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Machine Learning Algorithms: [With Essentials, Principles, Types & Examples covered]

The advancements in Science and Technology are making every step of our daily life more comfortable. Today, the use of Machine learning systems, which is an integral part of Artificial Intelligence, has spiked and is seen playing a remarkable role in every user’s life. For instance, the widely popular, Virtual Personal Assistant being used for playing a music track or setting an alarm, face detection or voice recognition applications are the awesome examples of the machine learning systems that we see everyday. Machine learning, a subset of artificial intelligence, is the ability of a system to learn or predict the user’s needs and perform an expected task without human intervention. The inputs for the desired predictions are taken from user’s previously performed tasks or from relative examples.Why should you choose Machine Learning?Wonder why one should choose Machine Learning? Simply put, machine learning makes complex tasks much easier.  It makes the impossible possible!The following scenarios explain why we should opt for machine learning:During facial recognition and speech processing, it would be tedious to write the codes manually to execute the process, that's where machine learning comes handy.For market analysis, figuring customer preferences or fraud detection, machine learning has become essential.For the dynamic changes that happen in real-time tasks, it would be a challenging ordeal to solve through human intervention alone.Essentials of ML AlgorithmsTo state simply, machine learning is all about predictions – a machine learning, thinking and predicting what’s next. Here comes the question – what will a machine learn, how will a machine analyze, what will it predict.You have to understand two terms clearly before trying to get answers to these questions:DataAlgorithmDataData is what that is fed to the machine. For example, if you are trying to design a machine that can predict the weather over the next few days, then you should input the past ‘data’ that comprise maximum and minimum air temperatures, the speed of the wind, amount of rainfall, etc. All these come under ‘data’ that your machine will learn, and then analyse later.If we observe carefully, there will always be some pattern or the other in the input data we have. For example, the maximum and minimum ranges of temperatures may fall in the same bracket; or speeds of the wind may be slightly similar for a given season, etc. But, machine learning helps analyse such patterns very deeply. And then it predicts the outcomes of the problem we have designed it for.AlgorithmWhile data is the ‘food’ to the machine, an algorithm is like its digestive system. An algorithm works on the data. It crushes it; analyses it; permutates it; finds the gaps and fills in the blanks.Algorithms are the methods used by machines to work on the data input to them.What to consider before finalizing an ML algorithm?Depending on the functionality expected from the machine, algorithms range from very basic to highly complex. You should be wise in making a selection of an algorithm that suits your ML needs. Careful consideration and testing are needed before finalizing an algorithm for a purpose.For example, linear regression works well for simple ML functions such as speech analysis. In case, accuracy is your first choice, then slightly higher level functionalities such as Neural networks will do.This concept is called ‘The Explainability- Accuracy Tradeoff’. The following diagram explains this better:Image SourceBesides, with regards to machine learning algorithms, you need to remember the following aspects very clearly:No algorithm is an all-in-one solution to any type of problem; an algorithm that fits a scenario is not destined to fit in another one.Comparison of algorithms mostly does not make sense as each one of it has its own features and functionality. Many factors such as the size of data, data patterns, accuracy needed, the structure of the dataset, etc. play a major role in comparing two algorithms.The Principle behind Machine Learning AlgorithmsAs we learnt, an algorithm churns the given data and finds a pattern among them. Thus, all machine learning algorithms, especially the ones used for supervised learning, follow one similar principle:If the input variables or the data is X and you expect the machine to give a prediction or output Y, the machine will work on as per learning a target function ‘f’, whose pattern is not known to us.Thus, Y= f(X) fits well for every supervised machine learning algorithm. This is otherwise also called Predictive Modeling or Predictive Analysis, which ultimately provides us with the best ever prediction possible with utmost accuracy.Types of Machine Learning AlgorithmsDiving further into machine learning, we will first discuss the types of algorithms it has. Machine learning algorithms can be classified as:Supervised, andUnsupervisedSemi-supervised algorithmsReinforcement algorithmsA brief description of the types of  algorithms is given below:1. Supervised machine learning algorithmsIn this method, to get the output for a new set of user’s input, a model is trained to predict the results by using an old set of inputs and its relative known set of outputs. In other words, the system uses the examples used in the past.A data scientist trains the system on identifying the features and variables it should analyze. After training, these models compare the new results to the old ones and update their data accordingly to improve the prediction pattern.An example: If there is a basket full of fruits, based on the earlier specifications like color, shape and size given to the system, the model will be able to classify the fruits.There are 2 techniques in supervised machine learning and a technique to develop a model is chosen based on the type of data it has to work on.A) Techniques used in Supervised learningSupervised algorithms use either of the following techniques to develop a model based on the type of data.RegressionClassification1. Regression Technique In a given dataset, this technique is used to predict a numeric value or continuous values (a range of numeric values) based on the relation between variables obtained from the dataset.An example would be guessing the price of a house based after a year, based on the current price, total area, locality and number of bedrooms.Another example is predicting the room temperature in the coming hours, based on the volume of the room and current temperature.2. Classification Technique This is used if the input data can be categorized based on patterns or labels.For example, an email classification like recognizing a spam mail or face detection which uses patterns to predict the output.In summary, the regression technique is to be used when predictable data is in quantity and Classification technique is to be used when predictable data is about predicting a label.B) Algorithms that use Supervised LearningSome of the machine learning algorithms which use supervised learning method are:Linear RegressionLogistic RegressionRandom ForestGradient Boosted TreesSupport Vector Machines (SVM)Neural NetworksDecision TreesNaive BayesWe shall discuss some of these algorithms in detail as we move ahead in this post.2. Unsupervised machine learning algorithmsThis method does not involve training the model based on old data, I.e. there is no “teacher” or “supervisor” to provide the model with previous examples.The system is not trained by providing any set of inputs and relative outputs.  Instead, the model itself will learn and predict the output based on its own observations.For example, consider a basket of fruits which are not labeled/given any specifications this time. The model will only learn and organize them by comparing Color, Size and shape.A. Techniques used in unsupervised learningWe are discussing these techniques used in unsupervised learning as under:ClusteringDimensionality ReductionAnomaly detectionNeural networks1. ClusteringIt is the method of dividing or grouping the data in the given data set based on similarities.Data is explored to make groups or subsets based on meaningful separations.Clustering is used to determine the intrinsic grouping among the unlabeled data present.An example where clustering principle is being used is in digital image processing where this technique plays its role in dividing the image into distinct regions and identifying image border and the object.2. Dimensionality reductionIn a given dataset, there can be multiple conditions based on which data has to be segmented or classified.These conditions are the features that the individual data element has and may not be unique.If a dataset has too many numbers of such features, it makes it a complex process to segregate the data.To solve such type of complex scenarios, dimensional reduction technique can be used, which is a process that aims to reduce the number of variables or features in the given dataset without loss of important data.This is done by the process of feature selection or feature extraction.Email Classification can be considered as the best example where this technique was used.3. Anomaly DetectionAnomaly detection is also known as Outlier detection.It is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.Examples of the usage are identifying a structural defect, errors in text and medical problems.4. Neural NetworksA Neural network is a framework for many different machine learning algorithms to work together and process complex data inputs.It can be thought of as a “complex function” which gives some output when an input is given.The Neural Network consists of 3 parts which are needed in the construction of the model.Units or NeuronsConnections or Parameters.Biases.Neural networks are into a wide range of applications such as coastal engineering, hydrology and medicine where they are being used in identifying certain types of cancers.B. Algorithms that use unsupervised learningSome of the most common algorithms in unsupervised learning are:hierarchical clustering,k-meansmixture modelsDBSCANOPTICS algorithmAutoencodersDeep Belief NetsHebbian LearningGenerative Adversarial NetworksSelf-organizing mapWe shall discuss some of these algorithms in detail as we move ahead in this post.3.Semi Supervised AlgorithmsIn case of semi-supervised algorithms, as the name goes, it is a mix of both supervised and unsupervised algorithms. Here both labelled and unlabelled examples exist, and in many scenarios of semi-supervised learning, the count of unlabelled examples is more than that of labelled ones.Classification and regression form typical examples for semi-supervised algorithms.The algorithms under semi-supervised learning are mostly extensions of other methods, and the machines that are trained in the semi-supervised method make assumptions when dealing with unlabelled data.Examples of Semi Supervised Learning:Google Photos are the best example of this model of learning. You must have observed that at first, you define the user name in the picture and teach the features of the user by choosing a few photos. Then the algorithm sorts the rest of the pictures accordingly and asks you in case it gets any doubts during classification.Comparing with the previous supervised and unsupervised types of learning models, we can make the following inferences for semi-supervised learning:Labels are entirely present in case of supervised learning, while for unsupervised learning they are totally absent. Semi-supervised is thus a hybrid mix of both these two.The semi-supervised model fits well in cases where cost constraints are present for machine learning modelling. One can label the data as per cost requirements and leave the rest of the data to the machine to take up.Another advantage of semi-supervised learning methods is that they have the potential to exploit the unlabelled data of a group in cases where data carries important unexploited information.4. Reinforcement LearningIn this type of learning, the machine learns from the feedback it has received. It constantly learns and upgrades its existing skills by taking the feedback from the environment it is in.Markov’s Decision process is the best example of reinforcement learning.In this mode of learning, the machine learns iteratively the correct output. Based on the reward obtained from each iteration,the machine knows what is right and what is wrong. This iteration keeps going till the full range of probable outputs are covered.Process of Reinforcement LearningThe steps involved in reinforcement learning are as shown below:Input state is taken by the agentA predefined function indicates the action to be performedBased on the action, the reward is obtained by the machineThe resulting pair of feedback and action is stored for future purposesExamples of Reinforcement Learning AlgorithmsComputer based games such as chessArtificial hands that are based on roboticsDriverless cars/ self-driven carsMost Used Machine Learning Algorithms - ExplainedIn this section, let us discuss the following most widely used machine learning algorithms in detail:Decision TreesNaive Bayes ClassificationThe AutoencoderSelf-organizing mapHierarchical clusteringOPTICS algorithm1. Decision TreesThis algorithm is an example of supervised learning.A Decision tree is a pictorial representation or a graphical representation which depicts every possible outcome of a decision.The various elements involved here are node, branch and leaf where ‘node’ represents an ‘attribute’, ‘branch’ representing a ‘decision’ and ‘leaf’ representing an ‘outcome’ of the feature after applying that particular decision.A decision tree is just an analogy of how a human thinks to take a decision with yes/no questions.The below decision tree explains a school admission procedure rule, where Age is primarily checked, and if age is < 5, admission is not given to them. And for the kids who are eligible for admission, a check is performed on Annual income of parents where if it is < 3 L p.a. the students are further eligible to get a concession on the fees.2. Naive Bayes ClassificationThis supervised machine learning algorithm is a powerful and fast classifying algorithm, using the Bayes rule in determining the conditional probability and to predict the results.Its popular uses are, face recognition, filtering spam emails, predicting the user inputs in chat by checking communicated text and to label news articles as sports, politics etc.Bayes Rule: The Bayes theorem defines a rule in determining the probability of occurrence of an “Event” when information about “Tests” is provided.