Publisher's Synopsis
The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Perhaps you're ready to jump in, but you're unsure where or how to begin. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.
In a way that's approachable yet deeply informative, author Aurelien Geron delivers the ultimate introductory guide to machine learning and deep learning. With a focus on clear explanations and real-world Python examples, the book takes you through cutting-edge tools like scikit-learn and PyTorch--from basic regression techniques to advanced neural networks like transformers and generative adversarial networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to begin building intelligent systems.
- Understand ML basics, including concepts like overfitting and hyperparameter tuning
- Learn to build end-to-end ML projects using scikit-learn, from data exploration to model evaluation
- Explore advanced architectures like convolutional and recurrent neural networks with PyTorch
- Discover techniques for unsupervised learning, such as clustering and anomaly detection
- Increase your expertise in state-of-the-art AI systems by fine-tuning pretrained models
- Build tangible skills with complete hands-on coding exercises and real-world applications
