Top 10 Best machine learning book in 2026

Main highlights

• •Use scikit-learn to track an example ML project end to end
• •Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
• •Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
• •Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
• •Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning