Learning_ML

Welcome to the Learning_ML repository! Here, you'll find all the Jupyter notebooks I have created while working through the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition by Aurélien Géron. This repository serves as a collection of my practical implementations, notes, and exercises as I progress through each chapter of the book.

Chapter Descriptions

• Chapter 1: The Machine Learning Landscape

  • A broad overview of the machine learning field, its various categories, and real-world applications. This chapter sets the stage for understanding the core concepts of ML.

• Chapter 2: End-to-End Machine Learning Project

  • Walkthrough of a complete ML project, from data loading and preparation to model selection, training, and fine-tuning.

• Chapter 3: Classification

  • Introduction to classification tasks, focusing on key algorithms like logistic regression, decision trees, and support vector machines.

• Chapter 4: Training Models

  • A deep dive into how models are trained, including concepts like gradient descent, loss functions, and the optimization process.

• Chapter 5: Support Vector Machines

  • Detailed exploration of SVMs, their applications, and how they can be used for both classification and regression tasks.

• Chapter 6: Decision Trees

  • Explanation of decision trees, random forests, and ensemble methods, showcasing their strengths and limitations.

• Chapter 7: Ensemble Learning and Random Forests

  • Overview of ensemble learning techniques, including bagging, boosting, and the power of random forests.

• Chapter 8: Dimensionality Reduction

  • Covers techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) to reduce the number of features while retaining important information.

• Chapter 9: Unsupervised Learning Techniques

  • Introduction to clustering algorithms such as K-Means and DBSCAN, and how to discover hidden patterns in data.

• Chapter 10: Introduction to Artificial Neural Networks

  • The basics of neural networks, including perceptrons, multi-layered networks, and activation functions.

• Chapter 11: Training Deep Neural Networks

  • A deeper look into the challenges and best practices when training deep neural networks, including weight initialization, optimizers, and regularization techniques.

• Chapter 12: Custom Models and Training with TensorFlow

  • Learn how to build custom models and train them with TensorFlow’s powerful low-level APIs.

• Chapter 13: Loading and Preprocessing Data with TensorFlow

  • Data pipelines in TensorFlow: how to load, preprocess, and handle large datasets efficiently.

• Chapter 14: Deep Computer Vision Using Convolutional Neural Networks (CNNs)

  • Learn how to build and train CNNs for image recognition tasks, a foundational technology behind computer vision.

• Chapter 15: Processing Sequences Using RNNs and CNNs

  • Explore techniques for working with sequential data, such as time series or natural language, using Recurrent Neural Networks (RNNs) and their variants.

• Chapter 16: Natural Language Processing with RNNs and Attention

  • Dive into the world of NLP, using RNNs and attention mechanisms to process text data and build translation systems.

• Chapter 17: Representation Learning and Generative Learning Using Autoencoders and GANs

  • Introduction to representation learning, autoencoders for unsupervised learning, and GANs (Generative Adversarial Networks) for generating realistic data.

• Chapter 18: Reinforcement Learning

  • A primer on reinforcement learning, the methodology behind many of today’s most advanced AI systems, including deep Q-learning.

Feel free to explore, experiment, and enjoy the journey of learning machine learning!