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

✍ Scribed by Abhishek Vijayvargia

• Publisher: BPB Publications
• Year: 2019
• Tongue: English
• Leaves: 490
• Series: Fundamentals of the technique
• Category: Library

✦ Synopsis

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 will you 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