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Mastering machine learning (ML) can seem overwhelming, but with the right resources, it can be much more manageable. GitHub, the widely used code hosting platform, hosts many valuable repositories that can benefit learners and practitioners at all levels. In this article, we review 10 essential GitHub repositories that provide a range of resources, from beginner-friendly tutorials to advanced machine learning tools.
This comprehensive 12-week program offers 26 lessons and 52 quizzes, making it a perfect starting point for newcomers. It serves as a starting point for those who have no prior machine learning experience and seeks to develop basic skills using Scikit-learn and Python.
Each lesson includes additional materials including pre- and post-quizzes, written instructions, solutions, homework, and other resources to supplement the hands-on activities.
This GitHub repository serves as a curated index of quality machine learning courses hosted on YouTube. By bringing together links to various ML tutorials, conferences, and educational series in one centralized location from vendors like Clatech, Stanford, and MIT, the repository makes it easier for interested learners to find video-based ML content that meets their needs.
It’s the only repository you need if you’re trying to learn things for free and at your own pace.
Mathematics is the backbone of machine learning, and this repository serves as a companion webpage to the book “Mathematics for Machine Learning.” The book motivates readers to learn the mathematical concepts necessary for machine learning. The authors aim to provide the mathematical skills needed to understand advanced machine learning techniques, rather than covering the techniques themselves.
It covers linear algebra, analytical geometry, matrix decompositions, vector calculus, probability, distribution, continuous optimization, linear regression, PCA, Gaussian mixture models and SVMs.
The Deep Learning Handbook is a comprehensive resource intended to help students and practitioners enter the field of machine learning, particularly deep learning. Published in 2016, the book provides a theoretical and practical foundation on the machine learning techniques that have led to recent advances in artificial intelligence.
The online version of the MIT Deep Learning Book is now complete and will remain freely available online, making a valuable contribution to the democratization of AI education.
The book covers a wide range of topics in depth, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.
Machine Learning ZoomCamp is a free, four-month online bootcamp that provides a comprehensive introduction to machine learning engineering. Ideal for those looking to advance their career, this program guides students in creating real-world machine learning projects, covering fundamental concepts such as regression, classification, evaluation metrics, model deployment, decision trees, neural networks, Kubernetes and TensorFlow Serving.
During the course, participants will gain hands-on experience in areas such as deep learning, serverless model deployment, and ensemble techniques. The program culminates with two capstone projects that allow students to demonstrate their newly developed skills.
This repository is a collection of tutorials, articles, and other resources on machine learning and deep learning. It covers a wide range of topics like Quora, Blogging, Interviewing, Kaggle Competitions, Cheat Sheets, Deep Learning Frameworks, Natural Language Processing, Computer Vision, Various Learning Algorithms automatic and assembly techniques.
The resource is designed to provide theoretical and practical knowledge with code examples and use case descriptions. It is a comprehensive learning tool that offers a multi-faceted approach to getting to grips with the machine learning landscape.
Awesome Machine Learning is a curated list of awesome machine learning frameworks, libraries, and software, perfect for those looking to explore different tools and technologies in the field. It covers tools in a range of programming languages from C++ to Go, which are then divided into various machine learning categories including computer vision, reinforcement learning, neural networks and machine learning for general use.
Awesome Machine Learning is a comprehensive resource for machine learning practitioners and enthusiasts, covering everything from data processing and modeling to model deployment and production. The platform makes it easy to compare different options to help users find the best solution for their specific projects and goals. Additionally, the repository stays up to date with the latest and greatest machine learning software in various programming languages, thanks to contributions from the community.
This repository provides condensed references and refreshers on machine learning concepts covered in Stanford’s CS 229 course. It aims to consolidate all the important concepts into VIP cheat sheets covering major topics such as supervised learning, unsupervised learning and deep learning. The repository also contains VIP updates that highlight prerequisites in probability, statistics, algebra, and calculus. Plus, there’s a super VIP cheat sheet that compiles all of these concepts into one ultimate reference that learners can easily have on hand.
By bringing together these key points, definitions, and technical concepts, the goal is to help learners fully understand machine learning topics in CS 229. Cheat sheets help summarize essential concepts from courses and textbooks into references condensed for technical interviews.
It provides a comprehensive study guide and resources to prepare for machine learning and data science engineering interviews at top tech companies like Facebook, Amazon, Apple, Google, Microsoft, etc.
Key themes covered:
- LeetCode questions sorted by type (SQL, programming, statistics).
- ML fundamentals like logistic regression, KMeans and neural networks.
- Deep learning concepts, from activation functions to RNNs.
- Designing ML systems, including articles on technical debt and ML rules
- Classic ML articles to read.
- ML production challenges like scaling at Uber and DL in production
- Common interview questions on ML system design, e.g. video/feed recommendation, fraud detection.
- Examples of solutions and architectures for YouTube, Instagram recommendations.
The guide consolidates material from top experts like Andrew Ng and includes real interview questions asked at top companies. It aims to provide the study plan to crack ML interviews in various leading technology companies.
This repository provides a curated list of open source libraries to help deploy, monitor, version, scale, and secure machine learning models in production environments. It covers various aspects of production machine learning, including:
- Explain the predictions and model
- Privacy-preserving ML
- Model and data versioning
- Orchestrating model training
- Model service and monitoring
- Automatic ML
- Data Pipeline
- Data labeling
- Metadata management
- Distribution of calculations
- Model serialization
- Optimized calculation
- Data flow processing
- Outlier and anomaly detection
- Feature Store
- Contradictory robustness
- Data storage optimization
- Data Science Notebook
- Neural research
- And more.
Whether you’re a beginner or an experienced ML practitioner, these GitHub repositories provide a wealth of knowledge and resources to deepen your understanding and skills in machine learning. From fundamental mathematics to advanced techniques and practical applications, these repositories are essential tools for anyone wanting to master machine learning.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a master’s degree in technology management and a bachelor’s degree in telecommunications engineering. His vision is to create an AI product using a graphical neural network for students struggling with mental illness.