Machine Learning (ML) with Python [eBook]

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 practicals covered.
• Text Analysis, Recommendation Engines and Time Series Analysis.
• Jupyter Notebook scripts are provided with a dataset used to test and try the algorithms.

Description:

This book provides the concept of machine learning with mathematical explanations and programming examples. Every chapter starts with fundamentals of the technique and working examples on real-world datasets. 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 widely accepted language for this. First, it is free and open source. It contains very good support from the open community. It contains a lot of libraries, so you don’t need to code everything. Also, it is scalable for large amounts of data and suitable for big data technologies.

What will you learn?

Building a machine learning model that is used in industries to solve data-related problems.

Who is this book for?

This book is helpful for all types of readers. Whether 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 learn the concept and practice it using sample code to get the full benefit of this book.

Table of Contents:

• 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.