ageron/handson-ml
Handson Ml
This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning.
The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation of autoencoders and capsule networks.
The repository covers the full data science pipeline, including data acquisition, sanitization, preprocessing, and dimensionality reduction. It further addresses model development through hyperparameter optimization, candidate model evaluation, and the use of ensemble methods.
A reproducible containerized environment is provided to manage dependencies, launch notebooks, and enable GPU acceleration.
Features
- Machine Learning Education - Provides educational materials focused on teaching fundamental machine learning concepts and implementation techniques.
- Educational Code Notebooks - Organizes educational content as executable code notebooks combining markdown documentation with live Python examples.
- Computational Graphs - Builds deep learning models using layered computational graphs to transform input data into predictions.
- Data Transformation Pipelines - Sequences data cleaning, scaling, and encoding into reusable pipelines to prevent data leakage.
- Ensemble Methods - Implements ensemble methods like bagging and boosting to improve predictive accuracy and reduce model variance.
- Machine Learning Implementations - Provides code-based reference examples and implementations of core machine learning algorithms.
- Deep Learning Implementations - Implements deep learning neural network architectures using layered computational graphs.
- Educational Neural Network Implementations - Provides pedagogical implementations of neural network components built from first principles.
- Data Preprocessing - Implements data preprocessing steps including cleaning missing values and scaling attributes for training.
- Hyperparameter Optimization - Provides iterative search strategies to optimize model hyperparameters against validation sets.
- Data Science Tutorials - Offers step-by-step guides for the entire data science pipeline, from acquisition to dimensionality reduction.
- Data Acquisition & Sanitization - Provides processes for collecting raw datasets and sanitizing sensitive information to ensure data privacy.
- Autoencoders - Implements neural network architectures for unsupervised learning through data compression and reconstruction.
- Convolutional Neural Networks - Builds deep learning architectures using convolutional layers to extract spatial features from images.
- Decision Trees - Implements supervised learning models using tree-like structures to predict target categories.
- Dimensionality Reduction - Transforms high-dimensional datasets into lower-dimensional representations to simplify visualization and reduce complexity.
- Ensemble Learning - Combines multiple learning algorithms via bagging and boosting to improve predictive robustness.
- Hyperparameter Tuning - Demonstrates techniques for fine-tuning hyperparameters and comparing model performance to improve accuracy.
- Model Training Optimizers - Fine-tunes hyperparameters using random search or Bayesian optimization to improve training convergence.
- Gradient Descent Algorithms - Demonstrates vectorized gradient descent using matrix operations to efficiently update model weights.
- Random Forest Ensembles - Implements predictive models that aggregate multiple decision trees to reduce variance and overfitting.
- Recurrent Neural Networks - Implements sequence-based architectures for processing time-series or textual data.
- Reinforcement Learning - Builds agents that learn optimal behaviors through trial and error and reward maximization.
- Support Vector Machines - Trains linear and non-linear classifiers to find optimal decision boundaries maximizing class margins.
- Model Evaluation - Uses cross-validation to evaluate and compare the performance of multiple candidate models.
- Data Characteristic Analyzers - Examines attribute types, distributions, and correlations to identify optimal data transformations.
- Containerized Notebook Servers - Launches a notebook server within a containerized environment to execute code without local dependencies.
- Containerized Training Environments - Provides pre-configured container images to ensure consistent dependencies and GPU acceleration for model training.
- Machine Learning and Deep Learning - Practical guide to building machine learning systems with Python.
- Educational Resources - Fundamentals of machine learning with Python.
