charts. Google Colab is another free tool that you can choose for the same purpose.

Libraries

Scikit-Learn

  • A very popular and beginner friendly library.
  • Supports most of the standard algorithms from supervised and unsupervised learning.
  • Offers models for data pre-processing and result analysis.
  • Limited support for deep learning.

TensorFlow

  • Supports Neural networks and deep learning.
  • Bulky compared to scikit learn
  • Offers best computational efficiency
  • Supports many classical algorithms of machine learning.

Pandas

The data gathering and preparation part of machine learning that we have seen in the stages involved in machine learning is taken care of by Pandas. This library:

  • Gathers and prepares data that other libraries of machine learning can use at a later point in time.
  • Gathers data from any type of data source such as text, SQL DB, MS Excel or JSON files.
  • Contains many statistical functionalities that can be used to work on the data that’s gathered.

NumPy and SciPy

NumPy supports all array based and linear algebraic functions needed while working on data, while SciPy offers many scientific calculations. NumPy is more widely used in many real time applications of machine learning as compared to SciPy.

Matplotlib

This is a machine learning library that has an extensive collection of plots and charts. This library is a collection of many other packages. Of them, Seaborn is the most popular and is widely used to work on data structures.

PyTorch and Keras

These are known for their usage in Deep learning.

  • PyTorch library is extensively used for Deep Learning. It is known for its amazingly speedy calculations and is very popular among deep learning programmers.
  • Keras uses other libraries such as Tensor flow and is apt for developing neural networks.

Tools and Libraries of Machine Learning

Machine Learning – Processes

Besides algorithms, machine learning offers many tools and processes to pair best with big data. Various such processes and tools that are at hand for developers are:

  • Data quality and management
  • GUIs that ease models and process flows
  • Data exploration in an interactive mode
  • Visualized outputs for models
  • Choosing the best learning model by comparison
  • Model evaluation done automatically that identifies the best performers
  • User friendly model deployment and data-to-decision process

Machine Learning Use Cases

Here is a list of five use cases that are based on machine learning:

  • PayPal: The online money transfers giant uses machine learning for detecting any suspicious activities related to financial transactions.
  • Amazon: The company’s Alexa, the digital assistant, is the best example of speech processing application of machine learning. The online retailing giant is also using machine learning to display recommendation to its customers.
  • Facebook: The social media company is using machine learning extensively to filter out spam posts and forwards, and to shred out poor quality content.
  • IBM: The company’s self-driven vehicle uses machine learning in taking a decision whether to give the driving control to a human or computer.
  • Kaspersky: The anti-virus manufacturing company is using machine learning to detect security breaches, or unknown malware threats and also for high quality endpoint security for businesses.

Which Industries Use Machine Learning?

As we have seen just now, machine learning is being adopted in many industries for the potential advantages it offers. Machine learning can be applied to any industry that deals with huge volumes of data, and which has many challenges to be answered. For instance, machine learning has been found to be extremely useful to organizations in the following domains which are  making the best use of the technology:

Pharmaceuticals

Pharma industry spends billions of dollars on drug design and testing every year across the globe. Machine learning helps in cutting down such costs and to obtain results with accuracy just by entering the entire data of the drugs and their chemical compounds and comparing with various other parameters.

Banks and Financial Services

This industry has two major needs to be addressed: attracting investor attention and increasing investments, and staying alert and preventing financial frauds and cyber threats. Machine learning does these two major tasks with ease and accuracy.

Health Care and Treatments

By predicting the possible  diseases that could affect a patient, based on the medical, genetic and lifestyle data, machine learning helps patients stay alert to probable health threats that they may encounter. Wearable smart devices are an example of the machine learning applications in health care.

Online Sales

Companies study the patterns that online shoppers are adopting through machine learning and use the results to display related ads, offers and discounts. Personalisation of internet shopping experience, merchandise supply panning and marketing campaigns are all based on the outcomes of machine learning results themselves.

Mining, Oil and Gas

Machine learning helps in predicting accurately the best location of availability of minerals, gas, oil and other such natural resources, which would otherwise need huge investments, manpower and time.

Government Schemes

Many governments are taking the help of machine learning to study the interests and needs of their people. They are accordingly using the results in plans and schemes, both for the betterment of people and optimum usage of financial resources.

Space Exploration and Science Studies

Machine learning greatly helps in studying stars, planets and finding out the secrets of other celestial bodies with far lesser investments and manpower. Scientists are also maximising the use of machine learning to discover various fascinating facts about the earth and its components.

Future of Machine Learning

Future of Machine Learning

Currently, machine learning is entering our lives with baby steps. By the next decade, radical changes can be expected in machine learning and the way it impacts our lives. Customers have already started trusting the power and comfort of machine learning, and would definitely welcome more such innovations in the near future.

Gartner says:

Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing, or application.

So, it would not be surprising if in the future, machine learning would:

  • Make its entry in almost every aspect of human  life
  • Be omnipresent in business and industries, irrespective of their size
  • Enter  cloud based services
  • Bring drastic changes in CPU design keeping in mind the need for computational efficiency
  • Altogether change the shape of data, its processing and usage
  • Change the way connected systems work and look  owing to the ever increasing data on the internet.

Conclusion

Machine Learning can work wonders

Machine learning is quite different in its own way. While many experts are raising concerns over the ever increasing dependence and presence of machine learning in our everyday lives, on the positive side, machine learning can work wonders. And the world is already witnessing its magic – in health care, finance industry, automotive industry, image processing and voice recognition and many other fields.

While many of us worry that machines may take over the world, it is totally up to us, how we design effective, yet safe and controllable machines. There is no doubt that machine learning would change the way we do many things including education, business and health services making the world a safer and better place.

Animikh

Animikh Aich

Computer Vision Engineer

Animikh Aich is a Deep Learning enthusiast, currently working as a Computer Vision Engineer. His work includes three International Conference publications and several projects based on Computer Vision and Machine Learning.

Join the Discussion

Your email address will not be published. Required fields are marked *

3 comments

vintage House restaurant 09 May 2019

Greetings! Very helpful advice within this article! It's the little changes that produce the greatest changes. Thanks a lot for sharing!

Aditya 21 Jun 2019

Excellent web site difficult to find high quality writing like yours nowadays,I honestly appreciate people like you! Take care

amith singh 06 Aug 2019

Hi, I read the complete blog and got full details of machine learning. It has been presented in such a way that anyone from a development background can understand easily. Thank you for the wonderful blog. Thank you Knowledgehut!

