A Practical Approach for Machine Learning and Deep Learning Algorithms

Guide covering topics from machine learning, regression models, neural network to tensor flow DESCRIPTION Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research.

Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing.

In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part.

Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it.

KEY FEATURES Machine learning in MATLAB using basic concepts and algorithms.

Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data.

Machine learning workflow for health monitoring.

The neural network domain and implementation in MATLAB with explicit explanation of code and results.

How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula.

Machine learning workflow for health monitoring.

WHAT WILL YOU LEARN Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning WHO THIS BOOK IS FOR The book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement.

We have touched all basic algorithms of machine learning in detail with a practical approach.

Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time.

Table of Contents 1.

Pre-requisite to Machine Learning 2.

An introduction to Machine Learning 3.

Finding Natural Patterns in Data 4.

Building Classification Methods 5.

Data Pre-Processing in Python 6.

Building Regression Models 7.

Creating Neural Networks 8.

Introduction to Deep Learning.