This book provides the concept of machine learning with mathematical explanations and programming examples. Every chapter starts with the 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 to 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 the open community. It contains a lot of libraries, so you don’t need to code everything. Also, it is scalable for a large amount of data and suitable for big data technologies.
- 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 the dataset used to test and try the algorithms