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A Complete Data Analytics Guide for Learners and Professionals. Key Features -Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. -Dive into Machine Learning, its tools, and applications. -Coverage of applications of Big Data, Data Analysis, and Business Intelligence. Description These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book “Data Analytics: Principles, Tools, and Practices” can be considered a handbook or a guide for professionals who want to start their journey in the field of data science. The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples. After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science. What you will learn -Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. -Learn to manage data warehousing with real time transaction processing. -Explore various machine learning techniques that apply to data analytics. -Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry. -Acquaint yourself with Big Data tools and statistical techniques for machine learning. Who this book is for IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book. Table of Contents 1. Database Management System 2. Online Transaction Processing and Data Warehouse 3. Business Intelligence and its deeper dynamics 4. Introduction to Data Visualization 5. Advanced Data Visualization 6. Introduction to Big Data and Hadoop 7. Application of Big Data Real Use Cases 8. Application of Big Data 9. Introduction to Machine Learning 10. Advanced Concepts to Machine Learning 11. Application of Machine Learning
Get answers to frequently asked questions on Data Science and Machine Learning using R Key Features Understand the capabilities of the R programming language Most of the machine learning algorithms and their R implementation covered in depth Answers on conceptual data science concepts are also covered Description This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language. The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job. By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. What will you learn Get answers to the basics, intermediate and advanced questions on R programming Understand the transformation and filtering capabilities of R Know how to perform visualization using R Who this book is for This book is a must for anyone interested in Data Science and Machine Learning. Anyone who wants to clear the interview can use it as a last-minute revision guide. Table of Contents 1. Data Science basic questions and terms 2. R programming questions 3. GGPLOT Questions 4. Statistics with excel sheet About the Author Vishwanathan Narayanan has 18 years of experience in the field of information technology and data analysis. He made many enterprise-level applications with stable output and scalability. Advanced level data analysis for complex problems using both R and Python has been the key area of work for many years. Extreme programmer on Java, Python, R, and many more technologies
Learn how to process and analysis data using Python Key Features The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Author Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of ‘Social Network Analysis and Mining’. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals.
Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key Features Understand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work. Learn how to implement Decision Trees and Random Forests. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling. Description This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. What will you learn Learn how to prepare Data for Machine Learning. Learn how to implement learning algorithms from scratch. Use scikit-learn to implement algorithms. Use various Feature Selection and Feature Extraction methods. Learn how to develop a Face recognition system. Who this book is for The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular. Table of Contents 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography About the Author Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development. He has authored a few books including Programming in C#, Oxford University Press, Algorithms, Oxford University Press, Python Basics, Mercury, Python for Beginners, New Age International. Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship. Outside work, he is deeply interested in Hindi Poetry, progressive era, Hindustani Classical Music, percussion instruments. His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning.
Learn, develop, test and document powerful yet simple RAML API specifications using MuleSoft API Designer and API Toolkit. Key Features Explore concept of API and its significance in enterprise applications Design your own API using Mulesoft Anypoint Platform Exciting coverage on how API works in Enterprise Applications Live demonstration on how to build and integrate API with end-to-end implementation and working code Description Hands-on MuleSoft Anypoint platform book directs you step-by-step in designing API, its Implementation, and how to integrate smartly with other applications. This book is enriched with lots of interactive screenshots and working source codes. Throughout this book, you will learn key industry insights on System Integration, API Led Connectivity, Centre for Enablement, and RAML. This book will talk about how to use publicly available free mock REST APIs and how to call and test them from RESTful clients like Postman. You can also see some of the commercially available license-based APIs. Equipped with exercises, you will practice developing your own RESTful API specification along with how to add, retrieve, update, and delete data for your business use. You will be using the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. At the end of the book, you will be summarizing your learnings with an end-to-end implementation demonstration on the API design and its implementation. What you will learn Know-how of public APIs, commercial APIs, and cloud-based SaaS APIs Role of Mulesoft in SaaS applications You learn to design and test the API development and implementation You get handy with all the features and mechanism of Mulesoft Anypoint Platform Who this book is for This book is for fresher, IT employees with less or no programming background such as Business Analysts, Quality Engineers, HR, Technical persons who are looking for a change in technology area if they are working in outdated technologies. Table of Contents 1. MuleSoft Fundamentals 2. MuleSoft Internals 3. MuleSoft Salient features 4. From ESB to API Led Connectivity 5. Cloud based SaaS Applications and MuleSoft Connectors 6. REST, SOAP, Postman and Anypoint Studio 