Suggested Blogs

What Is Data Science(with Examples), It's Lifecycle and Who exactly is a Data Scientist

Oh yes, Science is everywhere. A while ago, when children embarked on the journey of learning everyday science in school, the statement that always had a mention was “Science is everywhere”. The situation is more or less the same even in present times. Science has now added a few feathers to its cap. Yes, the general masses sing the mantra “Data Science” is everywhere. What does it mean when I say Data Science is everywhere? Let us take a look at the Science of Data. What are those aspects that make this Science unique from everyday Science?The Big Data Age as you may call it has in it Data as the object of study.Data Science for a person who has set up a firm could be a money spinnerData Science for an architect working at an IT consulting company could be a bread earnerData Science could be the knack behind the answers that come out from the juggler’s hatData Science could be a machine imported from the future, which deals with the Math and Statistics involved in your lifeData science is a platter full of data inference, algorithm development, and technology. This helps the users find recipes to solve analytically complex problems.With data as the core, we have raw information that streams in and is stored in enterprise data warehouses acting as the condiments to your complex problems. To extract the best from the data generated, Data Science calls upon Data Mining. At the end of the tunnel, Data Science is about unleashing different ways to use data and generate value for various organizations.Let us dig deeper into the tunnel and see how various domains make use of Data Science.Example 1Think of a day without Data Science, Google would not have generated results the way it does today.Example 2Suppose you manage an eatery that churns out the best for different taste buds. To model a product in the pipeline, you are keen on knowing what the requirements of your customers are. Now, you know they like more cheese on the pizza than jalapeno toppings. That is the existing data that you have along with their browsing history, purchase history, age and income. Now, add more variety to this existing data. With the vast amount of data that is generated, your strategies to bank upon the customers’ requirements can be more effective. One customer will recommend your product to another outside the circle; this will further bring more business to the organization.Consider this image to understand how an analysis of the customers’ requirements helps:Example 3Data Science plays its role in predictive analytics too.I have an organization that is into building devices that will send a trigger if a natural calamity is soon to occur. Data from ships, aircraft, and satellites can be accumulated and analyzed to build models that will not only help with weather forecasting but also predict the occurrence of natural calamities. The model device that I build will send triggers and save lives too.Consider the image shown below to understand how predictive analytics works:Example 4A lot many of us who are active on social media would have come across this situation while posting images that show you indulging in all fun and frolic with your friends. You might miss tagging your friends in the images you post but the tag suggestion feature available on most platforms will remind you of the tagging that is pending.The automatic tag suggestion feature uses the face recognition algorithm.Lifecycle of Data ScienceCapsulizing the main phases of the Data Science Lifecycle will help us understand how the Data Science process works. The various phases in the Data Science Lifecycle are:DiscoveryData PreparationModel PlanningModel BuildingOperationalizingCommunicating ResultsPhase 1Discovery marks the first phase of the lifecycle. When you set sail with your new endeavor,it is important to catch hold of the various requirements and priorities. The ideation involved in this phase needs to have all the specifications along with an outline of the required budget. You need to have an inquisitive mind to make the assessments – in terms of resources, if you have the required manpower, technology, infrastructure and above all time to support your project. In this phase, you need to have a business problem laid out and build an initial hypotheses (IH) to test your plan. Phase 2Data preparation is done in this phase. An analytical sandbox is used in this to perform analytics for the entire duration of the project. While you explore, preprocess and condition data, modeling follows suit. To get the data into the sandbox, you will perform ETLT (extract, transform, load and transform).We make use of R for data cleaning, transformation, and visualization and further spot the outliers and establish a relationship between the variables. Once the data is prepared after cleaning, you can play your cards with exploratory analytics.Phase 3In this phase of Model planning, you determine the methods and techniques to pick on the relationships between variables. These relationships set the base for the algorithms that will be implemented in the next phase.  Exploratory Data Analytics (EDA) is applied in this phase using various statistical formulas and visualization tools.Subsequently, we will look into the various models that are required to work out with the Data Science process.RR is the most commonly used tool. The tool comes with a complete set of modeling capabilities. This proves a good environment for building interpretive models.SQL Analysis Services SQL Analysis services has the ability to perform in-database analytics using basic predictive models and common data mining functions.SAS/ACCESS  SAS/ACCESS helps you access data from Hadoop. This can be used for creating repeatable and reusable model flow diagrams.You have now got an overview of the nature of your data and have zeroed in on the algorithms to be used. In the next stage, the algorithm is applied to further build up a model.Phase 4This is the Model building phase as you may call it. Here, you will develop datasets for training and testing purposes. You need to understand whether your existing tools will suffice for running the models that you build or if a more robust environment (like fast and parallel processing) is required. The various tools for model building are SAS Enterprise Miner, WEKA, SPCS Modeler, Matlab, Alpine Miner and Statistica.Phase 5In the Operationalize phase, you deliver final reports, briefings, code and technical documents. Moreover, a pilot project may also be implemented in a real-time production environment on a small scale. This helps users get a clear picture of the performance and other related constraints before full deployment.Phase 6The Communicate results phase is the conclusion. Here, we evaluate if you have been able to meet your goal the way you had planned in the initial phase. It is in this phase that the key findings pop their heads out. You communicate to the stakeholders in this phase. This phase brings you the result of your project whether it is a success or a failure.Why Do We Need Data Science?Data Science to be precise is an amalgamation of Infrastructure, Software, Statistics and the various data sources.To really understand big data, it would help us if we bridge back to the historical background. Gartner’s definition circa 2001, which is still the go-to definition says,Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs.When we break the definition into simple terms, all that it means is, big data is humongous. This involves the multiplication of complex data sets with the