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Introduction to the Machine Learning Stack

What is Machine LearningArthur Samuel coined the term Machine Learning or ML in 1959. Machine learning is the branch of Artificial Intelligence that allows computers to think and make decisions without explicit instructions.  At a high level, ML is the process of teaching a system to learn, think, and take actions like humans.  Machine Learning helps develop a system that analyses the data with minimal intervention of humans or external sources.  ML uses algorithms to analyse and filter search inputs and correspondingly displays the desirable outputs. Machine Learning implementation can be classified into three parts: Supervised Learning Unsupervised Learning Reinforcement Learning What is Stacking in Machine Learning? Stacking in generalised form can be represented as an aggregation of the Machine Learning Algorithm. Stacking Machine Learning provides you with the advantage of combining the meta-learning algorithm with training your dataset, combining them to predict multiple Machine Learning algorithms and machine learning models. Stacking helps you harness the capabilities of a number of well-established models that perform regression and classification tasking.When it comes to stacking, it is classified into 4 different parts: Generalisation Scikit- Learn API Classification of Stacking Regression of Stacking A generalisation of Stacking: Generalisation is a composition of numerous Machine Learning models performed on a similar dataset, somewhat similar to Bagging and Boosting. Bagging: Used mainly to provide stability and accuracy, it reduces variance and avoids overfitting. Boosting: Used mainly to convert a weak learning algorithm to a strong learning algorithm and reduce bias and variance. Scikit-Learn API: This is among the most popular libraries and contains tools for machine learning and statistical modeling.The basic technique of Stacking in Machine Learning; Divide the training data into 2 disjoint sets. The level to which you train data depends on the base learner. Test base learner and make a prediction. Collect correct responses from the output. Machine Learning StackDive deeper into the Machine Learning engineering stack to have a proper understanding of how it is used and where it is used. Find out the below list of resources: CometML: Comet.ML is the machine learning platform dedicated to data scientists and researchers to help them seamlessly track the performance, modify code, and manage history, models, and databases.   It is somewhat similar to GitHub, which allows training models, tracks code changes, and graphs the dataset. Comet.ml can be easily integrated with other machine learning libraries to maintain the workflow and develop insights for your data. Comet.ml can work with GitHub and other git services, and a developer can merge the pull request easily with your GitHub repository. You can get help from the comet.ml official website regarding the documentation, download, installing, and cheat sheet. GitHub: GitHub is an internet hosting and version control system for software developers. Using Git business and open-source communities, both can host and manage their project, review their code and deploy their software. There are more than 31 million who actively deploy their software and projects on GitHub. The GitHub platform was created in 2007, and in 2020 GitHub made all the core features free to use for everyone. You can add your private repository and perform unlimited collaborations. You can get help from the GitHub official website, or you can learn the basics of GitHub from many websites like FreeCodeCamp or the GitHub documentation. Hadoop: Hadoop provides you with a facility to store data and run an application on a commodity hardware cluster. Hadoop is powered by Apache that can be described as a software library or a framework that enables you to process data or large datasets. Hadoop environment can be scaled from one to a thousand commodities providing computing power and local storage capacity. The benefit of the Hadoop SystemHigh computing power. High fault tolerance. More flexibility Low delivery cost Easily grown system (More scalability). More storage. Challenges faced in using Hadoop SystemMost of the problems require a unique solution. Processing speed is very slow. Need for high data security and safety. High data management and governance requirements.  Where Hadoop is usedData lake. Data Warehouse Low-cost storage and management Building the IoT system Hadoop framework can be classified intoHadoop yarn Hadoop Distributed File System Hadoop MapReduce Hadoop common Keras: Keras is an open-source library, which provides you with the open interface for Artificial Intelligence and Artificial Neural Network using Python. It helps in designing API for human convenience and follows best practices to reduce cost and move toward cognitive load maintenance. It acts as an interface between the TensorFlow library and dataset. Keras was released in 2015. It has a vast ecosystem which you can deploy anywhere. There are many facilities provided by Keras which you can easily access with your requirements. CERN uses Keras, NASA, NIH, LHC, and other scientific organisations to implement their research ideas, offer the best services to their client, and develop a high-quality environment with maximum speed and convenience.  