7. Start RAML 8. RAML in detail 9. RAML Project About the Authors Nanda Nachimuthu is an Engineering graduate from Tamilnadu Agricultural University, Coimbatore, and Tamilnadu and has done Advanced Diploma from Indian Institute of Technology, Kharagpur in the field of Java and Internet Computing. He has also completed an Advanced Diploma from Indian Institute of International Trading, Delhi which specializes on Strategies for International Business. His 25 years of experience comes from various domains like Banking, Healthcare, Government and Airlines. He is, in to the technologies like Java, Big Data, Cloud, ESB, Security and IoT. He played various roles like Technical Architect, Solutions Architect, Cloud Architect and Enterprise Integration Architect and wanted to be in an Individual Contributor role always with hands-on coding experience. He is passionate about Social Entrepreneurship and Pro Bono consultations in multiple fields like Information Technology, Manufacturing, Trading, Agriculture and Internet of Things. He is the founder of some social platforms, and he owns few trademarks under his kitty. Presently he is focusing on Integration Technology Platforms like MuleSoft, where he finds lots of scope in the future for Digital Marketing and Machine to Machine Communications. LinkedIn Profile: https://www.linkedin.com/in/nanda3008/ Github: https://github.com/nanda3008
Hands-on MuleSoft Flows using MuleSoft Anypoint Studio Components and understanding Payload processing along with debugging. Key Features Get familiar with the MuleSoft Anypoint Studio key techniques such as Payload, Logger, Variables, Flow and Flow Reference. Deep dive into Massage Structure and Payload value handling. Get familiar with the Global Configuration Properties and Securing properties. Explore Mule Run Time and Deploying Mule Projects in CloudHub. Description This book is aimed to teach the readers how to design RAML APIs using Anypoint Platform. It also focuses on popular topics such as System Integration, API Led Connectivity, and Centre for Enablement and RAML. It will show how to use, call and test free mock REST APIs. The readers can also work with some commercially available license-based APIs. Furthermore, the book will explain most of the examples provided by RAML.org so that you can simulate it from your local system. This book will then help you develop your RESTful API specification for adding, retrieving, updating and deleting data for a business entity. Later, you will learn how to use the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. By the end, you will be able to develop an end to end RAML API using the MuleSoft Anypoint Studio. What you will learn Get exposed to Payload handling, logging and variables Work with different Flow Control components such as Choice, First Successful, Round-Robin and Scatter Gather Explore and work with Error Handling components such as Error Handler, On Error, Continue, On Error Propagate and Raise Error Understand Global Configuration Properties and Securing properties Gain knowledge about various scopes involved in MuleSoft Flow designing Who this book is for This book is meant for anyone interested to become an API designer. Experienced technical persons of the IT industry also can utilize the book to get extra insights, and they can align their knowledge in line with it. Table of Contents 1. Start Project 2. Anypoint Studio Components 3. Flow Control Components 4. Idempotent, Parse Template and Scheduler 5. Payload Component 6. MUnit 7. MuleSoft Runtime 8. Global Secured Configurations 9. Error Handling 10. RAML and Anypoint Studio About the Authors Nanda Nachimuthu is an Engineering graduate from Tamil Nadu Agricultural University, Coimbatore, and has completed Advanced Diploma from the Indian Institute of Technology, Kharagpur, in the field of Java and Internet Computing. He has also completed an Advanced Diploma from Indian Institute of International Trading, Delhi, which specializes in Strategies for International Business. He has 25 years of experience in various domains like banking, healthcare, government, and airlines. He’s into technologies like Java, Big Data, Cloud, ESB, Security and IoT. He has taken up various roles like technical architect, solutions architect, cloud architect, and enterprise integration architect and always wanted to take up an individual contributor’s role with hands-on coding experience. He is passionate about social entrepreneurship and takes pro-bono consultations in multiple fields like information technology, manufacturing, trading, agriculture, and internet of things. The founder of some social platforms, he also has a few trademarks under his kitty. Presently, he is focusing on integration technology platforms like MuleSoft, where he finds a wide scope in the future for digital marketing and machine to machine communication. Github Link: github.com/nanda3008 LinkedIn Profile: https://www.linkedin.com/in/nanda3008/
Hands-On ML problem solving and creating solutions using Python. Key Features Introduction to Python Programming Python for Machine Learning Introduction to Machine Learning Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms Linear Regression, Logistic Regression and Support Vector Machines Description You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them. We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters. What You Will Learn Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning. Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries. Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you. Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation. Get to know the basics of Deep Learning and some interesting algorithms in this space. Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model Who this book is for This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. Table of Contents 1. Introduction to Python Programming 2. Python for Machine Learning 3. Introduction to Machine Learning 4. Supervised Learning and Unsupervised Learning 5. Linear Regression: A Hands-on guide 6. Logistic Regression – An Introduction 7. A sneak peek into the working of Support Vector machines(SVM) 8. Decision Trees 9. Random Forests 10. Time Series models in Machine Learning 11. Introduction to Neural Networks 12. Recurrent Neural Networks 13. Convolutional Neural Networks 14. Performance Metrics 15. Introduction to Design Thinking 16. Design Thinking Case Study About the Author Gnana Lakshmi T C —iis Technology Geek, Innovator, Keynote speaker, Community builder and holds a Bachelor degree in Computer Science from National Institute of Technology, Tiruchirappalli. She is currently associated with WileyNXT as Product Manager, Emerging Technologies. She is also a Fellow Alumni at WomenWhoCode and started WomenWhoCode Blockchain community (www.womenwhocode.com/blockchain). She harnesses her knowledge by sharing it with others by conducting live events like webinars and workshops and through online channels like tutorials, social media posts etc. She has conducted several meetups on Machine learning, Blockchain and various other emerging technology topics including a recent meetup at the International Open UP Summit on GPT-3. LinkedIn Profile: https://www.linkedin.com/in/gyan-lakshmi Madeleine Shang —is a Recommender Systems Team Lead @OpenMined. She started the Data Science and Machine Learning community at WomenWhoCode which is now successfully running with 2147 members. She is an expert in AI and Blockchain Research. She has been involved in many startups as a Founder. She is an Adventurer and Futurist at heart. LinkedIn Profile: https://www.linkedin.com/in/madeleine-shang/