addition of new data sources. When the data sets are in such high volumes, our traditional data processing software fails to manage them. It is just like how you cannot expect your humble typewriter to do the job of a computer. You cannot expect a typewriter to even do the ctrl c + ctrl v job for you. The amount of data that comes with the solutions to all your business problems is massive. To help you with the processing of this data, you have Data Science playing the key role.The concept of big data itself may sound relatively new; however, the origins of large data sets can be traced back to the 1960s and the '70s. This is when the world of data was just getting started. The world witnessed the set up of the first data centers and the development of the relational database.Around 2005, Facebook, YouTube, and other online services started gaining immense popularity. The more people indulged in the use of these platforms, the more data they generated. The processing of this data involved a lot of Data Science. The masses had to store the amassed data and analyse it at a later point. As a platform that answers to the storage and analysis of the amassed data, Hadoop was developed. Hadoop is an open-source framework that helps in the storage and analysis of big data sets. And as we say, the rest will follow suit; we had NoSQL gaining popularity during this time.With the advent of big data, the need for its storage also grew. The storage of data became a major issue for enterprise industries until 2010. We have had Hadoop, Spark and other frameworks mitigating the challenge to a very large extent. Though the volume of big data is skyrocketing, the focus remains on the processing of the data, all thanks to these efficient frameworks. And, Data Science once again hogs the limelight.Can we say it is only the users leading to huge amounts of data? No, we cannot. It is not only humans generating the data but also the work they indulge in.Delving into the iota of the Internet of Things (IoT) will get us some clarity on the question that we just raised. As we have more objects and devices connected to the Internet, data gathers not just by use but also by the pattern of your usage and the performance of the various products.The Three Vs of Big DataData Science helps in the extraction of knowledge from the accumulated data. While big data has come far with the accumulation of users’ data, its usefulness is only just beginning.Following are the Three Properties that define Big Data:VolumeVelocityVarietyVolumeThe amount of data is a crucial factor here. Big data stands as a pillar when you have to process a multitude of low-density, unstructured data. The data may contain unknown value – such as clickstreams on a webpage or a mobile app and Twitter data feeds. The values of the data may differ from user to user. For some, the value might be in tens of terabytes of data. For others, the value might be in hundreds of petabytes.Consider the different social media platforms – Facebook records 2 billion users, YouTube has 1 billion users, 350 million users for Twitter and a whopping 700 million users on Instagram. There is exchange of billions of images, posts and tweets on these platforms. Imagine the amuck storage of data the users contribute too. Mind Boggling, is it not? This insanely large amount of data is generated every minute and every hour.VelocityThe fast rate at which the data is received and acted upon is the Velocity. Usually, the data is written to the disk. When there is data with highest velocity, it streams directly into the memory. With the advancement in technology, we now have more numbers of Internet-connected devices across industries. The velocity of the data generated through these devices that act real time or near real time may call for real-time evaluation and action.Sticking to our social media example, Facebook accounts for 900 million photo uploads, Twitter handles 500 million tweets, Google is to go to solution for 3.5 billion searches, YouTube calls for 0.4 millions hours of video uploads; all this on a daily basis. The bundled amount of data is stifling.VarietyThe data generated by the users comes in different types. The different types form different varieties of data. Dating back, we had traditional data types that were structured and organized in a relational database.Texts, tweets, videos, photos uploaded form the different varieties of structured data uploaded on the Internet.Voicemails, emails, ECG reading, audio recordings and a lot more form the different varieties of unstructured data that we find on the Internet.Who is a Data Scientist? A curious brain and an impressive training is all that you need to become a Data Scientist. Not as easy as it may sound.Deep thinking, deep learning with intense intellectual curiosity is a common trait found in data scientists. The more you ask questions, the more discoveries you come up with, the more augmented your learning experience is, the more it gets easier for you to tread on the path of Data Science.A factor that differentiates a data scientist from a normal bread earner is that they are more obsessed with creativity and ingenuity. A normal bread earner will go seeking money whereas, the motivator for a data scientist is the ability to solve analytical problems with a pinch of curiosity and creativity. Data scientists are always on a treasure hunt – hunting for the best from the trove.If you think, you need a degree in Sciences or you need to be a PhD in Math to become a legitimate data scientist, mind you, you are carrying a misconception. A natural propensity in these areas will definitely add to your profile but you can be an expert data scientist without a degree in these areas too. Data Science becomes a cinch with heaps of knowledge in programming and business acumen.Data Science is a discipline gaining colossal prominence of late. Educational institutions are yet to come up with comprehensive Data Science degree programs. A data scientist can never claim to have undergone all the required schooling. Learning the rights skills, guided by self-determination is a never-ending process for a data scientist.As Data Science is multidisciplinary, many people find it confusing to differentiate between Data Scientist and Data Analyst.Data Analytics is one of the components of Data Science. Analytics help in understanding the data structure of an organization. The achieved output is further used to solve problems and ring in business insights.The Basic Differences between a Data Scientist and a Data AnalystScientists and Analysts are not exactly synonymous. The roles are not mutually exclusive either. The roles of Data Scientists and Data Analysts differ a lot. Let us take a look at some of the basic differences:CriteriaData ScientistData AnalystGoalInquisitive nature and a strong business acumen helps Data Scientists to arrive at solutionsThey perform data analysis and sourcingTasksData Scientists need to be adept at data insight mining, preparation, and analysis to extract informationData Analysts gather, arrange, process and model both structured and unstructured dataSubstantive expertiseRequiredNot RequiredNon-technical skillsRequiredNot RequiredWhat Skills Are Required To Become a Data Scientist?Data scientists blend with the best skills. The fundamental skills required to become a Data Scientist are as follows:Proficiency in MathematicsTechnology knowhow and the knack to hackBusiness AcumenProficiency in MathematicsA Data Scientist needs to be equipped with a quantitative lens. You can be a Data Scientist if you have the ability to view the data quantitatively.Before a