List Of Machine Learning Technical Blogs Github

This is a curated list of awesome machine learning technical blogs from research to deployment. You want to stay up-to-date with the latest research breakthroughs you want more practical tutorials? In both cases, these are the site to keep an eye on. Your favorite piece is not listed here? Feel free to open an issue or a pull request. Alternatively, you can contact me @antbrl.

Thanks for your contribution! Explore these top machine learning repositories to build your skills, portfolio, and creativity through hands-on projects, real-world challenges, and AI resources. Machine learning is a vast and dynamic field that encompasses a wide range of domains, including computer vision, natural language processing, core machine learning algorithms, reinforcement learning, and more. While taking courses can help you learn the theoretical foundations, they often don't provide the hands-on experience needed to solve real-world problems or demonstrate your abilities to potential employers. To become job-ready as a machine learning engineer, it's essential to build a diverse portfolio of projects that showcase both your technical skills and your practical experience. In this article, we will review 10 GitHub repositories that feature collections of machine learning projects.

Each repository includes example codes, tutorials, and guides to help you learn by doing and expand your portfolio with impactful, real-world projects. Link: ChristosChristofidis/awesome-deep-learning GitHub Copilot and VS Code teams, along with the Microsoft Open Source Program Office (OSPO), sponsored these nine open source MCP projects that provide new frameworks, tools, and assistants to unlock AI-native workflows, agentic... Learn how to go from curious coder to AI wizard—with a little help from GitHub. Explore how Arm’s optimized performance and cost-efficient architecture, coupled with PyTorch, can enhance machine learning operations, from model training to deployment and learn how to leverage CI/CD for machine learning workflows, while reducing time,... Discover the exciting enhancements in GitHub that empower Machine Learning practitioners to do more.

Today we launched new code scanning analysis features powered by machine learning. The experimental analysis finds more of the most common types of vulnerabilities. This curated list presents 51 excellent GitHub repositories to learn Artificial Intelligence, organized by difficulty level: Beginner, Intermediate, and Advanced. They cover a wide range of topics: machine learning, deep learning, generative models, autonomous agents, NLP, computer vision, neural networks, MLOps, and more. Each repository includes its name with a link and a brief description. These are popular, well-documented projects with practical resources such as notebooks, datasets, and projects.

if you want to supercharge your productivity with AI, grab my free ebook: 30 ChatGPT Prompts That Will Make You More Productive Hey, Sunil here. I wanted to take a moment to thank you for reading until the end and for being a part of this community. Did you know that our team run these publications as a volunteer effort to over 200k supporters? We do not get paid by Medium! Machine learning (ML) is one of the fastest-growing fields in technology, and learning it effectively requires access to high-quality resources.

GitHub is a treasure trove of ML projects, tutorials, and tools that can help both beginners and advanced practitioners sharpen their skills. In this article, we explore some of the best GitHub repositories for learning and applying ML concepts, categorized by skill level and focus area. For those new to ML, structured courses and hands-on tutorials can make the learning curve smoother. Here are some excellent GitHub repositories to start with: Once you have a basic understanding of ML, hands-on projects help reinforce concepts. The following repositories provide excellent project-based learning opportunities:

For experienced ML engineers, leveraging state-of-the-art tools can lead to cutting-edge applications. These repositories offer advanced techniques and frameworks: For those interested in specialized ML domains like reinforcement learning or NLP, the following repositories offer deep insights and advanced projects: A curated collection of direct links to Machine Learning and AI engineering blogs from leading tech companies. Skip the navigation hassle - each link takes you straight to ML/AI-specific content. Perfect for staying updated with real-world ML implementations, engineering challenges, and solutions from industry practitioners.

If I had to pick one platform that has single-handedly kept me up-to-date with the latest developments in data science and machine learning – it would be GitHub. The sheer scale of GitHub, combined with the power of super data scientists from all over the globe, make it a must-use platform for anyone interested in this field. Can you imagine a world where machine learning libraries and frameworks like BERT, StanfordNLP, TensorFlow, PyTorch, etc. weren’t open sourced? It’s unthinkable! GitHub has democratized machine learning for the masses.

Interpretability is a HUGE thing in machine learning right now. Being able to understand how a model produced the output that it did – a critical aspect of any machine learning project. This GitHub repository contains InterpretML, an open-source package that offers a range of machine learning interpretability techniques. It allows users to train interpretable models, known as glassbox models, and also provides tools to explain the decisions made by more complex, blackbox systems. InterpretML is designed to help data scientists understand their models’ behavior and the reasons behind individual predictions. This is particularly useful for model debugging, feature engineering, detecting biases, and ensuring regulatory compliance.

The repository includes code for various interpretability techniques, such as Explainable Boosting, Decision Trees, and Linear/Logistic Regression. It also supports popular machine learning frameworks like scikit-learn and can handle dataframes and arrays. With InterpretML, users can gain valuable insights into their machine learning models and make more informed decisions. As a tech writer with 15 years of experience chronicling machine learning’s (ML) meteoric rise, I’ve seen it evolve from obscure algorithms to the backbone of autonomous systems, personalized medicine, and beyond. Staying ahead in this whirlwind of innovation isn’t just about reading papers or skimming X —it demands diving into Machine Learning Blogs that deliver actionable code, visionary research, and practitioner wisdom. These blogs, penned by researchers, engineers, and industry pioneers, are your roadmap to mastering ML’s complexities, from debugging neural nets to navigating AI ethics.

This post is your ultimate guide to the top Machine Learning Blogs to follow in 2025, packed with in-depth reviews, real-world examples, and personal takes to make it a gold mine for ML pros. You’ll find a comparison table, comprehensive blog breakdowns, an FAQ, a richly detailed 2025 ML trends section, and an action plan to maximize value. Written like a fireside chat with a fellow ML nerd, it’s loaded with subheadings, bullets, and ML insights. The blog covers machine learning courses, bootcamps, books, tools, interview questions, cheat sheets, MLOps platforms, and more to master ML and secure your dream job. 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, is home to numerous 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 an ideal starting point for newcomers. It serves as a starting point for those with no prior experience with machine learning and looks to build core competencies using Scikit-learn and Python. Each lesson features supplemental materials including pre- and post-quizzes, written instructions, solutions, assignments, and other resources to complement the hands-on activities. 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 into one centralized location from providers like Clatech, Stanford, and MIT, the repo makes it easier for interested learners to find video-based ML...

In this edition, we explore some of the best GitHub repositories for learning and applying ML concepts, categorized by skill level and focus area. Machine learning (ML) is one of the fastest-growing fields in technology, and learning it effectively requires access to high-quality resources. GitHub is a treasure trove of ML projects, tutorials, and tools that can help both beginners and advanced practitioners sharpen their skills. For those new to ML, structured courses and hands-on tutorials can make the learning curve smoother. Here are some excellent GitHub repositories to start with: Once you have a basic understanding of ML, hands-on projects help reinforce concepts.

The following repositories provide excellent project-based learning opportunities: For experienced ML engineers, leveraging state-of-the-art tools can lead to cutting-edge applications. These repositories offer advanced techniques and frameworks: For those interested in specialized ML domains like reinforcement learning or NLP, the following repositories offer deep insights and advanced projects:

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