Best Machine Learning Books 2021 with Python | Ultimate Review

This Machine Learning With Python Tutorial is designed for software programmers and beginners who need to learn Python programming language from scratch. Python is chosen by the best in the world, companies like Google, Facebook, or Microsoft, and it’s growing very fast. Developers love its features.

In this Step by Step Tutorial you will:

• Learn Exactly How Phyton Works and why its functionalities are so advantageous compared with any other programming language
• Realize How Python is The Ideal Programming Language for Querying Data and Retrieving Valuable Insights to always be able to find what you are looking for in the easiest possible way.
• Have the Chance to Practice What You Learn thanks to the exercises you find inside this Manual so that you are always sure you are doing the right thing in the right way.
• Discover, Even if You Use Python As a Beginner, Practical Ways to Build Your Machine Learning Solutions. With all the data available today, machine learning applications are limited only by your imagination.
• Have in Your Hands  Several Possibilities for Both High and Low-Level Web Development to create websites and web applications for any kind of busines.

Key Features

• Find out how to use Python code to extract insights from data using real-world examples
• Work with structured data and free text sources to answer questions and add value using data
• Perform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing data

What you will learn

• Understand the importance of data literacy and how to communicate effectively using data
• Find out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysis
• Wrangle data and create DataFrames using pandas
• Produce charts and data visualizations using time-series datasets
• Discover relationships and how to join data together using SQL
• Use NLP techniques to work with unstructured data to create sentiment analysis models
• Discover patterns in real-world datasets that provide accurate insights

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).

With six new chapters, on topics including movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.

At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.

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