“Event” can be considered as the patient having a Heart disease while “tests” are the positive conditions that match with the event3. The AutoencoderIt comes under the category of unsupervised learning using neural networking techniques.An autoencoder is intended to learn or encode a representation for a given data set.This also involves the process of dimensional reduction which trains the network to remove the "noise" signal.In hand, with the reduction, it also works in reconstruction where the model tries to rebuild or generate a representation from the reduced encoding which is equivalent to the original input.I.e. without the loss of important and needed information from the given input, an Autoencoder removes or ignores the unnecessary noise and also works on rebuilding the output.Pic sourceThe most common use of Autoencoder is an application that converts black and white image to color. Based on the content and object in the image (like grass, water, sky, face, dress) coloring is processed.4. Self-organizing mapThis comes under the unsupervised learning method.Self-Organizing Map uses the data visualization technique by operating on a given high dimensional data.The Self-Organizing Map is a two-dimensional array of neurons: M = {m1,m2,......mn}It reduces the dimensions of the data to a map, representing the clustering concept by grouping similar data together.SOM reduces data dimensions and displays similarities among data.SOM uses clustering technique on data without knowing the class memberships of the input data where several units compete for the current object.In short, SOM converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display.5. Hierarchical clusteringHierarchical clustering uses one of the below clustering techniques to determine a hierarchy of clusters.Thus produced hierarchy resembles a tree structure which is called a “Dendrogram”.The techniques used in hierarchical clustering are:K-Means,DBSCAN,Gaussian Mixture Models.The 2 methods in finding hierarchical clusters are:Agglomerative clusteringDivisive clusteringAgglomerative clusteringThis is a bottom-up approach, where each data point starts in its own cluster.These clusters are then joined greedily, by taking the two most similar clusters together and merging them.Divisive clusteringInverse to Agglomerative, this uses a top-down approach, wherein all data points start in the same cluster after which a parametric clustering algorithm like K-Means is used to divide the cluster into two clusters.Each cluster is further divided into two clusters until a desired number of clusters are hit.6. OPTICS algorithmOPTICS is an abbreviation for ordering points to identify the clustering structure.OPTICS works in principle like an extended DB Scan algorithm for an infinite number for a distance parameter which is smaller than a generating distance.From a wide range of parameter settings, OPTICS outputs a linear list of all objects under analysis in clusters based on their density.How to Choose Machine Learning Algorithms in Real TimeWhen implementing algorithms in real time, you need to keep in mind three main aspects: Space, Time, and Output.Besides, you should clearly understand the aim of your algorithm:Do you want to make predictions for the future?Are you just categorizing the given data?Is your targeted task simple or comprises of multiple sub-tasks?The following table will show you certain real-time scenarios and help you to understand which algorithm is best suited to each scenario:Real time scenarioBest suited algorithmWhy this algorithm is the best fit?Simple straightforward data set with no complex computationsLinear RegressionIt takes into account all factors involved and predicts the result with simple error rate explanation.For simple computations, you need not spend much computational power; and linear regression runs with minimal computational power.Classifying already labeled data into sub-labelsLogistic RegressionThis algorithm looks at every data point into two subcategories, hence best for sub-labeling.Logistic regression model works best when you have multiple targets.Sorting unlabelled data into groupsK-Means clustering algorithmThis algorithm groups and clusters data by measuring the spatial distance between each point.You can choose from its sub-types - Mean-Shift algorithm and Density-Based Spatial Clustering of Applications with NoiseSupervised text classification (analyzing reviews, comments, etc.)Naive BayesSimplest model that can perform powerful pre-processing and cleaning of textRemoves filler stop words effectivelyComputationally in-expensiveLogistic regressionSorts words one by one and assigns a probabilityRanks next to Naïve Bayes in simplicityLinear Support Vector Machine algorithmCan be chosen when performance mattersBag-of-words modelSuits best when vocabulary and the measure of known words is known.Image classificationConvolutional neural networkBest suited for complex computations such as analyzing visual cortexesConsumes more computational power and gives the best resultsStock market predictionsRecurrent neural networkBest suited for time-series analysis with well-defined and supervised data.Works efficiently in taking into account the relation between data and its time distribution.How to Run Machine Learning Algorithms?Till now you have learned in detail about various algorithms of machine learning, their features, selection and application in real time.When implementing the algorithm in real time, you can do it in any programming language that works well for machine learning.All that you need to do is use the standard libraries of the programming language that you have chosen and work on them, or program everything from scratch.Need more help? You can check these links for more clarity on coding machine learning algorithms in various programming languages.How To Get Started With Machine Learning Algorithms in RHow to Run Your First Classifier in WekaMachine Learning Algorithm Recipes in scikit-learnWhere do we stand in Machine Learning?Machine learning is slowly making strides into as many fields in our daily life as possible. Some businesses are making it strict to have transparent algorithms that do not affect their business privacy or data security. They are even framing regulations and performing audit trails to check if there is any discrepancy in the above-said data policies.The point to note here is that a machine working on machine learning principles and algorithms give output after processing the data through many nonlinear computations. If one needs to understand how a machine predicts, perhaps it can be possible only through another machine learning algorithm!Applications of Machine LearningCurrently, the role of Machine learning and Artificial Intelligence in human life is intertwined. With the advent of evolving technologies, AI and ML have marked their existence in all possible aspects.Machine learning finds a plethora of applications in several domains of our day to day life. An exhaustive list of fields where machine learning is currently in use now is shown in the diagram here. An explanation for the same follows further below:Financial Services: Banks and financial services are increasingly relying on machine learning to identify financial fraud, portfolio management, identify and suggest good options for investment for customers.Police Department: Apps based on facial recognition and other techniques of machine learning are being used by the police to identify and get hold of criminals.Online Marketing and Sales: Machine learning is helping companies a great deal in studying the shopping and spending patterns of customers and in making personalized product recommendations to them. Machine learning also eases customer support, product recommendations and advertising ideas for e-commerce.Healthcare: Doctors are using machine learning to predict and analyze the health status and disease progress of patients. Machine learning has proven its accuracy in detecting health condition, heartbeat, blood pressure and in identifying certain types of cancer. Advanced techniques of machine learning are being implemented in robotic surgery too.Household Applications: Household appliances that use face detection and voice recognition are gaining popularity as security devices and personal virtual assistants at homes.Oil and Gas: In analyzing underground minerals and carrying out the exploration and mining, geologists and scientists are using machine learning for improved accuracy and reduced investments.Transport: Machine learning can be used to identify the vehicles that are moving in prohibited zones for traffic control and safety monitoring purposes.Social Media: In social media, spam is a big nuisance. Companies are using machine learning to filter spam. Machine learning also aptly solves the purpose of sentiment analysis in social media.Trading and Commerce: Machine learning techniques are being implemented in online trading to automate the process of trading. Machines learn from the past performances of trading and use this knowledge to make decisions about future trading options.Future of Machine LearningMachine learning is already making a difference in the way businesses are offering their services to us, the customers. Voice-based search and preferences based ads are just basic functionalities of how machine learning is changing the face of businesses.ML has already made an inseparable mark in our lives. With more advancement in various fields, ML will be an integral part of all AI systems. ML algorithms are going to be made continuously learning with the day-to-day updating information.With the rapid rate at which ongoing research is happening in this field, there will be more powerful machine learning algorithms to make the way we live even more sophisticated!From 2013- 2017, the patents in the field of machine learning has recorded a growth of 34%, according to IFI Claims Patent Services (Patent Analytics). Also, 60% of the companies in the world are using machine learning for various purposes.A peek into the future trends and growth of machine learning through the reports of Predictive Analytics and Machine Learning (PAML) market shows a 21% CAGR by 2021.ConclusionUltimately, machine learning should be designed as an aid that would support mankind. The notion that automation and machine learning are threats to jobs and human workforce is pretty prevalent. It should always be remembered that machine learning is just a technology that has evolved to ease the life of humans by reducing the needed manpower and to offer increased efficiency at lower costs that too in a shorter time span. The onus of using machine learning in a responsible manner lies in the hands of those who work on/with it.However, stay tuned to an era of artificial intelligence and machine learning that makes the impossible possible and makes you witness the unseen!AI is likely to be the best thing or the worst thing to happen to humanity. – Stephen Hawking
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Machine Learning Algorithms: [With Essentials, Principles, Types & Examples covered]

