Machine Learning with Python
eBook - ePub

Machine Learning with Python

Design and Develop Machine Learning and Deep Learning Technique using real world code examples

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Machine Learning with Python

Design and Develop Machine Learning and Deep Learning Technique using real world code examples

Book details
Table of contents
Citations

About This Book

Develop and Implement your own Machine Learning Models to solve real world problems Key Features

  • Introduction to Machine Learning, Python and Jupyter
  • Learn about Feature Engineering and Data Visualization using real world data sets
  • Learn various regression and classification techniques
  • Deep Learning and Neural network concepts and practical covered
  • Text Analysis, Recommendation engines and Time Series Analysis
  • Jupyter notebook scripts are provided with dataset used to test and try the algorithms

  • Description
    This book provides concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on real world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the data.In this book we provide code examples in python. Python is the most suitable and worldwide accepted language for this. First, it is free and open source. It contains very good support from open community. It contains a lot of library, so you don't need to code everything. Also, it is scalable for large amount of data and suitable for big data technologies. What You Will Learn
    Building machine learning model which is used in industries to solve data related problems. Who this book is for
    This book is helpful for all types of readers. Either you want to start in machine learning or want to learn the concepts more or practice with the code, it provides everything. We recommend users to learn the concept and practice it using sample code to get full of this book. Table of Contents
    1. Understanding Python
    2. Feature Engineering
    3. Data Visualisation
    4. Basic and Advance Regression techniques
    5. Classification
    6. Un Supervised Learning
    7. Text Analysis
    8. Neural Network and Deep Learning
    9. Recommendation System
    10. Time Series Analysis About the Author
    Abhishek Vijayvargia is a Data Scientist. He worked in IT industry and helped in solving real time problems related to data science and Machine Learning. He worked on analytics problems related to transportation, government process, manufacturing, oil and gas, IoT, pharmaceuticals, shipping. He has completed his Masters from Indian Institute of Technology(IIT), Kanpur in Artificial Intelligence. His research interests are distributed Machine Learning, Deep Learning, Stream Processing and Blockchain. He worked as mentor for various machine learning projects and trained others in Algorithm, Competitive Programming and Data Science. LinkedIn profile: https://www.linkedin.com/in/avijayvargia

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Yes, you can access Machine Learning with Python by Abhishek Vijayvargia in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
BPB Publications
Year
2018
ISBN
9789386551931
Topic
Computer Science
Subtopic
Artificial Intelligence (AI) & Semantics
Index
Computer Science

Table of contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Preface
  5. Acknowledgement
  6. Table of Contents
  7. Chapter 1: Introduction to Machine Learning
  8. Chapter 2: Understanding Python
  9. Chapter 3: Feature Engineering
  10. Chapter 4: Data Visualization
  11. Chapter 5: Regression
  12. Chapter 6: More on Regression
  13. Chapter 7: Classification
  14. Chapter 8: Un Supervised Learning
  15. Chapter 9: Text Analysis
  16. Chapter 10: Neural Network and Deep Learning
  17. Chapter 11: Recommendation System
  18. Chapter 12: Time Series Analysis