Keras has always focused on user experience offering a simple APIs environment. Keras has abundant documentation and developer guides which are also open-source, which anyone in need can refer to. Luigi: This is a Python module that supports building batch jobs with the background of complex pipelining. Luigi is internally used by Spotify, and helps to run thousands of tasks daily, that are organised in the form of the complex dependency graph. Luigi uses the Hadoop task as a prelim job for the system. Luigi being open-source has no restrictions on its usage by users. The concept of Luigi is based on a unique contribution where there are thousands of open-source contributions or enterprises. Companies using LuigiSpotify. Weebly Deloitte Okko Movio Hopper Mekar M3 Assist Digital Luigi supports cascading Hive and Pig tools to manage the low level of data processing and bind them together in the big chain together. It takes care of workflow management and task dependency.Pandas: If you want to become a Data Scientist, then you must be aware of Pandas--a favourite tool with Data Scientists, and the backbone of many high-profile big data projects. Pandas are needed to clean, analyse, and transform the data according to the project's need. Pandas is a fast and open-source environment for data analysis and managing tools. Pandas is created at the top of the Python language. The latest version of Pandas is Pandas 1.2.3. When you are working with Pandas in your project, you must be aware of these scenariosWant to open the local file? It uses CSV, Excel, or delimited file. Want to open a remote store database? Convert list, dictionary, or NumPy using Pandas. Pandas provide an open-source environment and documentation where you can raise your concern, and they will identify the solution to your problem. PyTorch: PyTorch is developed in Python, which is the successor of the python torch library. PyTorch is also an open-source Machine learning Library; the main use of PyTorch is found in computer vision, NLP, and ML-related fields. It is released under the BSD license. Facebook and Convolutional Architecture operate PyTorch for Fast Feature Embedding (CAFFE2). Other major players are working with it like Twitter, Salesforce, and oxford. PyTorch has emerged as a replacement for NumPy, as it is faster than NumPy in performing the mathematical operations, array operations and provides the most suitable platform. PyTorch provides a more pythonic framework in comparison to TensorFlow. PyTorch follows a straightforward procedure and provides a pre-prepared model to perform a user-defined function. There is a lot of documentation you can refer to at their official site. Modules of PyTorchAutograd Module Optim module In module Key FeaturesMake your project production-ready. Optimised performance. Robust Ecosystem. Cloud support. Spark: Spark or Apache Spark is a project from Apache. It is an open-source, distributed, and general-purpose processing engine. It provides large-scale data processing for big data or large datasets. Spark provides you support for many backgrounds like Java, Python, R, or SQL, and many other technologies. The benefits of Spark includeHigh Speed. High performance. Easy to use UI. Large and complex libraries. Leverage data to a variety of sourcesAmazon S3. Cassandra. Hadoop Distributed File System. OpenStack. APIs Spark containsJava Python Scala Spark SQL R Scikit- learn: Scikit-Learn also known as sklearn, is a free and open-source software Machine Learning Library for Python. Scikit-Learn is the result of a Google summer Code project by David Cournapeau. Scikit-Learn makes use of NumPy for an operation like array operation, algebra, and high performance. The latest version of Scikit-Learn was deployed in Jan 2021, Version of Scikit-Learn 0.24. The benefits of Scikit-Learn includeIt provides simple and efficient tools. Easily assignable and reusable tool. Built on the top of NumPy, scipy, and matplotlib. Scikit-Learn is used inDimensionality reduction. Clustering Regression Classification Pre-processing Model selection and extraction. TensorFlow: TensorFlow is an open-source end-to-end software library used for numerical computation. It does graph-based computations quickly and efficiently leveraging the GPU (Graphics Processing Unit), making it seamless to distribute the work across multiple GPUs and computers. TensorFlow can be used across a range of projects with a particular concentration on the training dataset and Neural network. The benefits of TensorFlowRobust ML model. Easy model building. Provide powerful experiments for research and development. Provide an easy mathematical model. Why StackingStacking provides many benefits over other technologies. It is simple. More scalable. More flexible. More Space Less cost Most machine learning stacks are open source. Provides virtual chassis capability. Aggregation switching. How does stacking work? If you are working in Python, you must be aware of the K-folds clustering or k-mean clustering, and we perform stacking using the k fold method. Divide the dataset into k-folds very similar to the k-cross-validation method. If the model fits in k-1 parts, then the prediction is made for the kth part. Perform the same function for each part of the training data. The base model is fitted into the dataset, and then complete performance is calculated. Prediction from the training set used for the second level prediction. The next level makes predictions for the test dataset. Blending is a subtype of stacking. Installation of libraries on the systemInstalling libraries in Python is an easy task; you just require some pre-requisites. Ensure you can run your Python command using the Command-line interface. Use - python –version on your command line to check if Python is installed in your system. Try to run the pip command in your command-line interface. Python -m pip - - version Check for your pip, setup tools, and wheels recent update. Python -m pip install - - upgrade pip setuptools wheel Create a virtual environment. Use pip for installing libraries and packages into your system. ConclusionTo understand the basics of data science, machine learning, data analytics, and artificial intelligence, you must be aware of machine learning stacking, which helps store and manage the data and large datasets. There is a list of open-source models and platforms where you can find the complete documentation about the machine learning stacking and required tools. This machine learning toolbox is robust and reliable. Stacking uses the meta-learning model to develop the data and store them in the required model. Stacking has the capabilities to harness and perform classification, regression, and prediction on the provided dataset. It helps to constitute regression and classification predictive modelling. The model has been classified into two models, level 0, known as the base model, and the other model-level 1, known as a meta-model.