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Machine Learning Algorithms: [With Essentials, Principles, Types & Examples covered]

The advancements in Science and Technology are making every step of our daily life more comfortable. Today, the use of Machine learning systems, which is an integral part of Artificial Intelligence, has spiked and is seen playing a remarkable role in every user’s life. 

For instance, the widely popular, Virtual Personal Assistant being used for playing a music track or setting an alarm, face detection or voice recognition applications are the awesome examples of the machine learning systems that we see everyday. 

Machine learning, a subset of artificial intelligence, is the ability of a system to learn or predict the user’s needs and perform an expected task without human intervention. The inputs for the desired predictions are taken from user’s previously performed tasks or from relative examples.

Why should you choose Machine Learning?

Wonder why one should choose Machine Learning? Simply put, machine learning makes complex tasks much easier.  It makes the impossible possible!

The following scenarios explain why we should opt for machine learning:

Why should you choose Machine Learning

  1. During facial recognition and speech processing, it would be tedious to write the codes manually to execute the process, that's where machine learning comes handy.
  2. For market analysis, figuring customer preferences or fraud detection, machine learning has become essential.
  3. For the dynamic changes that happen in real-time tasks, it would be a challenging ordeal to solve through human intervention alone.

Essentials of ML Algorithms

To state simply, machine learning is all about predictions – a machine learning, thinking and predicting what’s next. Here comes the question – what will a machine learn, how will a machine analyze, what will it predict.

You have to understand two terms clearly before trying to get answers to these questions:

  • Data
  • Algorithm

Essentials of ML Algorithms

Data

Data is what that is fed to the machine. For example, if you are trying to design a machine that can predict the weather over the next few days, then you should input the past ‘data’ that comprise maximum and minimum air temperatures, the speed of the wind, amount of rainfall, etc. All these come under ‘data’ that your machine will learn, and then analyse later.

If we observe carefully, there will always be some pattern or the other in the input data we have. For example, the maximum and minimum ranges of temperatures may fall in the same bracket; or speeds of the wind may be slightly similar for a given season, etc. But, machine learning helps analyse such patterns very deeply. And then it predicts the outcomes of the problem we have designed it for.

Algorithm

Algorithm

While data is the ‘food’ to the machine, an algorithm is like its digestive system. An algorithm works on the data. It crushes it; analyses it; permutates it; finds the gaps and fills in the blanks.

Algorithms are the methods used by machines to work on the data input to them.

What to consider before finalizing an ML algorithm?

