|Number of Pages||267 Pages|
● 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
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.