English | 19 Apr. 2018 | ISBN: 9386551934 | 266 Pages | PDF
This book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the 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. This book: Covers all major areas in Machine Learning. Topics are discussed with graphical explanations. Comparison of different Machine Learning methods to solve any problem. Methods to handle real-world noisy data before applying any Machine Learning algorithm. Python code example for each concept discussed. Jupyter notebook scripts are provided with dataset used to test and try the algorithms Contents Introduction to Machine Learning Understanding Python Feature Engineering Data Visualisation Basic and Advanced Regression techniques Classification Un Supervised Learning Text Analysis Neural Network and Deep Learning Recommendation System Time Series Analysis