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Mastering machine learning (ML) may seem overwhelming, but with the right resources it can be much more manageable. GitHub, the widely used code hosting platform, hosts numerous valuable repositories that can benefit students and professionals of all levels. In this article, we review 10 essential GitHub repositories that provide a variety of resources, from beginner tutorials to advanced machine learning tools.
Repository: microsoft/ML-for-beginners
This comprehensive 12-week program offers 26 lessons and 52 tests, making it an ideal starting point for newcomers. It serves as a starting point for those without prior experience with machine learning and seeks to develop core competencies using Scikit-learn and Python.
Each lesson includes supplemental materials including pre- and post-tests, written instructions, solutions, assignments, and other resources to complement the hands-on activities.
Repository: ai/ML-YouTube-Courses” rel=”noopener” target=”_blank”>Dair-ai/ML-YouTube-Courses
This GitHub repository serves as a curated index of quality machine learning courses hosted on YouTube. By collecting links to various ML tutorials, lectures, and educational series in a centralized location from vendors such as Clatech, Stanford, and MIT, the repository makes it easy for interested students 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 on your own time.
Repository: mml-book/mml-book.github.io
Mathematics is the backbone of machine learning and this repository serves as a companion web page to the book “Mathematics for Machine Learning.” The book motivates readers to learn mathematical concepts necessary for machine learning. The authors aim to provide the mathematical skills necessary to understand advanced machine learning techniques, rather than covering the techniques themselves.
Covers linear algebra, analytical geometry, matrix decomposition, vector calculus, probability, distribution, continuous optimization, linear regression, PCA, Gaussian mixture models, and SVM.
Repository: janishar/mit-deep-learning-book-pdf
The Deep Learning textbook is a comprehensive resource intended to help students and professionals enter the field of machine learning, specifically deep learning. Published in 2016, the book provides a theoretical and practical foundation on the machine learning techniques that have driven recent advances in artificial intelligence.
The online version of the MIT Deep Learning Book is now complete and will continue to be freely available online, representing a valuable contribution to the democratization of ai education.
The book covers a wide range of topics in depth, including deep feedback networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.
Repository: DataTalksClub/machine learning zoomcamp
Machine Learning ZoomCamp is a free four-month online bootcamp that provides a comprehensive introduction to machine learning engineering. Ideal for those serious about advancing their careers, this program guides students through creating real-world machine learning projects, covering fundamental concepts such as regression, classification, evaluation metrics, model implementation, trees decision making, neural networks, Kubernetes and TensorFlow Serving.
Throughout the course, participants will gain hands-on experience in areas such as deep learning, serverless model deployment, and ensemble techniques. The curriculum culminates with two capstone projects that allow students to demonstrate their newly developed skills.
Repository: ujjwalkarn/Machine Learning Tutorials
This repository is a collection of tutorials, articles, and other resources on machine learning and deep learning. It covers a wide range of topics such as Quora, blogs, interviews, Kaggle contests, cheat sheets, deep learning frameworks, natural language processing, computer vision, various machine learning algorithms and assembly techniques.
The resource is designed to provide both 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 exposed to the machine learning landscape.
Repository: josephmisiti/awesome-machine-learning
Awesome Machine Learning is a curated list of amazing machine learning frameworks, libraries, and software that is perfect for those looking to explore different tools and technologies in the field. It covers tools in a variety of programming languages, from C++ to Go, which fall into several machine learning categories, including computer vision, reinforcement learning, neural networks, and general-purpose machine learning.
Awesome Machine Learning is a comprehensive resource for machine learning professionals and enthusiasts, covering everything from data processing and modeling to model deployment and production. The platform facilitates easy comparison of different options to help users find the one that best suits their specific projects and goals. Additionally, the repository is kept up-to-date with the latest and greatest machine learning software in various programming languages, thanks to contributions from the community.
Repository: afshinea/stanford-cs-229-machine-learning
This repository provides condensed references and updates on machine learning concepts covered in Stanford's CS 229 course. It aims to consolidate all the important notions into VIP cheat sheets covering important topics like 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 definitive reference that students can keep on hand.
By bringing together these key points, definitions, and technical concepts, the goal is to help students fully understand the machine learning topics in CS 229. Cheat sheets allow you to summarize vital concepts from lectures and materials from textbooks. text in condensed references for technical interviews.
Repository: khangich/interview-machine-learning
Provides a comprehensive study guide and resources to prepare for machine learning engineering and data science interviews at top technology companies such as Facebook, Amazon, Apple, Google, Microsoft, etc.
Key topics covered:
- LeetCode questions categorized by type (SQL, programming, statistics).
- ML fundamentals such as logistic regression, KMeans, neural networks.
- Deep learning concepts from activation functions to RNN.
- Design of ML systems, including documents on technical debt and ML rules.
- Classic ML articles to read.
- ML production challenges like scaling in Uber and DL in production
- Common ML system design interview questions, e.g. video/stream recommendation, fraud detection.
- Examples of solutions and architectures for YouTube, Instagram recommendations.
The guide consolidates materials from top experts like Andrew Ng and includes questions from real interviews conducted at top companies. It aims to provide the curriculum for conducting ML interviews at several large technology companies.
Repository: EthicalML/awesome-production-machine-learning
This repository provides a curated list of open source libraries to help deploy, monitor, version, scale, and secure machine learning models in production environments. Covers various aspects of production machine learning, including:
- Explaining the predictions and the model
- ML to preserve privacy
- Model and data versions
- Model Training Orchestration
- Model service and monitoring
- AutoML
- Data pipeline
- Data labeling
- Metadata management
- Calculation distribution
- Model serialization
- Optimized computing
- Data stream processing
- Detection of outliers and anomalies
- Feature Store
- Adversarial robustness
- Data storage optimization
- Data science notebook
- Neural search
- 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 serious about mastering 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 data science and machine learning technologies. Abid has a Master's degree in technology Management and a Bachelor's degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