Introduction to the Machine Learning Stack

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Introduction to the Machine Learning Stack

What is Machine Learning

Arthur Samuel coined the term Machine Learning or ML in 1959. Machine learning is the branch of Artificial Intelligence that allows computers to think and make decisions without explicit instructions.  At a high level, ML is the process of teaching a system to learn, think, and take actions like humans.  

Machine Learning helps develop a system that analyses the data with minimal intervention of humans or external sources.  ML uses algorithms to analyse and filter search inputs and correspondingly displays the desirable outputs. 

Machine Learning implementation can be classified into three parts: 

  • Supervised Learning 
  • Unsupervised Learning 
  • Reinforcement Learning 

What is Stacking in Machine Learning? 

Stacking in generalised form can be represented as an aggregation of the Machine Learning Algorithm. Stacking Machine Learning provides you with the advantage of combining the meta-learning algorithm with training your dataset, combining them to predict multiple Machine Learning algorithms and machine learning models. 

Stacking helps you harness the capabilities of a number of well-established models that perform regression and classification tasking.

When it comes to stacking, it is classified into 4 different parts: 

  • Generalisation 
  • Scikit- Learn API 
  • Classification of Stacking 
  • Regression of Stacking 

A generalisation of Stacking: Generalisation is a composition of numerous Machine Learning models performed on a similar dataset, somewhat similar to Bagging and Boosting. 

  • Bagging: Used mainly to provide stability and accuracy, it reduces variance and avoids overfitting. 
  • Boosting: Used mainly to convert a weak learning algorithm to a strong learning algorithm and reduce bias and variance. 
  • Scikit-Learn API: This is among the most popular libraries and contains tools for machine learning and statistical modeling.

Introduction to the Machine Learning Stack

The basic technique of Stacking in Machine Learning; 

  • Divide the training data into 2 disjoint sets. 
  • The level to which you train data depends on the base learner. 
  • Test base learner and make a prediction. 
  • Collect correct responses from the output. 

Machine Learning Stack

Dive deeper into the Machine Learning engineering stack to have a proper understanding of how it is used and where it is used. Find out the below list of resources: 

  1. CometML: Comet.ML is the machine learning platform dedicated to data scientists and researchers to help them seamlessly track the performance, modify code, and manage history, models, and databases.   
  2. It is somewhat similar to GitHub, which allows training models, tracks code changes, and graphs the dataset. Comet.ml can be easily integrated with other machine learning libraries to maintain the workflow and develop insights for your data. Comet.ml can work with GitHub and other git services, and a developer can merge the pull request easily with your GitHub repository. You can get help from the comet.ml official website regarding the documentation, download, installing, and cheat sheet. 
  3. GitHub: GitHub is an internet hosting and version control system for software developers. Using Git business and open-source communities, both can host and manage their project, review their code and deploy their software. There are more than 31 million who actively deploy their software and projects on GitHub. The GitHub platform was created in 2007, and in 2020 GitHub made all the core features free to use for everyone. You can add your private repository and perform unlimited collaborations. You can get help from the GitHub official website, or you can learn the basics of GitHub from many websites like FreeCodeCamp or the GitHub documentation. 
  4. Hadoop: Hadoop provides you with a facility to store data and run an application on a commodity hardware cluster. Hadoop is powered by Apache that can be described as a software library or a framework that enables you to process data or large datasets. Hadoop environment can be scaled from one to a thousand commodities providing computing power and local storage capacity. 

