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In today's high-tech world, machine learning is super important. You might have taken some online courses, but they often skim over the details. If you really want to dig deep and master machine learning, books are the way to go. I know it can be overwhelming with so many options out there. But don't worry, we've got your back.

I have handpicked five books that made a big difference in my own machine learning journey. These books will help you understand machine learning better in 2023. 

So, if you are ready to take your knowledge to the next level and explore the depths of this fascinating field, keep reading.

 

1. Machine Learning For Absolute Beginners 

 

Author: Oliver Theobald

Link:  Machine Learning For Absolute Beginners

 

Book Cover
 

You have heard the word Machine Learning and want to delve into this exciting field, but you don’t know where to start. Then this is the right book for you! 

This book is perfect for those who are new to the field and don’t have any prior coding experience. It is written in plain English and does not require any prior coding experience. The book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations. What else would you want more?

Topics Covered:

• What is Machine Learning?
• ML Categories
• The ML Toolbox
• Data Scrubbing
• Setting Up Your Data
• Regression Analysis
• Clustering
• Bias & Variance
• Artificial Neural Networks
• Decision Trees
• Ensemble Modeling
• Building a Model in Python
• Model Optimization

 

2. Mathematics for Machine Learning 

 

Author: Marc Peter Deisenroth

Link: Mathematics for Machine Learning

 

Book Cover
 

Now that you know some basic concepts, it is time to build your base for complex topics of machine learning. What should you do now? Mathematics for Machine Learning is all you need!

It is a self-contained textbook that introduces the fundamental mathematical tools needed to understand machine learning. The book presents mathematical concepts with a minimum of prerequisites and uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines.

The author of the book, Marc Peter Deisenroth, is the DeepMind Chair in Artificial Intelligence at University College London and has received several awards for his research in machine learning.

Topics Covered:

• Linear Algebra
• Analytic Geometry
• Matrix Decompositions
• Vector Calculus
• Probability and Distributions
• Continuous Optimization
• When Models Meet Data
• Linear Regression
• Dimensionality Reduction with Principal Component Analysis
• Density Estimation with Gaussian Mixture Models
• Classification with Support Vector Machines

 

3. Machine Learning for Hackers 

 

Authors:  Drew Conway and John Myles White

Link: Machine Learning for Hackers

 

Book Cover
 

You have been onto learning theory till now and you really want to get started  with hardcore machine learning coding. Do not worry then. If you are someone with a knack for programming and coding, this book is tailored just for you.

The book incorporates practical case studies to demonstrate the real-world relevance of machine learning algorithms. These examples, including one on building a Twitter follower recommendation system, serve to connect abstract concepts with tangible applications. This book is best for programmers who enjoy practical case studies.

Topics Covered:

• Data Exploration
• Classification: Spam Filtering
• Ranking: Priority Inbox
• Regression: Predicting Page Views
• Regularization: Text Regression
• Optimization: Breaking Codes
• PCA: Building a Market Index
• MDS: Visually Exploring US Senator Similarity
• kNN: Recommendation Systems
• Analyzing Social Graphs
• Model Comparison

 

4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

 

Author: Geron Aurelien

Link: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

 

Book Cover
 

This book is a practical guide to machine learning that focuses on building end-to-end systems. The book covers a wide range of topics including linear regression, decision trees, ensemble methods, neural networks, deep learning, and more. 

The latest edition of this book contains code from cutting-edge versions of machine learning and deep learning libraries like TensorFlow and Scikit-Learn. 

Topics Covered:

• Performance measure selection
• Test set creation
• Linear regression with Gradient Descent
• Ridge, Lasso, and Elastic Net regression
• SVM for classification
• Decision Trees and Gini Impurity
• Ensemble learning methods
• Principal Component Analysis (PCA)
• Clustering with K-Means and DBSCAN
• Artificial Neural Networks with Keras
• Deep neural network training
• Custom models with TensorFlow
• Data loading and preprocessing with TensorFlow
• CNNs, RNNs, and GANs in Deep learning

 

5. Approaching (Almost) Any Machine Learning Problem

 

Author: Abhishek Thakur

Link: Approaching (Almost) Any Machine Learning Problem

 

Book Cover
 

Ready to take your machine learning skills to the next level? This book is your ticket to the exciting world of applied machine learning. While it does not bog you down with complex algorithms, it is all about the "how" and "what" of solving real-world problems using machine learning and deep learning. If you're eager to bridge the gap between theory and practice, this book is definitely going to be your guide!

Topics Covered:

• Supervised vs unsupervised learning
• Cross-validation techniques
• Evaluation metrics
• Structuring machine learning projects
• Handling categorical variables
• Feature engineering
• Feature selection
• Hyperparameter optimization
• Image and text classification, ensembling, and reproducible code

 

Conclusion

 

In this article, we introduced you to the five best books to learn machine learning in 2023. These books cover a wide range of topics, from the basics of machine learning to more advanced topics like deep learning. They are all well-written and easy to follow, even for beginners.

If you are serious about learning machine learning, I encourage you to read all five of these books. However, if you are only able to read one or two, I recommend Machine Learning for Absolute Beginners by Oliver Theobald and Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron.

We're curious to know which books have played a pivotal role in your machine learning journey. Feel free to share your recommendations in the comment section.
 
 

Kanwal Mehreen Kanwal is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook "Maximizing Productivity with ChatGPT". As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She's also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.