greyhatguy007/Machine-Learning-Specialization-Coursera
Machine Learning Specialization Coursera
This repository is a collection of implementation references and solved notebooks covering supervised, unsupervised, and reinforcement learning techniques. It provides practical guides for building predictive models, clustering algorithms, and autonomous agents.
The project includes specific implementations for neural network architectures, such as multi-layer perceptrons for digit recognition, and recommender systems using collaborative and content-based filtering. It also features reinforcement learning systems that utilize deep Q-learning to optimize decision-making policies.
The codebase covers a broad range of machine learning capabilities, including linear and logistic regression, decision tree modeling, and multiclass classification. It also implements unsupervised learning workflows through K-means clustering and Gaussian anomaly detection. Support for model evaluation is provided via bias and variance analysis, decision boundary visualization, and regularization techniques to prevent overfitting.
The project is implemented as a series of Jupyter Notebooks.
Features
- Machine Learning Implementations - Provides a comprehensive collection of solved notebooks and code implementations for supervised, unsupervised, and reinforcement learning.
- Deep Q-Learning Implementations - Provides deep Q-learning implementations that use neural networks to optimize decision-making policies for autonomous agents.
- Deep Reinforcement Learning Implementations - Implements deep reinforcement learning agents that optimize behaviors through reward-based training.
- K-Means Clustering - Provides K-means clustering implementations that group unlabeled data by iteratively updating centroids.
- Multi-Layer Perceptrons - Builds multi-layer perceptron architectures to solve complex pattern recognition and classification tasks.
- Neural Networks - Implements neural networks for complex pattern recognition, including digit recognition systems.
- Gradient Descent Algorithms - Implements gradient descent algorithms to iteratively optimize model weights and minimize prediction error.
- Recommendation Engines - Implements collaborative and content-based filtering systems to suggest items based on user preferences.
- Recommender Systems - Develops recommender systems using both collaborative and content-based filtering engines.
- Reinforcement Learning - Builds reinforcement learning agents that optimize decision-making policies using deep Q-learning.
- Supervised Learning - Implements supervised learning models for regression and classification using labeled data.
- Unsupervised Learning - Implements unsupervised learning workflows covering K-means clustering and anomaly detection.
- Bias and Variance Analysis - Implements bias and variance analysis to optimize the machine learning development process.
- Multiclass - Implements multiclass classification for categorizing input data into three or more distinct classes.
- Collaborative Filtering Utilities - Implements collaborative filtering mechanisms to predict user preferences based on item and user similarity matrices.
- Decision Boundary Visualizations - Provides visualizations of decision boundaries to analyze classifier distinctions.
- Decision Trees - Provides implementations of decision tree models that partition data using tree-based logic.
- Feature Scale Normalization - Provides feature scaling and normalization techniques to ensure stable model convergence during training.
- High-Level Model APIs - Utilizes high-level model APIs and libraries to implement complex neural network architectures.
- Linear Regression - Trains linear models to find the relationship between features and targets using normal equations and optimization.
- Multiple - Implements multiple linear regression using vectorized computations and gradient descent.
- Logistic Loss Calculators - Computes logistic loss to measure the performance of binary classification models.
- Logistic Regression Models - Builds classification models using the sigmoid function and gradient descent to predict binary outcomes.
- Regularization Techniques - Implements regularization techniques to penalize large weights and prevent model overfitting.
- Model Performance Analysis - Implements model performance analysis using bias-variance diagnostics and regularization to optimize accuracy.
- Model Performance Evaluators - Implements cost functions to quantify model performance by comparing predictions against ground truth.
- Neural Network Architectures - Constructs multi-layer neural network architectures with various activation functions for classification.
- Neural Network Implementations - Implements neural networks from scratch using manual matrix operations and mathematical logic.
- Digit Recognition - Implements neural networks to identify and classify handwritten digit images.
- L2 Regularization - Implements L2 regularization to penalize large weights and prevent model overfitting.
- Anomaly Detection - Implements Gaussian anomaly detection to identify outliers by modeling normal data distributions.
- Gaussian Anomaly Detectors - Implements Gaussian anomaly detection to identify outliers by modeling the distribution of normal data points.
- Recommendation Algorithms - Implements collaborative and content-based recommendation algorithms to suggest items based on user preferences.