The benefit of the Hadoop System

  • High computing power. 
  • High fault tolerance. 
  • More flexibility 
  • Low delivery cost 
  • Easily grown system (More scalability). 
  • More storage. 

Challenges faced in using Hadoop System

  • Most of the problems require a unique solution. 
  • Processing speed is very slow. 
  • Need for high data security and safety. 
  • High data management and governance requirements.  

Where Hadoop is used

  • Data lake. 
  • Data Warehouse 
  • Low-cost storage and management 
  • Building the IoT system 

Hadoop framework can be classified into

  • Hadoop yarn 
  • Hadoop Distributed File System 
  • Hadoop MapReduce 
  • Hadoop common 
  1. Keras: Keras is an open-source library, which provides you with the open interface for Artificial Intelligence and Artificial Neural Network using Python. It helps in designing API for human convenience and follows best practices to reduce cost and move toward cognitive load maintenance. 

It acts as an interface between the TensorFlow library and dataset. Keras was released in 2015. It has a vast ecosystem which you can deploy anywhere. There are many facilities provided by Keras which you can easily access with your requirements. 

CERN uses Keras, NASA, NIH, LHC, and other scientific organisations to implement their research ideas, offer the best services to their client, and develop a high-quality environment with maximum speed and convenience. 

Keras has always focused on user experience offering a simple APIs environment. Keras has abundant documentation and developer guides which are also open-source, which anyone in need can refer to. 

  1. Luigi: This is a Python module that supports building batch jobs with the background of complex pipelining. Luigi is internally used by Spotify, and helps to run thousands of tasks daily, that are organised in the form of the complex dependency graph. Luigi uses the Hadoop task as a prelim job for the system. Luigi being open-source has no restrictions on its usage by users. 

The concept of Luigi is based on a unique contribution where there are thousands of open-source contributions or enterprises. 

Companies using Luigi

  • Spotify. 
  • Weebly 
  • Deloitte 
  • Okko 
  • Movio 
  • Hopper 
  • Mekar 
  • M3 
  • Assist Digital 

Luigi supports cascading Hive and Pig tools to manage the low level of data processing and bind them together in the big chain together. It takes care of workflow management and task dependency.

  1. Pandas: If you want to become a Data Scientist, then you must be aware of Pandas--a favourite tool with Data Scientists, and the backbone of many high-profile big data projects. Pandas are needed to clean, analyse, and transform the data according to the project's need. 

Pandas is a fast and open-source environment for data analysis and managing tools. Pandas is created at the top of the Python language. The latest version of Pandas is Pandas 1.2.3. 

When you are working with Pandas in your project, you must be aware of these scenarios

  • Want to open the local file? It uses CSV, Excel, or delimited file. 
  • Want to open a remote store databaseConvert list, dictionary, or NumPy using Pandas. 

Pandas provide an open-source environment and documentation where you can raise your concern, and they will identify the solution to your problem. 

  1. PyTorch: PyTorch is developed in Python, which is the successor of the python torch library. PyTorch is also an open-source Machine learning Library; the main use of PyTorch is found in computer vision, NLP, and ML-related fields. It is released under the BSD license. 

Facebook and Convolutional Architecture operate PyTorch for Fast Feature Embedding (CAFFE2). Other major players are working with it like Twitter, Salesforce, and oxford. 

PyTorch has emerged as a replacement for NumPy, as it is faster than NumPy in performing the mathematical operations, array operations and provides the most suitable platform. 

PyTorch provides a more pythonic framework in comparison to TensorFlow. PyTorch follows a straightforward procedure and provides a pre-prepared model to perform a user-defined function. There is a lot of documentation you can refer to at their official site. 

