Open-source alternatives to Machine Learning With Python Jadi

This repository is a collection of machine learning course materials, providing study notes and Python implementation examples for a professional specialization. It serves as a guide for supervised and unsupervised learning, focusing on the application of fundamental algorithms. The content covers a broad range of machine learning education, including the mathematical foundations and practical prototyping of models. It specifically provides resources for implementing regression, classification, clustering, and dimensionality reduction techniques. The project is organized as a curriculum-base

This project is a deep learning study resource and educational curriculum designed for mastering neural network architectures and theory. It serves as a learning platform that combines theoretical notes and mathematical formulas with practical code implementations. The curriculum is centered on the PyTorch framework, providing a structured path for building and training models through annotated code examples and technical reviews of mathematical foundations. The resource utilizes interactive notebooks for executing machine learning algorithms and experimenting with data models. Theoretical

This repository serves as an educational collection of practical examples and tutorials designed to facilitate the study of machine learning and data science concepts using Python. It provides a structured environment for learning core algorithms and data analysis techniques through hands-on implementation and iterative exploration. The project covers a broad range of analytical capabilities, including predictive modeling for regression, classification, and clustering tasks, as well as network topology analysis for identifying influence patterns in interconnected data. It also incorporates na

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

This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning. The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l

This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples. The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser. The content covers t

Linear-Algebra-With-Python is an educational resource that provides a structured curriculum for learning linear algebra through computational practice. It serves as a tutorial for data scientists and quantitative analysts, bridging the gap between abstract mathematical theory and practical implementation using Python. The project utilizes a literate programming approach, organizing lecture notes and code examples into interactive documents. By interleaving explanatory text with functional code, it allows users to experiment with mathematical concepts directly within their development environm

This project is a Chinese translation of a comprehensive educational resource for implementing machine learning. It serves as a technical guide for developing machine learning models, providing translated documentation and practical tutorials. The resource focuses specifically on the implementation of machine learning using Scikit-Learn and TensorFlow. It provides guides for building traditional machine learning models as well as developing deep learning neural networks. The content covers the end-to-end machine learning workflow, including data preparation, model training, and evaluation. E

This project is a structured data science curriculum and Python-based textbook designed to teach the fundamentals of data science through executable scripts and hands-on lessons. It functions as a guided programming tutorial for data manipulation and analysis within the Python ecosystem. The content covers introductory machine learning, including the implementation of basic models and algorithms, alongside Python data analysis for cleaning and processing datasets. The material is delivered via Jupyter Notebooks, combining modular exercises and markdown-driven documentation to map theoretical

YSDA course in Natural Language Processing

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 o

This repository serves as an educational resource for mastering machine learning concepts through structured exercises and practical programming examples. It functions as a library of implementations for core algorithms and models, designed to accompany standard academic textbooks and technical literature. The project utilizes a literate programming pattern within interactive documents, allowing users to interleave narrative explanations with executable code. By combining text and logic, the repository facilitates step-by-step experimentation and the translation of theoretical concepts into f

100-Days-Of-ML-Code is a machine learning curriculum and instructional resource designed as a structured 100-day learning path. It provides a sequence of daily milestones that cover the mathematical foundations and practical implementations of machine learning algorithms. The project is organized into specialized courses for supervised and unsupervised learning. Supervised learning materials cover the implementation of predictive models such as linear regression, decision trees, and support vector machines. Unsupervised learning materials focus on clustering models, including K-Means and hier

An interactive Python code notebook and machine learning tutorial repository, this project provides a collection of instructional guides and code examples explaining data science concepts and predictive modeling techniques. It specifically functions as a scikit-learn tutorial notebook containing educational documents that demonstrate machine learning algorithms and practical workflows. The repository supports data science tutorial authoring, machine learning education, and interactive notebook learning. It organises content into sequential, self-contained computational steps that combine narr

This repository serves as an educational collection of interactive notebooks and code examples designed to demonstrate fundamental machine learning and deep learning concepts. It provides a structured environment for exploring data science workflows, ranging from basic numerical computing and statistical analysis to the construction of complex neural network architectures. The project distinguishes itself through a focus on hands-on experimentation, offering practical implementations for tasks such as computer vision, natural language processing, and statistical simulation. Users can engage w

