Machine Learning with Python

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

Book 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.

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


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 the full of this book.

TOC
Understanding Python
Feature Engineering
Data Visualisation
Basic and Advance Regression techniques
Classification
Un Supervised Learning
Text Analysis
Neural Network and Deep Learning
Recommendation System
Time Series Analysis