Modules of PyTorch

  • Autograd Module 
  • Optim module 
  • In module 

Key Features

  • Make your project production-ready. 
  • Optimised performance. 
  • Robust Ecosystem. 
  • Cloud support. 
  1. Spark: Spark or Apache Spark is a project from Apache. It is an open-source, distributed, and general-purpose processing engine. It provides large-scale data processing for big data or large datasets. Spark provides you support for many backgrounds like Java, Python, R, or SQL, and many other technologies. 

The benefits of Spark include

  • High Speed. 
  • High performance. 
  • Easy to use UI. 
  • Large and complex libraries. 

Leverage data to a variety of sources

  • Amazon S3. 
  • Cassandra. 
  • Hadoop Distributed File System. 
  • OpenStack. 

APIs Spark contains

  • Java 
  • Python 
  • Scala 
  • Spark SQL 
  • R 
  1. Scikit- learn: Scikit-Learn also known as sklearn, is a free and open-source software Machine Learning Library for Python. Scikit-Learn is the result of a Google summer Code project by David Cournapeau. Scikit-Learn makes use of NumPy for an operation like array operation, algebra, and high performance. 

The latest version of Scikit-Learn was deployed in Jan 2021, Version of Scikit-Learn 0.24. 

The benefits of Scikit-Learn include

  • It provides simple and efficient tools. 
  • Easily assignable and reusable tool. 
  • Built on the top of NumPy, scipy, and matplotlib. 

Scikit-Learn is used in

  • Dimensionality reduction. 
  • Clustering 
  • Regression 
  • Classification 
  • Pre-processing 
  • Model selection and extraction. 
  1. TensorFlow: TensorFlow is an open-source end-to-end software library used for numerical computation. It does graph-based computations quickly and efficiently leveraging the GPU (Graphics Processing Unit), making it seamless to distribute the work across multiple GPUs and computers. TensorFlow can be used across a range of projects with a particular concentration on the training dataset and Neural network. 

The benefits of TensorFlow

  • Robust ML model. 
  • Easy model building. 
  • Provide powerful experiments for research and development. 
  • Provide an easy mathematical model. 

Why Stacking

Stacking provides many benefits over other technologies. 

  • It is simple. 
  • More scalable. 
  • More flexible. 
  • More Space 
  • Less cost 
  • Most machine learning stacks are open source. 
  • Provides virtual chassis capability. 
  • Aggregation switching. 

How does stacking work? 

If you are working in Python, you must be aware of the K-folds clustering or k-mean clustering, and we perform stacking using the k fold method. 

  • Divide the dataset into k-folds very similar to the k-cross-validation method. 
  • If the model fits in k-1 parts, then the prediction is made for the kth part. 
  • Perform the same function for each part of the training data. 
  • The base model is fitted into the dataset, and then complete performance is calculated. 
  • Prediction from the training set used for the second level prediction. 
  • The next level makes predictions for the test dataset. 

Blending is a subtype of stacking. 

Installation of libraries on the system

Installing libraries in Python is an easy task; you just require some pre-requisites. 

  • Ensure you can run your Python command using the Command-line interface. 
    • Use - python –version on your command line to check if Python is installed in your system. 
  • Try to run the pip command in your command-line interface. 
    • Python -m pip - - version 
  • Check for your pip, setup tools, and wheels recent update. 
    • Python -m pip install - - upgrade pip setuptools wheel 
  • Create a virtual environment. 

Use pip for installing libraries and packages into your system. 

Conclusion

To understand the basics of data science, machine learning, data analytics, and artificial intelligence, you must be aware of machine learning stacking, which helps store and manage the data and large datasets. 

There is a list of open-source models and platforms where you can find the complete documentation about the machine learning stacking and required tools. This machine learning toolbox is robust and reliable. Stacking uses the meta-learning model to develop the data and store them in the required model. 

Stacking has the capabilities to harness and perform classification, regression, and prediction on the provided dataset. It helps to constitute regression and classification predictive modelling. The model has been classified into two models, level 0, known as the base model, and the other model-level 1, known as a meta-model.

Abhresh

Abhresh Sugandhi

Author

Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.