This is a comprehensive deep learning course delivered entirely through Jupyter Notebooks, designed to teach neural network construction using TensorFlow 2.x. The curriculum follows a sequential-model-first pedagogy, introducing the Sequential API before moving to functional and subclassing approaches, and covers the full spectrum of model building from regression and classification through convolutional neural networks, natural language processing, and time series forecasting. The course is structured around a checkpoint-based training workflow that saves the best model weights during traini

This project is a collection of interactive Jupyter notebooks designed to teach machine learning and deep learning fundamentals through hands-on coding exercises. It provides a structured curriculum that guides users through the end-to-end data science lifecycle, covering everything from initial data preprocessing to final model evaluation. The repository distinguishes itself by bridging theoretical data science concepts with practical implementation using standard industry libraries. It features a series of tutorials that demonstrate how to build and train predictive models and complex neura

This is an interactive Python tutorial delivered as a collection of Jupyter notebooks. It is designed as a structured learning path for beginners, teaching fundamental language concepts through a sequence of lessons that combine explanatory text with runnable code cells and embedded practice exercises. Each notebook is a self-contained unit that introduces a topic, demonstrates it with a minimal code example, and then asks the learner to write code themselves, receiving immediate feedback from the browser-based execution environment. The curriculum is built on a progressive concept-stacking mo

This project is an educational resource and software architecture framework focused on the technical foundations of large language model engineering. It provides a collection of guides and design patterns for building and maintaining professional, scalable systems using large language models. The resource outlines practical implementation patterns for orchestrating workflows that combine prompt engineering, model calls, and vector databases. It focuses on transforming prompt development into a structured engineering process to ensure reliable model outputs in production environments. The cov

jupyter-themes is a Jupyter Notebook theme manager and CSS interface customizer. It provides a command line tool to apply custom color schemes, fonts, and layout styles to notebook environments. The project includes a data visualization styling tool that synchronizes the aesthetic properties and color schemes of plotting libraries with the active interface theme. This ensures that data charts and figures remain visually consistent with the overall workspace theme.

This project is a collection of educational resources and study materials focused on scientific computing and data analysis using Python. It consists of translated notes and Jupyter notebooks designed to guide learners through the Python data ecosystem. The content covers specialized workflows including numerical computation, data cleaning, and time series analysis. These materials provide a reference for performing complex data manipulations and processing sequential data to identify patterns. The resource is organized as a series of static files and markdown documents using a flat-file dir

This project is a collection of curricular resources and hands-on tutorials designed to teach Python programming and scientific computing. It consists of a series of interactive lessons and executable notebooks that provide a guided approach to learning Python through a combination of code and prose. The curriculum is specifically designed for experienced programmers to quickly master Python syntax, data structures, and core language semantics. It includes an introductory guide to the libraries and programming environments used for scientific computing and complex dataset analysis. The educa

Hands-on-RL is an interactive educational resource and collection of Jupyter notebooks designed for learning reinforcement learning. It combines technical theory with practical, runnable code to demonstrate the implementation and training of mainstream reinforcement learning agents. The project focuses on bridging the gap between theory and practice through a tutorial structure that organizes explanations and executable code blocks sequentially. It enables the prototyping of reinforcement learning models to observe their behavior and performance in real-time. The implementation utilizes a mo

This project is a curated directory of educational roadmaps and resource hubs for artificial intelligence, deep learning, and machine learning. It serves as a centralized collection of academic lectures, instructional videos, and courses designed to provide structured learning paths for AI practitioners. The directory covers specialized academic curricula across several core domains, including computer vision, natural language processing, and reinforcement learning. It also provides access to niche educational content such as medical imaging, Bayesian deep learning, and probabilistic graphica

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 di

This project is a comprehensive collection of machine learning educational resources, featuring a Python-based curriculum, study guides for deep learning, and a specialized knowledge base for machine learning operations. It provides structured learning paths that guide users from foundational programming through to advanced neural network implementations. The repository focuses on interactive learning by providing a directory of executable notebooks and cloud-hosted experiments. It maps theoretical research papers and textbooks to practical code implementations and maintains a curated directo

This repository provides a collection of machine learning algorithms implemented from scratch using pure Python. It serves as an educational resource designed to demonstrate the internal logic and mathematical foundations of predictive models without relying on external machine learning frameworks or black-box libraries. The project distinguishes itself by mapping code implementations directly to their underlying statistical and calculus-based formulas. Each model is constructed using base language primitives and manual gradient descent optimization, allowing users to observe the mechanics of