Guidance for successful installation of a wide range of IBM software products Key Features -Complete installation guide of IBM software systems, Redhat Enterprise, IBM Cloud, and Docker. -Expert-led demonstration on complete configuration and implementation of IBM software solutions. -Includes best practices and efficient techniques adopted by banks, financial services, and insurance companies. Description This book provides instructions for installation, configuration and troubleshooting sections to improve the IT support productivity and fast resolution of issues that arise. It covers readers' references that are available online and also step-by-step procedures required for a successful installation of a broad range of IBM Data Analytics products. This book provides a holistic in-depth knowledge for students, software architects, installation specialists, and developers of Data Analysis software and a handbook for data analysts who want a single source of information on IBM Data Analysis Software products. This book provides a single resource that covers the latest available IBM Data Analysis software on the most recent RedHat Linux and IBM Cloud platforms. This book includes comprehensive technical guidance, enabling IT professionals to gain an in-depth knowledge of the installation of a broad range of IBM Software products across different operating systems. What you will learn -Step-by-step installation and configuration of IBM Watson Analytics. -Managing RedHat Enterprise Systems and IBM Cloud Platforms. -Installing, configuring, and managing IBM StoredIQ. -Best practices to administer and maintain IBM software packages. -Upgrading VMware stations and installing Docker. Who this book is for This book is a go-to guide for IT professionals who are primarily Solution Architects, Implementation Experts, or Technology Consultants of IBM Software suites. This will also be a useful guide for IT managers who are looking to adopt and enable their enterprise with IBM products. Table of Contents 1. Getting Started with IBM Resources for Analytics 2. IBM Component Software Compatibility Matrix 3. IBM Download Procedures 4. On-Premise Server Configurations and Prerequisites 5. IBM Fix Packs 6. IBM Cloud PAK Systems 7. RedHat OpenShift 4.x Installations 8. IBM Cloud Private System 9. Base VMWare System Platform 10. IBM Cloud Private Cluster on CentOS 8.0 11. UIMA Pipeline and Java Code Extensions 12. IBM Watson Explorer Foundational Components V12 13. IBM Watson Explorer oneWEX 12.0.3 14. IBM StoredIQ for Legal       APPENDIX References and End of Life Support About the Authors Alan Bluck, has over 45 years of IT experience. He has been a Solutions Architect for IBM for over 10 years. He is now the Director and owner of ASB Software Development Limited, an IBM PartnerWorld partner and a consultancy providing systems architecture for a broad range of services. He is a member of the British Computer Society (MBCS, CITP).
A Cookbook that will help you implement Machine Learning algorithms and techniques by building real-world projects Key Features Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning. Description Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. What will you learn Understand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. Who this book is for This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners. Table of Contents 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI About the Author Rehan Guha —A Researcher by the day and an Artist by night. Our Author is a Scholar -lecturer, an Innovator, and also a Humanitarian -Philanthropist. He started his life as a Coder, Developer, and now he is into research in the field of Machine Learning and Algorithms but also has a keen interest in General Science, Technology, Invention & Innovation. The author holds a graduation degree from the Institute of Engineering & Management, Kolkata, and a Postgraduate certification on Deep Learning from the Indian Institute of Technology, Kharagpur (IIT-K)-AICTE approved FDP course. If we talk about Rehan's area of interest, it lies in Optimization Problems, Explainable AI, Deep Learning Architecture, Algorithms, Complexity, Algorithmic Thinking, et cetera… He has multiple publications through Journals and Open Publications, along with his publications he has filed multiple patents for his Innovations and Inventions. At an early age, one of his patents was also demonstrated to the Indian Army. In Rehan’s career, he has been involved with a variety of Business Verticals, starting from Banking, Consulting, Law, Insurance, Freight & Logistics, and Telcom.
Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key FeaturesUnderstand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work.Learn how to implement Decision Trees and Random Forests. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.Description This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. What will you learnLearn how to prepare Data for Machine Learning.Learn how to implement learning algorithms from scratch.Use scikit-learn to implement algorithms.Use various Feature Selection and Feature Extraction methods.Learn how to develop a Face recognition system. Who this book is for The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.Table of Contents 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography About the Author Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development. Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship. Outside work, he is deeply interested in Hindi Poetry, progressive era, Hindustani Classical Music, percussion instruments. His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning. Your LinkedIn Profile: https://in.linkedin.com/in/harsh-bhasin-69134426