The 8 Best Machine Learning Books
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
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
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
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
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
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
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
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