Depending on the functionality expected from the machine, algorithms range from very basic to highly complex. You should be wise in making a selection of an algorithm that suits your ML needs. Careful consideration and testing are needed before finalizing an algorithm for a purpose.

For example, linear regression works well for simple ML functions such as speech analysis. In case, accuracy is your first choice, then slightly higher level functionalities such as Neural networks will do.

This concept is called ‘The Explainability- Accuracy Tradeoff’. The following diagram explains this better:

Explainability-accuracy tradeoffImage Source

Besides, with regards to machine learning algorithms, you need to remember the following aspects very clearly:

  • No algorithm is an all-in-one solution to any type of problem; an algorithm that fits a scenario is not destined to fit in another one.
  • Comparison of algorithms mostly does not make sense as each one of it has its own features and functionality. Many factors such as the size of data, data patterns, accuracy needed, the structure of the dataset, etc. play a major role in comparing two algorithms.

The Principle behind Machine Learning Algorithms

As we learnt, an algorithm churns the given data and finds a pattern among them. Thus, all machine learning algorithms, especially the ones used for supervised learning, follow one similar principle:

If the input variables or the data is X and you expect the machine to give a prediction or output Y, the machine will work on as per learning a target function ‘f’, whose pattern is not known to us.

Thus, Y= f(X) fits well for every supervised machine learning algorithm. This is otherwise also called Predictive Modeling or Predictive Analysis, which ultimately provides us with the best ever prediction possible with utmost accuracy.

Types of Machine Learning Algorithms

Diving further into machine learning, we will first discuss the types of algorithms it has. Machine learning algorithms can be classified as:

  • Supervised, and
  • Unsupervised
  • Semi-supervised algorithms
  • Reinforcement algorithms

A brief description of the types of  algorithms is given below:

1. Supervised machine learning algorithms

In this method, to get the output for a new set of user’s input, a model is trained to predict the results by using an old set of inputs and its relative known set of outputs. In other words, the system uses the examples used in the past.

A data scientist trains the system on identifying the features and variables it should analyze. After training, these models compare the new results to the old ones and update their data accordingly to improve the prediction pattern.

An example: If there is a basket full of fruits, based on the earlier specifications like color, shape and size given to the system, the model will be able to classify the fruits.

There are 2 techniques in supervised machine learning and a technique to develop a model is chosen based on the type of data it has to work on.

A) Techniques used in Supervised learning

Supervised algorithms use either of the following techniques to develop a model based on the type of data.

  1. Regression
  2. Classification

1. Regression Technique 

  • In a given dataset, this technique is used to predict a numeric value or continuous values (a range of numeric values) based on the relation between variables obtained from the dataset.
  • An example would be guessing the price of a house based after a year, based on the current price, total area, locality and number of bedrooms.
  • Another example is predicting the room temperature in the coming hours, based on the volume of the room and current temperature.

2. Classification Technique 

  • This is used if the input data can be categorized based on patterns or labels.
  • For example, an email classification like recognizing a spam mail or face detection which uses patterns to predict the output.

In summary, the regression technique is to be used when predictable data is in quantity and Classification technique is to be used when predictable data is about predicting a label.

B) Algorithms that use Supervised Learning

Some of the machine learning algorithms which use supervised learning method are:

  • Linear Regression
  • Logistic Regression
  • Random Forest
  • Gradient Boosted Trees
  • Support Vector Machines (SVM)
  • Neural Networks
  • Decision Trees
  • Naive Bayes

We shall discuss some of these algorithms in detail as we move ahead in this post.

2. Unsupervised machine learning algorithms

This method does not involve training the model based on old data, I.e. there is no “teacher” or “supervisor” to provide the model with previous examples.

The system is not trained by providing any set of inputs and relative outputs.  Instead, the model itself will learn and predict the output based on its own observations.

For example, consider a basket of fruits which are not labeled/given any specifications this time. The model will only learn and organize them by comparing Color, Size and shape.

A. Techniques used in unsupervised learning

We are discussing these techniques used in unsupervised learning as under:

  • Clustering
  • Dimensionality Reduction
  • Anomaly detection
  • Neural networks

1. Clustering

  • It is the method of dividing or grouping the data in the given data set based on similarities.
  • Data is explored to make groups or subsets based on meaningful separations.
  • Clustering is used to determine the intrinsic grouping among the unlabeled data present.
  • An example where clustering principle is being used is in digital image processing where this technique plays its role in dividing the image into distinct regions and identifying image border and the object.

2. Dimensionality reduction

  • In a given dataset, there can be multiple conditions based on which data has to be segmented or classified.
  • These conditions are the features that the individual data element has and may not be unique.
  • If a dataset has too many numbers of such features, it makes it a complex process to segregate the data.
  • To solve such type of complex scenarios, dimensional reduction technique can be used, which is a process that aims to reduce the number of variables or features in the given dataset without loss of important data.
  • This is done by the process of feature selection or feature extraction.
  • Email Classification can be considered as the best example where this technique was used.

3. Anomaly Detection

  • Anomaly detection is also known as Outlier detection.
  • It is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
  • Examples of the usage are identifying a structural defect, errors in text and medical problems.

4. Neural NetworksNeural Networks

  • A Neural network is a framework for many different machine learning algorithms to work together and process complex data inputs.
  • It can be thought of as a “complex function” which gives some output when an input is given.
  • The Neural Network consists of 3 parts which are needed in the construction of the model.
    • Units or Neurons
    • Connections or Parameters.
    • Biases.

Neural networks are into a wide range of applications such as coastal engineering, hydrology and medicine where they are being used in identifying certain types of cancers.

B. Algorithms that use unsupervised learning

Some of the most common algorithms in unsupervised learning are:

  1. hierarchical clustering,
  2. k-means
  3. mixture models
  4. DBSCAN
  5. OPTICS algorithm
  6. Autoencoders
  7. Deep Belief Nets
  8. Hebbian Learning
  9. Generative Adversarial Networks
  10. Self-organizing map

We shall discuss some of these algorithms in detail as we move ahead in this post.

3.Semi Supervised Algorithms

In case of semi-supervised algorithms, as the name goes, it is a mix of both supervised and unsupervised algorithms. Here both labelled and unlabelled examples exist, and in many scenarios of semi-supervised learning, the count of unlabelled examples is more than that of labelled ones.

Classification and regression form typical examples for semi-supervised algorithms.

The algorithms under semi-supervised learning are mostly extensions of other methods, and the machines that are trained in the semi-supervised method make assumptions when dealing with unlabelled data.

Examples of Semi Supervised Learning:

Google Photos are the best example of this model of learning. You must have observed that at first, you define the user name in the picture and teach the features of the user by choosing a few photos. Then the algorithm sorts the rest of the pictures accordingly and asks you in case it gets any doubts during classification.

Comparing with the previous supervised and unsupervised types of learning models, we can make the following inferences for semi-supervised learning:

  • Labels are entirely present in case of supervised learning, while for unsupervised learning they are totally absent. Semi-supervised is thus a hybrid mix of both these two.
  • The semi-supervised model fits well in cases where cost constraints are present for machine learning modelling. One can label the data as per cost requirements and leave the rest of the data to the machine to take up.
  • Another advantage of semi-supervised learning methods is that they have the potential to exploit the unlabelled data of a group in cases where data carries important unexploited information.

4. Reinforcement Learning

In this type of learning, the machine learns from the feedback it has received. It constantly learns and upgrades its existing skills by taking the feedback from the environment it is in.

Markov’s Decision process is the best example of reinforcement learning.

In this mode of learning, the machine learns iteratively the correct output. Based on the reward obtained from each iteration,the machine knows what is right and what is wrong. This iteration keeps going till the full range of probable outputs are covered.

Process of Reinforcement Learning

The steps involved in reinforcement learning are as shown below:

  1. Input state is taken by the agent
  2. A predefined function indicates the action to be performed
  3. Based on the action, the reward is obtained by the machine
  4. The resulting pair of feedback and action is stored for future purposes

Examples of Reinforcement Learning Algorithms

  • Computer based games such as chess
  • Artificial hands that are based on robotics
  • Driverless cars/ self-driven cars

Most Used Machine Learning Algorithms - Explained

In this section, let us discuss the following most widely used machine learning algorithms in detail:

  1. Decision Trees
  2. Naive Bayes Classification
  3. The Autoencoder
  4. Self-organizing map
  5. Hierarchical clustering
  6. OPTICS algorithm

1. Decision Trees

  • This algorithm is an example of supervised learning.
  • A Decision tree is a pictorial representation or a graphical representation which depicts every possible outcome of a decision.
  • The various elements involved here are node, branch and leaf where ‘node’ represents an ‘attribute’, ‘branch’ representing a ‘decision’ and ‘leaf’ representing an ‘outcome’ of the feature after applying that particular decision.
  • A decision tree is just an analogy of how a human thinks to take a decision with yes/no questions.
  • The below decision tree explains a school admission procedure rule, where Age is primarily checked, and if age is < 5, admission is not given to them. And for the kids who are eligible for admission, a check is performed on Annual income of parents where if it is < 3 L p.a. the students are further eligible to get a concession on the fees.

Decision Trees

2. Naive Bayes Classification

  • This supervised machine learning algorithm is a powerful and fast classifying algorithm, using the Bayes rule in determining the conditional probability and to predict the results.
  • Its popular uses are, face recognition, filtering spam emails, predicting the user inputs in chat by checking communicated text and to label news articles as sports, politics etc.
  • Bayes Rule: The Bayes theorem defines a rule in determining the probability of occurrence of an “Event” when information about “Tests” is provided.

Bayes Rule

  • “Event” can be considered as the patient having a Heart disease while “tests” are the positive conditions that match with the event

3. The Autoencoder

  • It comes under the category of unsupervised learning using neural networking techniques.
  • An autoencoder is intended to learn or encode a representation for a given data set.
  • This also involves the process of dimensional reduction which trains the network to remove the "noise" signal.
  • In hand, with the reduction, it also works in reconstruction where the model tries to rebuild or generate a representation from the reduced encoding which is equivalent to the original input.
  • I.e. without the loss of important and needed information from the given input, an Autoencoder removes or ignores the unnecessary noise and also works on rebuilding the output.

 The Autoencoder

Pic source

  • The most common use of Autoencoder is an application that converts black and white image to color. Based on the content and object in the image (like grass, water, sky, face, dress) coloring is processed.

4. Self-organizing map

  • This comes under the unsupervised learning method.
  • Self-Organizing Map uses the data visualization technique by operating on a given high dimensional data.
  • The Self-Organizing Map is a two-dimensional array of neurons: M = {m1,m2,......mn}
  • It reduces the dimensions of the data to a map, representing the clustering concept by grouping similar data together.
  • SOM reduces data dimensions and displays similarities among data.
  • SOM uses clustering technique on data without knowing the class memberships of the input data where several units compete for the current object.
  • In short, SOM converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display.

5. Hierarchical clustering

  • Hierarchical clustering uses one of the below clustering techniques to determine a hierarchy of clusters.
  • Thus produced hierarchy resembles a tree structure which is called a “Dendrogram”.
  • The techniques used in hierarchical clustering are:
    • K-Means,
    • DBSCAN,
    • Gaussian Mixture Models.
  • The 2 methods in finding hierarchical clusters are:
  1. Agglomerative clustering
  2. Divisive clustering
  • Agglomerative clustering

    • This is a bottom-up approach, where each data point starts in its own cluster.
    • These clusters are then joined greedily, by taking the two most similar clusters together and merging them.
  • Divisive clustering

    • Inverse to Agglomerative, this uses a top-down approach, wherein all data points start in the same cluster after which a parametric clustering algorithm like K-Means is used to divide the cluster into two clusters.
    • Each cluster is further divided into two clusters until a desired number of clusters are hit.

6. OPTICS algorithm

  • OPTICS is an abbreviation for ordering points to identify the clustering structure.
  • OPTICS works in principle like an extended DB Scan algorithm for an infinite number for a distance parameter which is smaller than a generating distance.
  • From a wide range of parameter settings, OPTICS outputs a linear list of all objects under analysis in clusters based on their density.

How to Choose Machine Learning Algorithms in Real Time

When implementing algorithms in real time, you need to keep in mind three main aspects: Space, Time, and Output.

Besides, you should clearly understand the aim of your algorithm:

  • Do you want to make predictions for the future?
  • Are you just categorizing the given data?
  • Is your targeted task simple or comprises of multiple sub-tasks?

The following table will show you certain real-time scenarios and help you to understand which algorithm is best suited to each scenario:

Real time scenario
Best suited algorithmWhy this algorithm is the best fit?
Simple straightforward data set with no complex computationsLinear Regression
  • It takes into account all factors involved and predicts the result with simple error rate explanation.
  • For simple computations, you need not spend much computational power; and linear regression runs with minimal computational power.
Classifying already labeled data into sub-labelsLogistic Regression
  • This algorithm looks at every data point into two subcategories, hence best for sub-labeling.
  • Logistic regression model works best when you have multiple targets.
Sorting unlabelled data into groupsK-Means clustering algorithm
  • This algorithm groups and clusters data by measuring the spatial distance between each point.
  • You can choose from its sub-types - Mean-Shift algorithm and Density-Based Spatial Clustering of Applications with Noise
Supervised text classification (analyzing reviews, comments, etc.)Naive Bayes
  • Simplest model that can perform powerful pre-processing and cleaning of text
  • Removes filler stop words effectively
  • Computationally in-expensive

Logistic regression
  • Sorts words one by one and assigns a probability
  • Ranks next to Naïve Bayes in simplicity
Linear Support Vector Machine algorithm
  • Can be chosen when performance matters
Bag-of-words model
  • Suits best when vocabulary and the measure of known words is known.
Image classificationConvolutional neural network
  • Best suited for complex computations such as analyzing visual cortexes
  • Consumes more computational power and gives the best results
Stock market predictionsRecurrent neural network
  • Best suited for time-series analysis with well-defined and supervised data.
  • Works efficiently in taking into account the relation between data and its time distribution.

How to Run Machine Learning Algorithms?

Till now you have learned in detail about various algorithms of machine learning, their features, selection and application in real time.

When implementing the algorithm in real time, you can do it in any programming language that works well for machine learning.

All that you need to do is use the standard libraries of the programming language that you have chosen and work on them, or program everything from scratch.

Need more help? You can check these links for more clarity on coding machine learning algorithms in various programming languages.

How To Get Started With Machine Learning Algorithms in R

How to Run Your First Classifier in Weka

Machine Learning Algorithm Recipes in scikit-learn

Where do we stand in Machine Learning?

