The 8 best machine learning books

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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

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

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

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

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

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

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

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.