data product is finally built, it calls for a tremendous amount of data insight mining. There are portions of data that include textures, dimensions and correlations. To be able to find solutions to come with an end product, a mathematical perspective always helps.If you have that knack for Math, finding solutions utilizing data becomes a cakewalk laden with heuristics and quantitative techniques. The path to finding solutions to major business problems is a tedious one. It involves the building of analytical models. Data Scientists need to identify the underlying nuts and bolts to successfully build models.Data Science carries with it a misconception that it is all about statistics. Statistics is crucial; however, only the Math type is more accountable. Statistics has two offshoots – the classical and the Bayesian. When people talk about stats, they are usually referring to classical stats. Data Scientists need to refer both types to arrive at solutions. Moreover, there is a mix of inferential techniques and machine learning algorithms; this mix leans on the knowledge of linear algebra. There are popular methods in Data Science; finding a solution using these methods calls upon matrix math which has got very less to do with classical stats.Technology knowhow and the knack to hackOn a lighter note, let us put a disclaimer… you are not being asked to learn hacking to come crashing on computers. As a hacker, you need to be gelled with the amalgam of creativity and ingenuity. You are expected to use the right technical skills to build models and thereby find solutions to complex analytical problems.Why does the world of Data Science vouch on your hacking ability? The answer finds its element in the use of technology by Data Scientists. Mindset, training and the right technology when put together can squeeze out the best from mammoth data sets. Solving complex algorithms requires more sophisticated tools than just Excel. Data scientists need to have the nitty-gritty ability to code. They should be able to prototype quick solutions, as well as integrate with complex data systems. SQL, Python, R, and SAS are the core languages associated with Data Science. A knowhow of Java, Scala, Julia, and other languages also helps. However, the knowledge of language fundamentals does not suffice the quest to extract the best from enormous data sets. A hacker needs to be creative to sail through technical waters and make the codes reach the shore.Business AcumenA strong business acumen is a must-have in the portfolio of any Data Scientist. You need to make tactical moves and fetch that from the data, which no one else can. To be able to translate your observation and make it a shared knowledge calls for a lot of responsibility that can face no fallacy.With the right business acumen, a Data Scientist finds it easy to present a story or the narration of a problem or a solution.To be able to put your ideas and the solutions you arrive at across the table, you need to have business acumen along with the prowess for tech and algorithms.Data, Math, and tech will not help always. You need to have a strong business influence that can further be influenced by a strong business acumen.Companies Using Data ScienceTo address the issues associated with the management of complex and expanding work environments, IT organizations make use of data to identify new value sources. The identification helps them exploit future opportunities and to further expand their operations. What makes the difference here is the knowledge you extract from the repository of data. The biggest and the best companies use analytics to efficiently come up with the best business models.Following are a few top companies that use Data Science to expand their services and increase their productivity.GoogleAmazonProcter & GambleNetflixGoogle Google has always topped the list on a hiring spree for top-notch data scientists. A force of data scientists, artificial intelligence and machine learning by far drives Google. Moreover, when you are here, you get the best when you give the best of your data expertise.AmazonAmazon, the global e-commerce and cloud computing giant hire data scientists on a big scale. To bank upon the customers’ mindsets, enhance the geographical outreach of both the cloud domain and e-commerce domain among other business-driven goals, they make use of Data Science. Data Scientists play a crucial role in steering Data Science.Procter & Gamble and NetflixBig Data is a major component of Data Science.It has answers to a range of business problems – from customer experience to analytics.Netflix and Procter & Gamble join the race of product development by using big data to anticipate customer demand. They make use of predictive analytics, an offshoot of Data Science to build models for services in their pipeline. This modelling is an attribute that contributes to their commercial success. The significant addition to the commercial success of P&G is that it uses data and analytics from test markets, social media, and early store rollouts. Following this strategy, it further plans, produces, and launches the final products. And, the finale often garners an overwhelming response for them.The Final Component of the Big Data StoryWhen speed multiplied with storage capabilities, thus evolved the final component of the Big Data story – the generation and collection of the data. If we still had massive room-sized calculators working as computers, we may not have come across the humongous amount of data that we see today. With the advancement in technology, we called upon ubiquitous devices. With the increase in the number of devices, we have more data being generated. We are generating data at our own pace from our own space owing to the devices that we make use of from our comfort zones. Here I tweet, there you post, while a video is being uploaded on some platform by someone from some corner of the room you are seated in.The more you inform people about what you are doing in your life, the more data you end up writing. I am happy and I share a quote on Facebook expressing my feelings; I am contributing to more data. This is how enormous amount of data is generated. The Internet-connected devices that we use support in writing data. Anything that you engage with in this digital world, the websites you browse, the apps you open on your cell phone, all the data pertaining to these can be logged in a database miles away from you.Writing data and storing it is not an arduous task anymore. At times, companies just push the value of the data to the backburner. At some point of time, this data will be fetched and cooked when they see the need for it.There are different ways to cash upon the billions of data points. Data Science puts the data into categories to get a clear picture. On a Final NoteIf you are an organization looking out to expand your horizons, being data-driven will take you miles. The application of an amalgam of Infrastructure, Software and Statistics, and the various data sources is the secret formula to successfully arrive at key business solutions. The future belongs to Data Science. Today, it is data that we see all around us. This new age sounds the bugle for more opportunities in the field of Data Science. Very soon, the world will need around one million Data Scientists.If you are keen on donning the hat of a Data Scientist, be your own architect when it comes to solving analytical problems. You need to be a highly motivated problem solver to overcome the toughest analytical challenges.
Rated 4.5/5 based on 1 customer reviews
9741
What Is Data Science(with Examples), It's Lif...

