Ml Coursera Python Assignments

This project is a machine learning coursework repository containing a collection of Python exercises and notebooks. It is designed for implementing foundational machine learning algorithms and completing curriculum assignments through interactive documents that combine instructional text and executable code.

The repository provides code formatted for compatibility with automated grading systems, allowing for the submission and validation of technical exercises. It includes predefined environment configurations and dependency locks to ensure consistent execution of data science tools across different compute environments.

The project covers machine learning implementation and education, utilizing an interactive notebook workflow to translate mathematical concepts into functional code. These exercises are designed to run within browser-based workspaces to remove the need for local software installation.

Features

Machine Learning Education - Provides comprehensive materials for teaching fundamental machine learning concepts and implementation techniques.
Machine Learning Foundations - Serves as a collection of exercises and notebooks focusing on the mathematical and theoretical foundations of machine learning.
Machine Learning Implementations - Implements foundational machine learning algorithms using Python to bridge mathematical theory and functional code.
Data Science Notebooks - Implements a step-by-step machine learning learning workflow using interactive notebooks that combine instructional text and executable code.

Visualize-ML/Book5_Essentials-of-Probability-and-Statistics

3,675View on GitHub

This project is an educational resource providing a mathematical foundation in probability and statistics for machine learning. It offers a collection of interactive notebooks and textbooks designed to explain core statistical theories and data science principles through practical code examples. The content is structured into modular chapters that allow for self-paced learning of topics such as Bayesian inference and probability distributions. By utilizing browser-based execution and declarative visualization, the project enables users to manipulate variables and observe mathematical outcomes

ujjwalkarn/Machine-Learning-Tutorials

17,909View on GitHub

This repository serves as a structured educational resource for machine learning and data science, providing a centralized collection of tutorials, lecture notes, and implementation guides. It is designed to support self-directed learning by organizing complex technical concepts into a clear, hierarchical path that spans from foundational statistical methods to advanced deep learning architectures. The project distinguishes itself through a comprehensive approach to skill development, bridging the gap between theoretical algorithmic foundations and functional software applications. It offers

rasbt/python-machine-learning-book-3rd-edition

4,988View on GitHub

This is the companion code repository for the third edition of the book Python Machine Learning. It delivers the entire learning path as a structured collection of Jupyter notebooks that progress from classical machine learning algorithms to advanced deep learning models, with every concept demonstrated through executable code and narrative text. What distinguishes this resource is its pedagogical design. Each notebook cell encapsulates a single conceptual step, letting readers run, inspect, and modify discrete units of learning. The code provides interchangeable implementations of deep lea

mrdbourke/zero-to-mastery-ml

5,839View on GitHub

This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr

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Features

Open-source alternatives to Ml Coursera Python Assignments

Visualize-ML/Book5_Essentials-of-Probability-and-Statistics

ujjwalkarn/Machine-Learning-Tutorials

rasbt/python-machine-learning-book-3rd-edition

mrdbourke/zero-to-mastery-ml

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Open-source alternatives to Ml Coursera Python Assignments

Visualize-ML/Book5_Essentials-of-Probability-and-Statistics

ujjwalkarn/Machine-Learning-Tutorials

rasbt/python-machine-learning-book-3rd-edition

mrdbourke/zero-to-mastery-ml