• Blog
  • The 8 best machine learning books

The 8 best machine learning books

May 14, 2020
· 4 min read

This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here.

With the prevalence of computer science constantly rising, knowing at least the basics of machine learning systems is extremely valuable in business. Today we will be discussing 8 of the best machine learning books, from beginner to expert level, along with the topics covered in each, where you can get a copy, and the next steps you can take after reading these books. Let’s get started.

Beginner books

1. Machine Learning for Absolute Beginners: A Plain English Introduction


Topics covered:

  • Downloading free datasets
  • Tools and machine learning libraries you need
  • Data scrubbing techniques (includes one-hot encoding, binning and dealing with missing data)
  • Preparing data for analysis (includes k-fold Validation)
  • Regression analysis to create trend lines
  • Clustering (includes k-means and k-nearest Neighbors)
  • The basics of Neural Networks
  • Bias/Variance to improve your machine learning model
  • Decision Trees to decode classification
  • Building your first ML model to predict house values using Python

Price: $14.80
Author: Oliver Theobald
Where to buy: Amazon

2. Introduction to Machine Learning with Python

image6 700x922 1

Topics covered:

  • Fundamental concepts and applications of machine learning
  • Advantages/shortcomings of widely used machine learning algorithms
  • Representing data processed by ML and which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of “pipelines” for chaining models and encapsulating your workflow
  • Methods for working with text data (including text-specific processing techniques)
  • Suggestions for improving your machine learning and data science skills

Price: $51.48
Author: Andreas C. Müller & Sarah Guido
Where to buy: Amazon

3. Machine Learning For Dummies

image7 700x878 1

Topics covered:

  • Learn how day-to-day activities are powered by machine learning
  • Learn to ‘speak’ certain languages (such as Python and R), allowing you to teach machines how to perform data analysis and pattern-oriented tasks
  • How to code in R using R Studio
  • How to code in Python using Anaconda

Price: $21.31
Author: John Paul Mueller & Luca Massaron
Where to buy: Amazon

Intermediate Books

4. Python Machine Learning By Example

image8 700x862 1

Topics covered:

  • Handling data extraction, manipulation, and exploration techniques
  • Visualization of data spread across multiple dimensions and extracting useful features
  • Correctly predicting situations using analytics
  • Implementing ML classification and regression algorithms from scratch
  • Evaluating and optimizing the performance of a machine learning model
  • Solving real-world problems using machine learning

Price: $49.99
Author: Yuxi (Hayden) Liu
Where to buy: Amazon

5. Hands-On Machine Learning with Scikit-Learn and TensorFlow

image3 700x888 1

Topics covered:

  • Exploring the machine learning landscape, particularly neural nets
  • Using scikit-learn to track an example machine-learning project end-to-end
  • Several training models (includes support vector machines, decision trees, random forests, and ensemble methods)
  • Using the TensorFlow library to build and train neural nets
  • Dive into neural net architectures (includes convolutional nets, recurrent nets, and deep reinforcement learning)
  • Techniques for training and scaling deep neural nets
  • Applying practical code examples without acquiring excessive machine learning theory or algorithm details

Price: $56.99
Author: Aurélien Géron
Where to buy: Amazon

6. Pattern Recognition and Machine Learning

image4 700x914 1

Topics covered:

  • Introduction to basic probability theory
  • Introduction to pattern recognition and machine learning
  • Graphical models to describe probability distributions
  • Approximate inference algorithms
  • New models based on kernels
  • Bayesian methods

Price: $73.99
Author: Christoper M. Bishop
Where to buy: Amazon

Advanced Books

7. Machine Learning: A Probabilistic Perspective

image1 700x788 1

Topics covered:

  • Comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach
  • Probability
  • Optimization
  • Linear algebra
  • Conditional random fields
  • L1 regularization
  • Deep learning

Price: $68.33
Author: Kevin P. Murphy
Where to buy: Amazon

8. Deep Learning

image5 700x916 1

Topics covered:

  • Mathematical and conceptual background
    • Linear algebra
    • Probability theory and information theory
    • Numerical computation
    • Machine learning
  • Deep learning techniques used in industry
    • Deep feedforward networks
    • Regularization
    • Optimization algorithms
    • Convolutional networks
    • Sequence modeling
    • Practical methodology
  • Research perspectives
    • Linear factor models
    • Autoencoders
    • Representation learning
    • Structured probabilistic models
    • Monte Carlo methods
    • The partition function
    • Approximate inference
    • Deep generative models

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” — Elon Musk (Co-founder/CEO of Tesla and SpaceX, Co-chair of OpenAI)

Price: $70.00
Author: Ian Goodfellow, Yoshua Bengio, & Aaron Courville
Where to buy: Amazon

Next steps

These books teach the ins-and-outs of ML, but that’s only the first step. If you’re interested in working in machine learning, your next steps would be to practice engineering ML. If you’re part of a business that uses ML, and your organization needs a way of implementing machine learning models efficiently at scale, then that’s where Algorithmia steps in. We created a serverless microservices architecture that allows enterprises to easily deploy and manage machine learning models at scale.

See DataRobot in Action
Request a demo
About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

Meet DataRobot
  • Listen to the blog
  • Share this post
    Subscribe to DataRobot Blog
    Newsletter Subscription
    Subscribe to our Blog