Best machine learning books 2020 with Python | Ultimate Review

If you are an absolute beginner who knows a little or nothing about ML, this is the right book for getting the grip of ML with Python. It offers a comprehensive explanation of the core concepts of ML so that you can easily understand the aspect of those concepts and apply them to solve real-world problems. With visual examples, you will get an easy and clear understanding of every ML methods and techniques. 

What You Will Learn:

• Getting the data from various sources

• Cleaning data using various techniques

• Preparing data for using it in the model

• Performing regression analysis using various algorithms

• Working with clustering algorithms

• Understanding essential concepts of Neural Networks

• Improving your machine learning model with various techniques

If you want to find out the question of how to use Python to start and answering critical questions of your data, then this book, Python Machine Learning is exactly for you. If you want to get started from scratch or want to extend your data science knowledge, then this is an essential and valuable resource.

What You Will Learn

• Using simple ML algorithms to train your classification model

• Working ScikitLearn Library for regression and classification

• Understanding data preprocessing techniques

• Sentiment Analysis with ML

• Integrating ML Model into a Web Application

• Developing a model for cluster analysis with unlabeled data

• Building deep neural network models including ANN, CNN, and RNN using Tensorflow 

Learning scikit-learn: Machine Learning in Python book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques.

What You Will Learn

• Essential concepts and methods of ML with hands-on example

• Incorporating machine learning in your applications

• Evaluating your results and apply advanced techniques for preprocessing data

• Working with ScikitLearn inside your Python environment.

• Classifying objects from documents to human faces and flower species

• Using regression techniques for prediction

• Parallelization techniques for building better models

Machine learning doesn't have to be complex and highly specialized. This book shows you how Python makes this technology more accessible to a much wider audience, using methods that are simpler, more effective, and well tested without requiring an extensive background in math or statistics.

What You Will Learn

• Analyzing data to understand the very problem associated with it

• Applying two core ML algorithms for making a prediction in Python

• Various methods of building predictive models

• Evaluating the performance of each model to ensure that the right one is used

• Using linear and ensemble algorithm families

• Building predictive models to solve a range of simple and complex problems

• Using sample code directly to build custom solutions

Featuring a wealth of real-world examples, this book provides you with an accessible route into Python machine learning. You will learn the Iris dataset and find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity.

What You Will Learn

• Creating machine learning algorithms in Python 

• Working with scikitLearn, numpy, scipy, matplotlib, and other important Python libraries 

• Employing computer vision using methods for image processing 

• Uncovering patterns and trends in your data

• Topic modeling and building a topic model for Wikipedia

• Analyzing Twitter data using sentimental analysis

• Solving classification and regression problems with real-world examples