Oh yes, Science is everywhere. A while ago, when c... Read More

Essential Skills to Become a Data Scientist

The demand for Data Science professionals is now at an all-time high. There are companies in virtually every industry looking to extract the most value from the heaps of information generated on a daily basis.With the trend for Data Science catching up like never before, organizations are making complete use of their internal data assets to further examine the integration of hundreds of third-party data sources. What is crucial here is the role of the data scientists.Not very long back, the teams playing the key role of working on the data always found their places in the back rooms of multifold IT organizations. The teams though sitting on the backseat would help in steering the various corporate systems with the required data that acted as the fuel to keep the activities running. The critical database tasks performed by the teams responsible allowed corporate executives to report on operations activities and deliver financial results.When you take up a career in Data Science, your previous experience or skills do not matter. As a matter of fact, you would need a whole new range of skills to pursue a career in Data Science. Below are the skills required to become a top dog in Data Science.What should Data Scientists knowData scientists are expected to have knowledge and expertise in the following domains:The areas arch over dozens of languages, frameworks, and technologies that data scientists need to learn. Data scientists should always have the curiosity to amass more knowledge in their domain so that they stay relevant in this dynamic field.The world of Data Science demands certain important attributes and skills, according to IT leaders, industry analysts, data scientists, and others.How to become a Data Scientist?A majority of Data scientists already have a Master’s degree. If Master’s degree does not quench their thirst for more degrees, some even go on to acquire PhD degrees. Mind you, there are exceptions too. It isn’t mandatory that you should be an expert in a particular subject to become a Data Scientist. You could become one even with a qualification in Computer Science, Physical Sciences, Natural Sciences, Statistics or even Social Sciences. However, a degree in Mathematics and Statistics is always an added benefit for enhanced understanding of the concepts.Qualifying with a degree is not the end of the requirements. Brush up your skills by taking online lessons in a special skill set of your choice — get certified on how to use Hadoop, Big Data or R. You can also choose to enroll yourself for a Postgraduate degree in the field of Data Science, Mathematics or any other related field.Remember, learning does not end with earning a degree or certification. You need to practice what you learned — blog and share your knowledge, build an app and explore other avenues and applications of data.The Data Scientists of the modern world have a major role to play in businesses across the globe. They have the ability to extract useful insights from vast amounts of raw data using sophisticated techniques. The business acumen of the Data Scientists help a big deal in predicting what lies ahead for enterprises. The models that the Data Scientists create also bring out measures to mitigate potential threats if any.Take up organizational challenges with ABCDE skillsetAs a Data Scientist, you may have to face challenges while working on projects and finding solutions to problems.A = AnalyticsIf you are a Data Scientist, you are expected not just to study the data and identify the right tools and techniques; you need to have your answers ready to all the questions that come across while you are strategizing on working on a solution with or without a business model.B = Business AcumenOrganizations vouch for candidates with strong business acumen. As a Data Scientist, you are expected to showcase your skills in a way that will make the organization stand one step ahead of the competition. Undertaking a project and working on it is not the end of the path scaled by you. You need to understand and be able to make others understand how your business models influence business outcomes and how the outcomes will prove beneficial to the organization.C = CodingAnd a Data Scientist is expected to be adept at coding too. You may encounter technical issues where you need to sit and work on codes. If you know how to code, it will make you further versatile in confidently assisting your team.D = DomainThe world does not expect Data Scientists to be perfect with knowledge of all domains. However, it is always assumed that a Data Scientist has know-how of various industrial operations. Reading helps as a plus point. You can gain knowledge in various domains by reading the resources online.E = ExplainTo be a successful Data Scientist, you should be able to explain the problem you are faced with to figure out a solution to the problem and share it with the relevant stakeholders. You need to create a difference in the way you explain without leaving any communication gaps.The Important Skills for a Data ScientistLet us now understand the important skills to become an expert Data Scientist – all the skills that go in, to become one. The skills are as follows:Critical thinkingCodingMathML, DL, AICommunication1. Critical thinkingData scientists need to keep their brains racing with critical thinking. They should be able to apply the objective analysis of facts when faced with a complex problem. Upon reaching a logical analysis, a data scientist should formulate opinions or render judgments.Data scientists are counted upon for their understanding of complex business problems and the risks involved with decision-making. Before they plunge into the process of analysis and decision-making, data scientists are required to come up with a 'model' or 'abstract' on what is critical to coming up with the solution to a problem. Data scientists should be able to determine the factors that are extraneous and can be ignored while churning out a solution to a complex business problem.According to Jeffry Nimeroff, CIO at Zeta Global, which provides a cloud-based marketing platform – A data scientist needs to have experience but also have the ability to suspend belief...Before arriving at a solution, it is very important for a Data Scientist to be very clear on what is being expected and if the expected solution can be arrived at. It is only with experience that your intuition works stronger. Experience brings in benefits.If you are a novice and a problem is posed in front of you; all that the one who put the problem in front of you would get is a wide-eyed expression, perhaps. Instead, if you have hands-on experience of working with complex problems no matter what, you will step back, look behind at your experience, draw some inference from multiple points of view and try assessing the problem that is put forth.In simple steps, critical thinking involves the following steps:a. Describe the problem posed in front of you.b. Analyse the arguments involved – The IFs and BUTs.c. Evaluate the significance of the decisions being made and the successes or failures thereafter.2. CodingHandling a complex task might at times call for the execution of a chain of programming tasks. So, if you are a data scientist, you should know how to go about writing code. It does not stop at just writing the code; the code should be executable and should be crucial in helping you find a solution to a complex business problem.In the present scenario, Data Scientists are more inclined towards learning and becoming an expert with Python as the language of choice. There is a substantial crowd following R as well. Scala, Clojure, Java and Octave are a few other languages that find prominence too.Consider the following aspects to be a successful Data Scientist that can dab with programming skills –a) You need to deal with humongous volumes of data.b) Working with real-time data should be like a cakewalk for you.c) You need to hop around cloud computing and work your way with statistical models like the ones shown below:Different Statistical ModelsRegressionOptimizationClusteringDecision treesRandom forestsData scientists are expected to understand and have the ability to code in a bundle of languages – Python, C++ or Java.Gaining the knack to code helps Data Scientists; however, this is not the end requirement. A Data Scientist can always be surrounded by people who code.3. MathIf you have never liked Mathematics as a subject or are not proficient in Mathematics, Data Science is probably not the right career choice for you.You might own an organization or you might even be representing it; the fact is while you engage with your clients, you might have to look into many disparate issues. To deal with the issues that lay in front of you, you will be required to develop complex financial or operational models. To finally be able to build a worthy model, you will end up pulling chunks from large volumes of data. This is where Mathematics helps you.If you have the expertise in Mathematics, building statistical models is easier. Statistical models further help in developing or switching over to key business strategies. With skills in both Mathematics and Statistics, you can get moving in the world of Data Science. Spell the mantra of Mathematics and Statistics onto your lamp of Data Science, lo and behold you can be the genie giving way to the best solutions to the most complex problems.4. Machine learning, Deep Learning, AIData Science overlaps with the fields of Machine Learning, Deep Learning and AI.There is an increase in the way we work with computers, we now have enhanced connectivity; a large amount of data is being collected and industries make use of this data and are moving extremely fast.AI and deep learning may not show up in the requirements of job postings; yet, if you have AI and deep learning skills, you end up eating the big pie.A data scientist needs to be hawk-eyed and alert to the changes in the curve while research is in progress to come up with the best methodology to a problem. Coming up with a model might not be the end. A Data Scientist must be clear as to when to apply which practice to solve a problem without making it more complex.Data scientists need to understand the depth of problems before finding solutions. A data scientist need not go elsewhere to study the problems; all that is there in the data fetched is what is needed to bring out the best solution.A data scientist should be aware of the computational costs involved in building an environment and the following system boundary conditions:a. Interpretabilityb. Latencyc. BandwidthStudying a customer can act as a major plus point for both a data scientist and an orgaStudying nization… This helps in understanding what technology to apply.No matter how generations advance with the use of automated tools and open source is readily available, statistical skills are considered the much-needed add-ons for a data scientist.Understanding statistics is not an easy job; a data scientist needs to be competent to comprehend the assumptions made by the various tools and software.Experts have put forth a few important