Learning · Article or course
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
Practical guide to machine learning and deep learning using Python frameworks.
This book by Aurélien Géron provides an end-to-end, code-first introduction to building machine learning and deep learning models. It covers core ML algorithms using Scikit-Learn alongside neural network architectures built with Keras and TensorFlow. It is widely treated as a standard practical textbook for developers bridging the gap between theory and real code.
What it's for
- Build and deploy an end-to-end machine learning project from scratch
- Train and evaluate traditional machine learning models like SVMs and decision trees
- Construct deep neural networks for computer vision and other complex tasks
- Prepare, clean, and engineer pipelines for training data
Features
- Covers both classical machine learning and modern deep learning architectures
- Hands-on exercises and accompanying Jupyter and Google Colab notebooks
- Step-by-step walkthrough of an end-to-end project workflow
- Practical code implementations using Scikit-Learn, Keras, and TensorFlow
- Detailed guidance on performance evaluation, hyperparameter tuning, and cross-validation
Pricing
Paid book purchase or access via O'Reilly subscription
Details
- Type
- Article or course
- Category
- Learning
- Topic
- Books
- Open source
- No
- Platforms
- Web, other
- For
- Python developers and data scientists looking for a code-first, practical machine learning guide.
- Alternative to
- Deep Learning with Python by François Chollet, Pattern Recognition and Machine Learning
- Tags
- machine learning, deep learning, python, scikit learn, tensorflow, keras
Why it was saved
- @datasciencebrain: Widely considered the definitive practical guide for transitioning from ML concepts to real-world code implementation.
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