Machine learning is slowly making strides into as many fields in our daily life as possible. Some businesses are making it strict to have transparent algorithms that do not affect their business privacy or data security. They are even framing regulations and performing audit trails to check if there is any discrepancy in the above-said data policies.

The point to note here is that a machine working on machine learning principles and algorithms give output after processing the data through many nonlinear computations. If one needs to understand how a machine predicts, perhaps it can be possible only through another machine learning algorithm!

Applications of Machine Learning

Applications of Machine Learning

Currently, the role of Machine learning and Artificial Intelligence in human life is intertwined. With the advent of evolving technologies, AI and ML have marked their existence in all possible aspects.

Machine learning finds a plethora of applications in several domains of our day to day life. An exhaustive list of fields where machine learning is currently in use now is shown in the diagram here. An explanation for the same follows further below:

  1. Financial Services: Banks and financial services are increasingly relying on machine learning to identify financial fraud, portfolio management, identify and suggest good options for investment for customers.
  2. Police DepartmentApps based on facial recognition and other techniques of machine learning are being used by the police to identify and get hold of criminals.
  3. Online Marketing and Sales: Machine learning is helping companies a great deal in studying the shopping and spending patterns of customers and in making personalized product recommendations to them. Machine learning also eases customer support, product recommendations and advertising ideas for e-commerce.
  4. Healthcare: Doctors are using machine learning to predict and analyze the health status and disease progress of patients. Machine learning has proven its accuracy in detecting health condition, heartbeat, blood pressure and in identifying certain types of cancer. Advanced techniques of machine learning are being implemented in robotic surgery too.
  5. Household Applications: Household appliances that use face detection and voice recognition are gaining popularity as security devices and personal virtual assistants at homes.
  6. Oil and Gas: In analyzing underground minerals and carrying out the exploration and mining, geologists and scientists are using machine learning for improved accuracy and reduced investments.
  7. TransportMachine learning can be used to identify the vehicles that are moving in prohibited zones for traffic control and safety monitoring purposes.
  8. Social Media: In social media, spam is a big nuisance. Companies are using machine learning to filter spam. Machine learning also aptly solves the purpose of sentiment analysis in social media.
  9. Trading and Commerce: Machine learning techniques are being implemented in online trading to automate the process of trading. Machines learn from the past performances of trading and use this knowledge to make decisions about future trading options.

Future of Machine Learning

Machine learning is already making a difference in the way businesses are offering their services to us, the customers. Voice-based search and preferences based ads are just basic functionalities of how machine learning is changing the face of businesses.

ML has already made an inseparable mark in our lives. With more advancement in various fields, ML will be an integral part of all AI systems. ML algorithms are going to be made continuously learning with the day-to-day updating information.

With the rapid rate at which ongoing research is happening in this field, there will be more powerful machine learning algorithms to make the way we live even more sophisticated!

From 2013- 2017, the patents in the field of machine learning has recorded a growth of 34%, according to IFI Claims Patent Services (Patent Analytics). Also, 60% of the companies in the world are using machine learning for various purposes.

A peek into the future trends and growth of machine learning through the reports of Predictive Analytics and Machine Learning (PAML) market shows a 21% CAGR by 2021.

Conclusion

Ultimately, machine learning should be designed as an aid that would support mankind. The notion that automation and machine learning are threats to jobs and human workforce is pretty prevalent. It should always be remembered that machine learning is just a technology that has evolved to ease the life of humans by reducing the needed manpower and to offer increased efficiency at lower costs that too in a shorter time span. The onus of using machine learning in a responsible manner lies in the hands of those who work on/with it.

However, stay tuned to an era of artificial intelligence and machine learning that makes the impossible possible and makes you witness the unseen!

AI is likely to be the best thing or the worst thing to happen to humanity. – Stephen Hawking

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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. 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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. 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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. 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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!
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What Is Machine Learning: Everything That 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 ML 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. 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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 MLCurrently, 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.
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What Is Machine Learning: Everything That You Need...

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Skills to Become Machine Learning Engineer