requisites for data scientists to make the best use of their models:Data scientists need to be handy with proper data interpretation techniques and ought to understand –a. the various functional interfaces to the machine learning algorithmsb. the statistics within the methodsIf you are a data scientist, try dabbing your profile with colours of computer science skills. You must be proficient in working with the keyboard and have a sound knowledge of fundamentals in software engineering.5. CommunicationCommunication and technology show a cycle of operations wherein, there is an integration between people, applications, systems, and data. Data science does not stand separate in this. Working with Data Science is no different. As a Data Scientist, you should be able to communicate with various stakeholders. Data plays a key attribute in the wheel of communication.Communication in Data Science ropes in the ‘storytelling’ ability. This helps you translate a solution you have arrived at into action or intervention that you have put in the pipeline. As a Data Scientist, you should be adept at knitting with the data you have extracted and communicated it clearly to your stakeholders.What does a data scientist communicate to the stakeholders?The benefits of dataThe technology and the computational costs involved in the process of extracting and making use of the dataThe challenges posed in the form of data quality, privacy, and confidentialityA Data Scientist also needs to keep an eye on the wide horizons for better prospects. The organization can be shown a map highlighting other areas of interest that can prove beneficial.If you are a Data Scientist with different feathers in your cap, one being that of a good communicator, you should be able to change a complex form of technical information to a simple and compact form before you present it to the various stakeholders. The information should highlight the challenges, the details of the data, the criteria for success and the anticipated results.If you want to excel in the field of Data Science, you must have an inquisitive bent of mind. The more you ask questions, the more information you gather, the easier it is to come up with paramount business models.6. Data architectureLet us draw some inference from the construction of a building and the role of an architect. Architects have the most knowledge of how the different blocks of buildings can go together and how the different pillars for a block make a strong support system. Like how architects manage and coordinate the entire construction process, so do the Data Scientists while building business models.A Data Scientist needs to understand all that happens to the data from the inception level to when it becomes a model and further until a decision is made based on the model.Not understanding the data architecture can have a tremendous impact on the assumptions made in the process and the decisions arrived at. If a Data Scientist is not familiar with the data architecture, it may lead to the organization taking wrong decisions leading to unexpected and unfavourable results.A slight change within the architecture might lead to situations getting worse for all the involved stakeholders.7. Risk analysis, process improvement, systems engineeringA Data Scientist with sharp business acumen should have the ability to analyse business risks, suggest improvements if any and facilitate further changes in various business processes. As a Data Scientist, you should understand how systems engineering works.If you want to be a Data Scientist and have sharp risk analysis, process improvement and systems engineering skills, you can set yourself for a smooth sail in this vast sea of Data Science.And, rememberYou will no more be a Data Scientist if you stop following scientific theories… After all, Data Science in itself is a major breakthrough in the field of Science.It is always recommended to analyse all the risks that may confront a business before embarking on a journey of model development. This helps in mitigating risks that an organization may have to encounter later. For a smooth business flow, a Data Scientist should also have the nature to probe into the strategies of the various stakeholders and the problems encountered by customers.A Data Scientist should be able to get the picture of the prevailing risks or the various systems that can have a whopping impact on the data or if a model can lead to positive fruition in the form of customer satisfaction.8. Problem-solving and strong business acumenData scientists are not very different when compared to the commoners. We can say this on the lines of problem-solving. The problem solving traits are inherent in every human being. What makes a data scientist stand apart is very good problem-solving skills. We come across complex problems even in everyday situations. How we differ in solving problems is in the perspectives that we apply. Understanding and analyzing before moving on to actually solving the problems by pulling out all the tools in practice is what Data Scientists are good at.The approach that a Data Scientist takes to solve a problem reaps more success than failure. With their approach, they bring critical thinking to the forefront.  Finding a Data Scientist with skill sets at variance is a problem faced by most of the employers.Technical Skills for a Data ScientistWhen the employers are on a hunt to trap the best, they look out for specialization in languages, libraries, and expertise in tech tools. If a candidate comes in with experience, it helps in boosting the profile.Let us see some very important technical skills:PythonRSQLHadoop/Apache SparkJava/SASTableauLet us briefly understand how these languages are in demand.PythonPython is one of the most in-demand languages. This has gained immense popularity as an open-source language. It is widely used both by beginners and experts. Data Scientists need to have Python as one of the primary languages in their kit.RR is altogether a new programming language for statisticians. Anyone with a mathematical bent of mind can learn it. Nevertheless, if you do not appreciate the nuances of Mathematics then it’s difficult to understand R. This never means that you cannot learn it, but without having that mathematical creativity, you cannot harness the power of it.SQLStructured Query Language or SQL is also highly in demand. The language helps in interacting with relational databases. Though it is not of much prominence yet, with a know-how in SQL you can gain a stand in the job market.Hadoop & SparkBoth Hadoop and Spark are open source tools from Apache for big data.Apache Hadoop is an open source software platform. Apache Hadoop helps when you have large data sets on computer clusters built from commodity hardware and you find it difficult to store and process the data sets.Apache Spark is a lightning-fast cluster computing and data processing engine designed for fast computation. It comes with a bunch of development APIs. It supports data workers with efficient execution of streaming, machine learning or SQL workloads.Java & SASWe also have Java and SAS joining the league of languages. These are in-demand languages by large players. Employers offer whopping packages to candidates with expertise in Java and SAS.TableauTableau joins the list as an analytics platform and visualization tool. The tool is powerful and user-friendly. The public version of the tool is available for free. If you wish to keep your data private, you have to consider the costs involved too.Easy tips for a Data ScientistLet us see the in-demand skill set for a Data Scientist in brief.a. A Data Scientist should have the acumen to handle data processing and go about setting models that will help various business processes.b. A Data Scientist should understand the depth of a business problem and the structure of the data that will be used in the process of solving it.c. A Data Scientist should always be ready with an explanation on how the created business models work; even the minute details count.A majority of the crowd out there is good at Maths, Statistics, Engineering or other related subjects. However, when interviewed, they may not show the required traits and when recruited may fail to shine in their performance levels. Sometimes the recruitment process to hire a Data Scientist gets so tedious that employers end up searching with lanterns even in broad daylight. Further, the graphical representation below shows some smart tips for smart Data Scientists.Smart tips for a Data ScientistWhat employers seek the most from Data Scientists?Let us now throw some light into what employers seek the most from Data Scientists:a. A strong sense of analysisb. Machine learning is at the core of what is sought from Data Scientists.c. A Data Scientist should infer and refer to data that has been in practice and will be in practice.d. Data Scientists are expected to be adept at Machine Learning and create models predicting the performance on the basis of demand.e. And, a big NOD to a combo skill set of statistics, Computer Science and Mathematics.Following screenshot shows the requirements of a topnotch employer from a Data Scientist. The requirements were posted on a jobs’ listing website.Let us do a sneak peek into the same job-listing website and see the skills in demand for a Data Scientist.ExampleRecommendations for a Data ScientistWhat are some general recommendations for Data Scientists in the present scenario? Let us walk you through a few.Exhibit your demonstration skills with data analysis and aim to become learned at Machine Learning.Focus on your communication skills. You would have a tough time in your career if you cannot show what you have and cannot communicate what you know. Experts have recommended reading Made to Stick for far-reaching impact of the ideas that you generate.Gain proficiency in deep learning. You must be familiar with the usage, interest, and popularity of deep learning framework.If you are wearing the hat of a Python expert, you must also have the know-how of common python data science libraries – numpy, pandas, matplotlib, and scikit-learn.ConclusionData Science is all about contributing more data to the technologically advanced world. Make your online presence a worthy one; learn while you earn.Start by browsing through online portals. If you are a professional, make your mark on LinkedIn. Securing a job through LinkedIn is now easier than scouring through job sites.Demonstrate all the skills that you are good at on the social portals you are associated with. Suppose you write an article on LinkedIn, do not refrain from sharing the link to the article on your Facebook account.Most important of all – when faced with a complex situation, understand why and what led to the problem. A deeper understanding of a problem will help you come up with the best model. The more you empathize with a situation, the more will be your success count. And in no time, you can become that extraordinary whiz in Data Science.Wishing you immense success if you happen to choose or have already chosen Data Science as the path for your career.All the best for your career endeavour!
Rated 4.5/5 based on 1 customer reviews
9048
Essential Skills to Become a Data Scientist

