Best Machine Learning Books 2020 with Python | Ultimate Review

Machine learning provides computers with the ability to learn without being explicitly programmed. It is a subfield of computer science and a type of artificial intelligence (AI). The most popular language for machine learning is Python Programming Language. Because Python is an accessible, powerful and flexible language for machine learning.

But getting started and work in ML with Python is not that easy if you can not access the right books. Here in this article, we examined the best books for Python machine learning in 2020. Have a careful look at them and get the suitable ones for you!

Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning From Scratch)
Author: Oliver Theobald
Published at: 01/01/2018
ISBN: 1549617214

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



Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Author: Sebastian Raschka,Vahid Mirjalili
Published at: 12/12/2019
ISBN: 1789955750

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
Author: Raúl Garreta,Guillermo Moncecchi
Published at: 25/11/2013
ISBN: 1783281936

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 with Spark and Python: Essential Techniques for Predictive Analytics
Author: Michael Bowles
Published at: 05/11/2019
ISBN: 1119561930

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



Building Machine Learning Systems with Python
Author: Willi Richert,Luis Pedro Coelho
Published at: 26/07/2013
ISBN: B00E7NC9D2

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



Also you can check these books:
  • Introduction to Machine Learning with Python: A Guide for Data Scientists
  • Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications
  • Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
  • Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition)
  • Simple Machine Learning for Programmers: Beginner's Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applicat
  • Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
  • Machine Learning: Make Your Own Recommender System (Machine Learning for Beginners Book 2)
  • Python Machine Learning: A Guide For Beginners (2nd Edition)

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