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greyhatguy007 avatar

greyhatguy007/Machine-Learning-Specialization-Coursera

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6,996 stars·3,572 forks·Jupyter Notebook·mit·26 views

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.

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  • 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.

    • Star history

    Star history chart for greyhatguy007/machine-learning-specialization-courseraStar history chart for greyhatguy007/machine-learning-specialization-coursera

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does greyhatguy007/machine-learning-specialization-coursera do?

    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.

    What are the main features of greyhatguy007/machine-learning-specialization-coursera?

    The main features of greyhatguy007/machine-learning-specialization-coursera are: Machine Learning Implementations, Deep Q-Learning Implementations, Deep Reinforcement Learning Implementations, K-Means Clustering, Multi-Layer Perceptrons, Neural Networks, Gradient Descent Algorithms, Recommendation Engines.

    Which projects share features with greyhatguy007/machine-learning-specialization-coursera?

    Projects with overlapping indexed features include: rasbt/python-machine-learning-book — This project is an educational resource providing practical code examples and implementations of machine learning… lawlite19/machinelearning_python — This is a Python machine learning library featuring a collection of core algorithms implemented from scratch to… d2l-ai/d2l-en — This project is an educational platform and research toolkit designed to teach deep learning through a combination of… trekhleb/homemade-machine-learning — This project provides a collection of machine learning algorithms implemented from scratch in Python. It serves as an… jack-cherish/machine-learning — This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using… nyandwi/machine_learning_complete — This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep…

    Projects sharing features with Machine Learning Specialization Coursera

    These projects share indexed features with Machine Learning Specialization Coursera. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • rasbt/python-machine-learning-bookrasbt avatar

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      This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

      Jupyter Notebook
      View on GitHub↗12,614
    • lawlite19/machinelearning_pythonlawlite19 avatar

      lawlite19/MachineLearning_Python

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      This is a Python machine learning library featuring a collection of core algorithms implemented from scratch to demonstrate foundational AI concepts. It provides a comprehensive toolkit for supervised learning, unsupervised learning, and neural network development. The project is distinguished by its custom implementation of a neural network framework, which includes multi-layer perceptrons with backpropagation, gradient descent, and weight regularization. It also includes a specialized anomaly detection toolkit that identifies outliers and rare events using Gaussian probability distributions

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    • d2l-ai/d2l-end2l-ai avatar

      d2l-ai/d2l-en

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      This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

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    • trekhleb/homemade-machine-learningtrekhleb avatar

      trekhleb/homemade-machine-learning

      24,608View on GitHub↗

      This project provides a collection of machine learning algorithms implemented from scratch in Python. It serves as an educational resource using interactive notebooks that combine code with mathematical explanations to demonstrate the first principles of data science. The repository includes reference implementations for neural networks, such as multilayer perceptrons with backpropagation, and supervised learning models including linear and logistic regression. It also covers unsupervised learning through k-means clustering and Gaussian anomaly detection. The codebase covers a broad range of

      Jupyter Notebook
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