The demand for Data Science professionals is now a... Read More

What is Bias-Variance Tradeoff in Machine Learning

What is Machine Learning? Machine Learning is a multidisciplinary field of study, which gives computers the ability to solve complex problems, which otherwise would be nearly impossible to be hand-coded by a human being. Machine Learning is a scientific field of study which involves the use of algorithms and statistics to perform a given task by relying on inference from data instead of explicit instructions. Machine Learning Process:The process of Machine Learning can be broken down into several parts, most of which is based around “Data”. The following steps show the Machine Learning Process. 1. Gathering Data from various sources: Since Machine Learning is basically the inference drawn from data before any algorithm can be used, data needs to be collected from some source. Data collected can be of any form, viz. Video data, Image data, Audio data, Text data, Statistical data, etc. 2. Cleaning data to have homogeneity: The data that is collected from various sources does not always come in the desired form. More importantly, data contains various irregularities like Missing data and Outliers.These irregularities may cause the Machine Learning Model(s) to perform poorly. Hence, the removal or processing of irregularities is necessary to promote data homogeneity. This step is also known as data pre-processing. 3. Model Building & Selecting the right Machine Learning Model: After the data has been correctly pre-processed, various Machine Learning Algorithms (or Models) are applied on the data to train the model to predict on unseen data, as well as to extract various insights from the data. After various models are “trained” to the data, the best performing model(s) that suit the application and the performance criteria are selected.4. Getting Insights from the model’s results: Once the model is selected, further data is used to validate the performance and accuracy of the model and get insights as to how the model performs under various conditions. 5. Data Visualization: This is the final step, where the model is used to predict unseen and real-world data. However, these predictions are not directly understandable to the user, and hence, data Visualization or converting the results into understandable visual graphs is necessary. At this stage, the model can be deployed to solve real-world problems.How is Machine Learning different from Curve Fitting? To get the similarities out of the way, both, Machine Learning and Curve Fitting rely on data to infer a model which, ideally, fits the data perfectly. The difference comes in the availability of the data. Curve Fitting is carried out with data, all of which is already available to the user. Hence, there is no question of the model to encounter unseen data.However, in Machine Learning, only a part of the data is available to the user at the time of training (fitting) the model, and then the model has to perform equally well on data that it has never encountered before. Which is, in other words, the generalization of the model over a given data, such that it is able to correctly predict when it is deployed.A high-level introduction to Bias and Variance through illustrative and applied examples Let’s initiate the idea of Bias and Variance with a case study. Let’s assume a simple dataset of predicting the price of a house based on its carpet area. Here, the x-axis represents the carpet area of the house, and the y-axis represents the price of the property. The plotted data (in a 2D graph) is shown in the graph below: The goal is to build a model to predict the price of the house, given the carpet area of the property. This is a rather easy problem to solve and can easily be achieved by fitting a curve to the given data points. But, for the time being, let’s concentrate on solving the same using Machine Learning.In order to keep this example simple and concentrate on Bias and Variance, a few assumptions are made:Adequate data is present in order to come up with a working model capable of making relatively accurate predictions.The data is homogeneous in nature and hence no major pre-processing steps are involved.There are no missing values or outliers, and hence they do not interfere with the outcome in any way. The y-axis data-points are independent of the order of the sequence of the x-axis data-points.With the above assumptions, the data is processed to train the model using the following steps: 1. Shuffling the data: Since the y-axis data-points are independent of the order of the sequence of the x-axis data-points, the dataset is shuffled in a pseudo-random manner. This is done to avoid unnecessary patterns from being learned by the model. During the shuffling, it is imperative to keep each x-y pair data point constant. Mixing them up will change the dataset itself and the model will learn inaccurate patterns. 2. Data Splitting: The dataset is split into three categories: Training Set (60%), Validation Set (20%), and Testing Set (20%). These three sets are used for different purposes:Training Set - This part of the dataset is used to train the model. It is also known as the Development Set. Validation Set - This is separate from the Training Set and is only used for model selection. The model does not train or learn from this part of the dataset.Testing Set - This part of the dataset is used for performance evaluation and is completely independent of the Training or Validation Sets. Similar to the Validation Set, the model does not train on this part of the dataset.3. Model Selection: Several Machine Learning Models are applied to the Training Set and their Training and Validation Losses are determined, which then helps determine the most appropriate model for the given dataset.During this step, we assume that a polynomial equation fits the data correctly. The general equation is given below: The process of “Training” mathematically is nothing more than figuring out the appropriate values for the parameters: a0, a1, ... ,an, which is done automatically by the model using the Training Set.The developer does have control over how high the degree of the polynomial can be. These parameters that can be tuned by the developer are called Hyperparameters. These hyperparameters play a key role in deciding how well would the model learn and how generalized will the learned parameters be. Given below are two graphs representing the prediction of the trained model on training data. The graph on the left represents a linear model with an error of 3.6, and the graph on the right represents a polynomial model with an error of 1.7. By looking at the errors, it can be concluded that the polynomial model performs significantly better when compared to the linear model (Lower the error, better is the performance of the model). However, when we use the same trained models on the Testing Set, the models perform very differently. The graph on the left represents the same linear model’s prediction on the Testing Set, and the graph on the right side represents the Polynomial model’s prediction on the Testing Set. It is clearly visible that the Polynomial model inaccurately predicts the outputs when compared to the Linear model.In terms of error, the total error for the Linear model is 3.6 and for the Polynomial model is a whopping 929.12. Such a big difference in errors between the Training and Testing Set clearly signifies that something is wrong with the Polynomial model. This drastic change in error is due to a phenomenon called Bias-Variance Tradeoff.What is “Error” in Machine Learning? Error in Machine Learning is the difference in the expected output and the predicted output of the model. It is a measure of how well the model performs over a given set of data.There are several methods to calculate error in Machine Learning. One of the most commonly used terminologies to represent the error is called the Loss/Cost Function. It is also known as the Mean Squared Error (or MSE) and is given by the following equation:The necessity of minimization of Errors: As it is obvious from the previously shown graphs, the higher the error, the worse the model performs. Hence, the error of the prediction of a model can be considered as a performance measure: Lower the error of a model, the better it performs. In addition to that, a model judges its own performance and trains itself based on the error created between its own output and the expected output. The primary target of the model is to minimize the error so as to get the best parameters that would fit the data perfectly. Total Error: The error mentioned above is the Total Error and consists of three types of errors: Bias + Variance + Irreducible Error. Total Error = Bias + Variance + Irreducible ErrorEven for an ideal model, it is impossible to get rid of all the types of errors. The “irreducible” error rate is caused by the presence of noise in the data and hence is not removable. However, the Bias and Variance errors can be reduced to a minimum and hence, the total error can also be reduced significantly. Why is the splitting of data important? Ideally, the complete dataset is not used to train the model. The dataset is split into three sets: Training, Validation and Testing Sets. Each of these serves a specific role in the development of a model which performs well under most conditions.Training Set (60-80%): The largest portion of the dataset is used for training the Machine Learning Model. The model extracts the features and learns to recognize the patterns in the dataset. The quality and quantity of the training set determines how well the model is going to perform. Testing Set (15-25%): The main goal of every Machine Learning Engineer is to develop a model which would generalize the best over a given dataset. This is achieved by training the model(s) on a portion of the dataset and testing its performance by applying the trained model on another portion of the same/similar dataset that has not been used during training (Testing Set). This is important since the model might perform too well on the training set, but perform poorly on unseen data, as was the case with the example given above. Testing set is primarily used for model performance evaluation.Validation Set (15-25%): In addition to the above, because of the presence of more than one Machine Learning Algorithm (model), it is often not recommended to test the performance of multiple models on the same dataset