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A candidate earns the Professional certificate from Coursera and a badge from IBM that recognises a candidate's proficiency in the area. Prerequisites: It is the optimal course for freshers since it requires no requisite programming knowledge or proficiency in Analytics. Exam Fee: It costs $39 per month (Source) to access the course materials and the certificate. The course is handled by the Coursera organisation. Expected Salary: This certification can earn you the title of IBM Data Scientist and help you earn a salary of $134,846 per annum. (Source) 6. Microsoft Certified Azure Data Scientist AssociateIt's one of the most well-known certifications for newcomers to step into the field of Big Data and Data analytics. This credential is offered by the leader in the industry, Microsoft Azure. This credential validates a candidate's ability to work with Microsoft Azure developing environment and proficiency in analysing big data, preparing data for the modelling process, and then progressing to designing models. One advantage of this credential is that it has no expiry date and does not need renewal; it also authorises the candidate’s extensive knowledge in predictive Analytics. Prerequisites: knowledge and experience in data science and using Azure Machine Learning and Azure Databricks. Exam Fee: It costs $165 to (Source) register for the exam. One advantage is that there is no need to attend proxy institutions to prepare for this exam, as Microsoft offers free training materials as well as an instructor-led course that is paid. There is a comprehensive collection of resources available to a candidate. Expected Salary: The job title typically offered is Microsoft Data Scientist and it typically fetches a yearly pay of $130,993.(Source) Why be a Data Analytics professional? For those already working in the field of data, being a Data Analyst is one of the most viable options. The salary of a data analyst ranges from $65,000 to $85,000 depending on number of years of experience. This lucrative salary makes it worth the investment to get a certification and advance your skills to the next level so that you can work for multinational companies by interpreting and organising data and using this analysis to accelerate businesses. These certificates demonstrate that you have the required knowledge needed to operate data models of the volumes needed by big organizations. 1. Demand is more than supply With the advent of the Information Age, there has been a huge boom in companies that either entirely or partially deal with IT. For many companies IT forms the core of their business. Every business has to deal with data, and it is crucial to get accurate insights from this data and use it to further business interests and expand profits. The interpretation of data also aims to guide them in the future to make the best business decisions.  Complex business intelligence algorithms are in place these days. They need trained professionals to operate them; since this field is relatively new, there is a shortage of experts. Thus, there are vacancies for data analyst positions with lucrative pay if one is qualified enough.2. Good pay with benefitsA data analyst is an extremely lucrative profession, with an average base pay of $71,909 (Source), employee benefits, a good work-home balance, and other perks. It has been consistently rated as being among the hottest careers of the decade and allows professionals to have a long and satisfying career.   Companies Hiring Certified Data Analytics Professionals Oracle A California based brand, Oracle is a software company that is most famous for its data solutions. With over 130000 employees and a revenue of 39 billion, it is surely one of the bigger players in Data Analytics.  MicroStrategy   Unlike its name, this company is anything but micro, with more than 400 million worth of revenue. It provides a suite of analytical products along with business mobility solutions. It is a key player in the mobile space, working natively with Android and iOS.   SAS   One of the companies in the list which provides certifications and is also without a doubt one of the largest names in the field of Big Data, machine learning and Data Analytics, is SAS. The name SAS is derived from Statistical Analysis System. This company is trusted and has a solid reputation. It is also behind the SAS Institute for Data Science. Hence, SAS is the organisation you would want to go to if you're aiming for a long-term career in data science.    Conclusion To conclude, big data and data Analytics are a field of endless opportunities. By investing in the right credential, one can pave the way to a viable and lucrative career path. Beware though, there are lots of companies that provide certifications, but only recognised and reputed credentials will give you the opportunities you are seeking. Hiring companies look for these certifications as a mark of authenticity of your hands-on experience and the amount of work you can handle effectively. Therefore, the credential you choose for yourself plays a vital role in the career you can have in the field of Data analytics.  Happy learning!    
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Top Data Analytics Certifications

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Why Should You Start a Career in Machine Learning?

If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it.  Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques.  We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses.  This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation.  Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment.  The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of $112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch $20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising.  A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer.  Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

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Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course. 
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Types of Probability Distributions Every Data Scie...

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