Machine learning has been making a silent revolution in our lives since the past decade. From capturing selfies with a blurry background and focused face capture to getting our queries answered by virtual assistants such as Siri and Alexa, we are increasingly depending on products and applications that implement machine learning at their core.In more basic terms, machine learning is one of the steps involved in artificial intelligence. Machines learn through machine learning. How exactly? Just like how humans learn – through training, experience, and feedback.Once machines learn through machine learning, they implement the knowledge so acquired for many purposes including, but not limited to, sorting, diagnosis, robotics, analysis and predictions in many fields.It is these implementations and applications that have made machine learning an in-demand skill in the field of programming and technology.Look at the stats that show a positive trend for machine learning projects and careers.Gartner’s report on artificial intelligence showed that as many as 2.3 million jobs in machine learning would be available across the globe by 2020.Another study from Indeed, the online job portal giant, revealed that machine learning engineers, data scientists and software engineers with these skills are topping the list of most in-demand professionals.High profile companies such as Univa, Microsoft, Apple, Google, and Amazon have invested millions of dollars on machine learning research and designing and are developing their future projects on it.With so much happening around machine learning, it is no surprise that any enthusiast who is keen on shaping their career in software programming and technology would prefer machine learning as a foundation to their career. This post is specifically aimed at guiding such enthusiasts and gives comprehensive information on skills that are needed to become a machine learning engineer, who is ready to dive into the real-time challenges.Machine Learning SkillsOrganizations are showing massive interest in using machine learning in their products, which would in turn bring plenty of opportunities for machine learning enthusiasts.When you ask machine learning engineers the question – “What do you do as a machine learning engineer?”, chances are high that individual answers would differ from one professional to another. This may sound a little puzzling, but yes, this is true!Hence, a beginner to machine learning needs to have a clear understanding that there are different roles that they can perform with machine learning skills. And accordingly the skill set that they should possess, would differ. This section will give clarity on machine learning skills that are needed to perform various machine learning roles.Broadly, three main roles come into the picture when you talk about machine learning skills:Data EngineerMachine Learning EngineerMachine Learning ScientistOne must understand that data science, machine learning and artificial intelligence are interlinked. The following quote explains this better:Data science produces insights. Machine learning produces predictions. Artificial intelligence produces actions.A machine learning engineer is someone who deals with huge volumes of data to train a machine and impart it with knowledge that it uses to perform a specified task. However, in practice, there may be a little more to add to this:Machine Learning RoleSkills RequiredRoles and ResponsibilitiesData EngineerPython, R, and DatabasesParallel and distributed Knowledge on quality and reliabilityVirtual machines and cloud environmentMapReduce and HadoopCleaning, manipulating and extracting the required data   Developing code for data analysis and manipulationPlays a major role in statistical analysis of dataMachine Learning EngineerConcepts of computer science and software engineeringData analysis and feature engineeringMetrics involved in MLML algorithm selection, and cross validationMath and StatisticsAnalyses and checks the suitability of an algorithm if it caters the needs of the current taskPlays main role in deciding and selecting machine learning libraries for given task.Machine Learning ScientistExpert knowledge in:Robotics and Machine LearningCognitive ScienceEngineeringMathematics and mathematical modelsDesigns new models and algorithms of machine learningResearches intensively on machine learning and publishes their research papers.Thus, gaining machine learning skills should be a task associated with clarity on the job role and of course the passion to learn them. As it is widely known, becoming a machine learning engineer is not a straightforward task like becoming a web developer or a tester.Irrespective of the role, a learner is expected to have solid knowledge on data science. Besides, many other subjects are intricately intertwined in learning machine learning and for a learner it requires a lot of patience and zeal to learn skills and build them up as they move ahead in their career.The following diagram shows the machine learning skills that are in demand year after year:AI - Artificial IntelligenceTensorFlowApache KafkaData ScienceAWS - Amazon Web Services                                                                                                                                                                                                                                                                                                                                Image SourceIn the coming sections, we would be discussing each of these skills in detail and how proficient you are expected to be in them.Technical skills required to become ML EngineerBecoming a machine learning engineer means preparing oneself to handle interesting and challenging tasks that would change the way humanity is experiencing things right now. It demands both technical and non-technical expertise. Firstly, let’s talk about the technical skills needed for a machine learning engineer. Here is a list of technical skills a machine learning engineer is expected to possess:Applied MathematicsNeural Network ArchitecturesPhysicsData Modeling and EvaluationAdvances Signal Processing TechniquesNatural Language ProcessingAudio and video ProcessingReinforcement LearningLet us delve into each skill in detail now:1.Applied MathematicsMathematics plays an important role in machine learning, and hence it is the first one on the list. If you wish to see yourself as a proven machine learning engineer, you ought to love math and be an expert in the following specializations of math.But first let us understand why a machine learning engineer would need math at all? There are many scenarios where a machine learning engineer should depend on math. For example:Choosing the right algorithm that suits the final needsUnderstanding and working with parameters and their settings.Deciding on validation strategiesApproximating the confidence intervals.How much proficiency in Math does a machine learning engineer need to have?It depends on the level at which a machine learning engineer works. The following diagram gives an idea about how important various concepts of math are for a machine learning enthusiast.A) Linear algebra: 15%B) Probability Theory and Statistics: 25%C) Multivariate Calculus: 15%D) Algorithms and Optimization: 15%F) Other concepts: 10%Data SourceA) Linear AlgebraThis concept plays a main role in machine learning. One has to be skilled in the following sub-topics of linear algebra:Principal Component Analysis (PCA), Singular Value Decomposition (SVD)Eigen decomposition of a matrixLU DecompositionQR Decomposition/FactorizationSymmetric MatricesOrthogonalization & OrthonormalizationMatrix OperationsProjectionsEigenvalues & EigenvectorsVector Spaces and NormsB) Probability Theory and StatisticsThe core aim of machine learning is to reduce the probability of error in the final output and decision making of the machine. Thus, it is no wonder that probability and statistics play a major role.The following topics are important in these subjects:CombinatoricsProbability Rules & AxiomsBayes’ TheoremRandom VariablesVariance and ExpectationConditional and Joint DistributionsStandard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)Moment Generating Functions, Maximum Likelihood Estimation (MLE)Prior and PosteriorMaximum a Posteriori Estimation (MAP)Sampling Methods.C) CalculusIn calculus, the following concepts have notable importance in machine learning:Integral CalculusPartial Derivatives,Vector-Values FunctionsDirectional GradientHessian, Jacobian, Laplacian and Lagrangian Distributions.D) Algorithms and OptimizationThe scalability and the efficiency of computation of a machine learning algorithm depends on the chosen algorithm and optimization technique adopted. The following areas are important from this perspective:Data structures (Binary Trees, Hashing, Heap, Stack etc)Dynamic ProgrammingRandomized & Sublinear AlgorithmGraphsGradient/Stochastic DescentsPrimal-Dual methodsE) Other ConceptsBesides, the ones mentioned above, other concepts of mathematics are also important for a learner of machine learning. They are given below:Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions Limits, Cauchy Kernel, Fourier Transforms)Information Theory (Entropy, Information Gain)Function Spaces and Manifolds2.Neural Network ArchitecturesNeural networks are the predefined set of algorithms for implementing machine learning tasks. They offer a class of models and play a key role in machine learning.The following are the key reasons why a machine learning enthusiast needs to be skilled in neural networks:Neural networks let one understand how the human brain works and help to model and simulate an artificial one.Neural networks give a deeper insight of parallel computations and sequential computationsThe following are the areas of neural networks that are important for machine learning:Perceptrons Convolutional Neural Networks Recurrent Neural NetworkLong/Short Term Memory Network (LSTM)Hopfield Networks Boltzmann Machine NetworkDeep Belief NetworkDeep Auto-encoders3.PhysicsHaving an idea of physics definitely helps a machine learning engineer. It makes a difference in designing complex systems and is a skill that is a definite bonus for a machine learning enthusiast.4.Data Modeling and EvaluationA machine learning has to work with huge amounts of data and leverage them into predictive analytics. Data modeling and evaluation is important in working with such bulky volumes of data and estimating how good the final model is.For this purpose, the following concepts are worth learnable for a machine learning engineer:Classification AccuracyLogarithmic LossConfusion MatrixArea under CurveF1 ScoreMean Absolute ErrorMean Squared Error5.Advanced Signal Processing TechniquesThe crux of signal processing is to minimize noise and extract the best features of a given signal.For this purpose, it uses certain concepts such as:convex/greedy optimization theory and algorithmsspectral time-frequency analysis of signalsAlgorithms such as wavelets, shearlets, curvelets, contourlets, bandlets, etc.All these concepts find their application in machine learning as well.6. Natural language processingThe importance of natural language processing in artificial intelligence and machine learning is not to be forgotten. Various libraries and techniques of natural language processing used in machine learning are listed here:Gensim and NLTKWord2vecSentiment analysisSummarization7. Audio and Video ProcessingThis differs from natural language processing in the sense that we can apply audio and video processing on audio signals only. For achieving this, the following concepts are essential for a machine learning engineer:Fourier transformsMusic theoryTensorFlow8. Reinforcement LearningThough