and then choose the best one. This process is called Model Selection, and for this, a separate part of the training set is used, which is also known as Validation Set. A validation set behaves similar to a testing set but is primarily used in model selection and not in performance evaluation.Bias and Variance - A Technical Introduction What is Bias?Bias is used to allow the Machine Learning Model to learn in a simplified manner. Ideally, the simplest model that is able to learn the entire dataset and predict correctly on it is the best model. Hence, bias is introduced into the model in the view of achieving the simplest model possible.Parameter based learning algorithms usually have high bias and hence are faster to train and easier to understand. However, too much bias causes the model to be oversimplified and hence underfits the data. Hence these models are less flexible and often fail when they are applied on complex problems.Mathematically, it is the difference between the model’s average prediction and the expected value.What is Variance?Variance in data is the variability of the model in a case where different Training Data is used. This would significantly change the estimation of the target function. Statistically, for a given random variable, Variance is the expectation of squared deviation from its mean. In other words, the higher the variance of the model, the more complex the model is and it is able to learn more complex functions. However, if the model is too complex for the given dataset, where a simpler solution is possible, a model with high Variance causes the model to overfit. When the model performs well on the Training Set and fails to perform on the Testing Set, the model is said to have Variance.Characteristics of a biased model A biased model will have the following characteristics:Underfitting: A model with high bias is simpler than it should be and hence tends to underfit the data. In other words, the model fails to learn and acquire the intricate patterns of the dataset. Low Training Accuracy: A biased model will not fit the Training Dataset properly and hence will have low training accuracy (or high training loss). Inability to solve complex problems: A Biased model is too simple and hence is often incapable of learning complex features and solving relatively complex problems.Characteristics of a model with Variance A model with high Variance will have the following characteristics:Overfitting: A model with high Variance will have a tendency to be overly complex. This causes the overfitting of the model.Low Testing Accuracy: A model with high Variance will have very high training accuracy (or very low training loss), but it will have a low testing accuracy (or a low testing loss). Overcomplicating simpler problems: A model with high variance tends to be overly complex and ends up fitting a much more complex curve to a relatively simpler data. The model is thus capable of solving complex problems but incapable of solving simple problems efficiently.What is Bias-Variance Tradeoff? From the understanding of bias and variance individually thus far, it can be concluded that the two are complementary to each other. In other words, if the bias of a model is decreased, the variance of the model automatically increases. The vice-versa is also true, that is if the variance of a model decreases, bias starts to increase.Hence, it can be concluded that it is nearly impossible to have a model with no bias or no variance since decreasing one increases the other. This phenomenon is known as the Bias-Variance TradeA graphical introduction to Bias-Variance Tradeoff In order to get a clear idea about the Bias-Variance Tradeoff, let us consider the bulls-eye diagram. Here, the central red portion of the target can be considered the location where the model correctly predicts the values. As we move away from the central red circle, the error in the prediction starts to increase. Each of the several hits on the target is achieved by repetition of the model building process. Each hit represents the individual realization of the model. As can be seen in the diagram below, the bias and the variance together influence the predictions of the model under different circumstances.Another way of looking at the Bias-Variance Tradeoff graphically is to plot the graphical representation for error, bias, and variance versus the complexity of the model. In the graph shown below, the green dotted line represents variance, the blue dotted line represents bias and the red solid line represents the error in the prediction of the concerned model. Since bias is high for a simpler model and decreases with an increase in model complexity, the line representing bias exponentially decreases as the model complexity increases. Similarly, Variance is high for a more complex model and is low for simpler models. Hence, the line representing variance increases exponentially as the model complexity increases. Finally, it can be seen that on either side, the generalization error is quite high. Both high bias and high variance lead to a higher error rate. The most optimal complexity of the model is right in the middle, where the bias and variance intersect. This part of the graph is shown to produce the least error and is preferred. Also, as discussed earlier, the model underfits for high-bias situations and overfits for high-variance situations.Mathematical Expression of Bias-Variance Tradeoff The expected values is a vector represented by y. The predicted output of the model is denoted by the vector y for input vector x. The relationship between the predicted values and the inputs can be taken as y = f(x) + e, where e is the normally distributed error given by:The third term in the above equation, irreducible_error represents the noise term and cannot be fundamentally reduced by any given model. If hypothetically, infinite data is available, it is possible to tune the model to reduce the bias and variance terms to zero but is not possible to do so practically. Hence, there is always a tradeoff between the minimization of bias and variance. Detection of Bias and Variance of a modelIn model building, it is imperative to have the knowledge to detect if the model is suffering from high bias or high variance. The methods to detect high bias and variance is given below:Detection of High Bias:The model suffers from a very High Training Error.The Validation error is similar in magnitude to the training error.The model is underfitting.Detection of High Variance:The model suffers from a very Low Training Error.The Validation error is very high when compared to the training error.The model is overfitting.A graphical method to Detect a model suffering from High Bias and Variance is shown below: The graph shows the change in error rate with respect to model complexity for training and validation error. The left portion of the graph suffers from High Bias. This can be seen as the training error is quite high along with the validation error. In addition to that, model complexity is quite low. The right portion of the graph suffers from High Variance. This can be seen as the training error is very low, yet the validation error is very high and starts increasing with increasing model complexity.A systematic approach to solve a Bias-Variance Problem by Dr. Andrew Ng:Dr. Andrew Ng proposed a very simple-to-follow step by step architecture to detect and solve a High Bias and High Variance errors in a model. The block diagram is shown below:Detection and Solution to High Bias problem - if the training error is high: Train longer: High bias means a usually less complex model, and hence it requires more training iterations to learn the relevant patterns. Hence, longer training solves the error sometimes.Train a more complex model: As mentioned above, high bias is a result of a less than optimal complexity in the model. Hence, to avoid high bias, the existing model can be swapped out with a more complex model. Obtain more features: It is often possible that the existing dataset lacks the required essential features for effective pattern recognition. To remedy this problem: More features can be collected for the existing data.Feature Engineering can be performed on existing features to extract more non-linear features. Decrease regularization: Regularization is a process to decrease model complexity by regularizing the inputs at different stages, promote generalization and prevent overfitting in the process. Decreasing regularization allows the model to learn the training dataset better. New model architecture: If all of the above-mentioned methods fail to deliver satisfactory results, then it is suggested to try out other new model architectures. Detection and Solution to High Variance problem - if a validation error is high: Obtain more data: High variance is often caused due to a lack of training data. The model complexity and quantity of training data need to be balanced. A model of higher complexity requires a larger quantity of training data. Hence, if the model is suffering from high variance, more datasets can reduce the variance. Decrease number of features: If the dataset consists of too many features for each data-point, the model often starts to suffer from high variance and starts to overfit. Hence, decreasing the number of features is recommended. Increase Regularization: As mentioned above, regularization is a process to decrease model complexity. Hence, if the model is suffering from high variance (which is caused by a complex model), then an increase in regularization can decrease the complexity and help to generalize the model better.New model architecture: Similar to the solution of a model suffering from high bias, if all of the above-mentioned methods fail to deliver satisfactory results, then it is suggested to try out other new model architectures.Conclusion To summarize, Bias and Variance play a major role in the training process of a model. It is necessary to reduce each of these parameters individually to the minimum possible value. However, it should be kept in mind that an effort to decrease one of these parameters beyond a certain limit increases the probability of the other getting increased. This phenomenon is called as the Bias-Variance Tradeoff and is a parameter to consider during model building. 
Rated 4.5/5 based on 1 customer reviews
7599
What is Bias-Variance Tradeoff in Machine Learning

What is Machine Learning? Machine Learning is a m... Read More

20% Discount