5 Free Books to Master Machine Learning
Machine Learning is one of the most exciting fields in computer science today. In this article, we will take a look at the five best yet free books to learn machine learning in 2023.
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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
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?
- What is Machine Learning?
- ML Categories
- The ML Toolbox
- Data Scrubbing
- Setting Up Your Data
- Regression Analysis
- 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
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.
- 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
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.
- 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
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.
- 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
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!
- 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
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 is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.