reinforcement learning plays a major role in learning and understanding deep learning and artificial intelligence, it is good for a beginner of machine learning to know the basic concepts of reinforcement learning.Programming skills required to become ML EngineerMachine learning, ultimately, is coding and feeding the code to the machines and getting them to do the tasks we intend them to do. As such, a machine learning engineer should have hands-on expertise in software programming and related concepts. Here is a list of programming skills a machine learning engineer is expected to have knowledge on:Computer Science Fundamentals and ProgrammingSoftware Engineering and System DesignMachine Learning Algorithms and LibrariesDistributed computingUnixLet us look into each of these programming skills in detail now:1.Computer Science Fundamentals and ProgrammingIt is important that a machine learning engineer apply the concepts of computer science and programming correctly as the situation demands. The following concepts play an important role in machine learning and are a must on the list of the skillsets a machine learning engineer needs to have:Data structures (stacks, queues, multi-dimensional arrays, trees, graphs)Algorithms (searching, sorting, optimization, dynamic programming)Computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.)Computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.)2.Software Engineering and System DesignWhatever a machine learning engineer does, ultimately it is a piece of software code – a beautiful conglomerate of many essential concepts and the one that is entirely different from coding in other software languages.Hence, it is quintessential that a machine learning engineer have solid knowledge of the following areas of software programming and system design:Scaling algorithms with the size of dataBasic best practices of software coding and design, such as requirement analysis, version control, and testing.Communicating with different modules and components of work using library calls, REST APIs and querying through databases.Best measures to avoid bottlenecks and designing the final product such that it is user-friendly.3. Machine Learning Algorithms and LibrariesA machine learning engineer may need to work with multiple packages, libraries, algorithms as a part of day-to-day tasks. It is important that a machine learning engineer is well-versed with the following aspects of machine learning algorithms and libraries:A thorough idea of various learning procedures including linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods.Sound knowledge in packages and APIs such as scikit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc.Expertise in models such as decision trees, nearest neighbor, neural net, support vector machine and a knack to deciding which one fits the best.Deciding and choosing hyperparameters that affect learning model and the outcome.Comfortable to work with concepts such as gradient descent, convex optimization, quadratic programming, partial differential equations.Select an algorithm which yields the best performance from random forests, support vector machines (SVMs), and Naive Bayes Classifiers, etc.4   Distributed Computing Working as a machine learning engineer means working with huge sets of data, not just focused on one isolated system, but spread among a cluster of systems. For this purpose, it is important that a machine learning engineer knows the concepts of distributed computing.5. UnixMost clusters and servers that machine learning engineers need to work are variants of Linux(Unix). Though randomly they work on Windows and Mac, more than half of the time, they need to work on Unix systems only. Hence having sound knowledge on Unix and Linux is a key skill to become a machine learning engineer.Programming Languages for Machine LearningMachine learning engineers need to code to train machines. Several programming languages can be used to do this. The list of programming languages that a machine learning expert should essentially know are as under:C, C++ and JavaSpark and HadoopR ProgrammingApache KafkaPythonWeka PlatformMATLAB/OctaveIn this section, let us know in detail why each of these programming languages is important for a machine learning engineer:1.C, C++ and JavaThese languages give essentials of programming and teach many concepts in a simple manner that form a foundation stone for working on complex programming patterns of machine learning. Knowledge of C++ helps to improve the speed of the program, while Java is needed to work with Hadoop and Hive, and other tools that are essential for a machine learning engineer.2.Spark and HadoopHadoop skills are needed for working in a distributed computing environment. Spark, a recent variant of Hadoop is gaining popularity among the machine learning tribe. It is a framework to implement machine learning on a large scale.3.R ProgrammingR is a programming language built by statisticians specifically to work with programming that involves statistics. Many mathematical computations of machine learning are based on statistics; hence it is no wonder that a machine learning engineer needs to have sound knowledge in R programming.4.Apache KafkaApache Kafka concepts such as Kafka Streams and KSQL play a major role in pre-processing of data in machine learning. Also, a sound knowledge of Apache Kafka lets a machine learning engineer to design solutions that are both multi-cloud based or hybrid cloud-based.  Other concepts such as business information such as latency and model accuracy are also from Kafka and find use in Machine learning.5.PythonOf late, Python has become the unanimous programming language for machine learning. In fact, experts quote that humans communicate with machines through Python language.Why Python is preferred for Machine Learning?Python Programming Language has several key features and benefits that make it the monarch of programming languages for machine learning:It is an all-in-one purpose programming language that can do a lot more than dealing with statistics.It is beginner friendly and easy to learn.It boasts of rich libraries and APIs that solve various needs of machine learning pretty easily.Its productivity is higher than its other counterparts.It offers ease of integration and gets the workflow smoothly from the designing stage to the production stage.Python EcoSystemThere are various components of Python that make it preferred language for machine learning. Such components are discussed below:Jupyter NotebookNumpyPandasScikit-LearnTensorFlow1.Jupyter NotebookJupyter offers excellent computational environment for Python based data science applications. With the help of Jupyter notebook, a machine learning engineer can illustrate the flow of the process step-by-step very clearly.2.NumPyNumPy or Numerical Python is one of the components of Python that allows the following operations of machine learning in a smooth way:Fourier transformationLinear algebraic operationsLogical and numerical operations on arrays.Of late, NumPy is gaining attention because it makes an excellent substitute to MATLAB, as it coordinates with Matplotlib and SciPy very smoothly.3.PandasPandas is a Python library that offers various features for loading, manipulating, analysing, modeling and preparing data. It is entirely dedicated for data analysis and manipulation.4.Scikit-learnBuilt on NumPy, SciPy, and Matplotlib, it is an open-source library of Python. It offers excellent features and functionalities for major aspects of machine learning such as clustering, dimensionality reduction, model reduction, regression and classification.5.TensorFlowTensorFlow is another framework of Python. It finds its usage in deep learning and having a knowledge of its libraries such as Keras, helps a machine learning engineer to move ahead confidently in their career.6.Weka PlatformIt is widely known that machine learning is a non-linear process that involves many iterations. Weka or Waikato Environment for Knowledge Analysis is a recent platform that is designed specifically designed for applied machine learning. This tool is also slowing gaining its popularity and thus is a must-include on the list of skills for a machine learning engineer.7.MATLAB/OctaveThis is a basic programming language that was used for simulation of various engineering models. Though not popularly used in machine learning, having sound knowledge in MATLAB lets one learns the other mentioned libraries of Python easily.Soft skills or behavioural skills required to become ML engineerTechnical skills are relevant only when they are paired with good soft skills. And the machine learning profession is no exception to this rule. Here is a list of soft skills that a machine learning engineer should have:Domain knowledgeCommunication SkillsProblem-solving skillsRapid prototypingTime managementLove towards constant learningLet us move ahead and discuss how each of these skills make a difference to a machine learning engineer.1.Domain knowledgeMachine learning is such a subject that needs the best of its application in real-time. Choosing the best algorithm while solving a machine learning problem in your academia is far different from what you do in practice. Various aspects of business come into picture when you are a real-time machine learning engineer. Hence, a solid understanding of the business and domain of machine learning is of utmost importance to succeed as a good machine learning engineer.2.Communication SkillsAs a machine learning engineer, you need to communicate with offshore teams, clients and other business teams. Excellent communication skills are a must to boost your reputation and confidence and to bring up your work in front of peers.3.Problem-solving skillsMachine learning is all about solving real time challenges. One must have good problem-solving skills and be able to weigh the pros and cons of the given problem and apply the best possible methods to solve it.4.Rapid PrototypingChoosing the correct learning method or the algorithm are signs of a machine learning engineer’s good prototyping skills. These skills would be a great saviour in real time as they would show a huge impact on budget and time taken for successfully completing a machine learning project.5.Time managementTraining a machine is not a cake-walk. It takes huge time and patience to train a machine. But it’s not always that machine learning engineers are allotted ample time for completing tasks. Hence, time management is an essential skill a machine learning professional should have to effectively deal with bottlenecks and deadlines.6.Love towards constant learningSince its inception, machine learning has witnessed massive change – both in the way it is implemented and in its final form. As we have seen in the previous section, technical and programming skills that are needed for machine learning are constantly evolving. Hence, to prove oneself a successful machine learning expert, it is very crucial that they have a zeal to update themselves – constantly!ConclusionThe skills that one requires to begin their journey in machine learning are exactly what we have discussed in this post. The future for machine learning is undoubtedly bright with companies ready to offer millions of dollars as remuneration, irrespective of the country and the location.Machine learning and deep learning will create a new set of hot jobs in the next five years. – Dave WatersAll it takes to have an amazing career in machine learning is a strong will to hone one’s skills and gain a solid knowledge of them. All the best for an amazing career in machine learning!
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Skills to Become Machine Learning Engineer

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