🗂 One of the best resources for learning Data Science and Machine Learning

Kaggle offers interactive courses that will help you quickly understand the key topics of DS and ML.

The format is simple: short lessons, practical tasks, and a certificate upon completion — all for free.

Inside:

• basics of Python for data analysis;
• machine learning and working with models;
• pandas, SQL, visualization;
• advanced techniques and practical cases.

Each course takes just 3–5 hours and immediately provides practical knowledge for work.

➡ Link to the platform

tags: #ML #DEEPLEARNING #AI

➡ https://news.1rj.ru/str/CodeProgrammer

#Polars Cheat Sheet with example

Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb

https://news.1rj.ru/str/DataAnalyticsX 🔴

💾 6 AI courses from Anthropic for free

If you work with AI, agents, or APIs, this is the foundation that developers in top companies are currently going through.

▶️ Working with the Claude API

https://anthropic.skilljar.com/claude-with-the-anthropic-api 

▶️ Introduction to Model Context Protocol (MCP)

https://anthropic.skilljar.com/introduction-to-model-context-protocol 

▶️ Claude in Amazon Bedrock

https://anthropic.skilljar.com/claude-in-amazon-bedrock 

▶️ Claude in Google Cloud (Vertex AI)

https://anthropic.skilljar.com/claude-with-google-vertex 

▶️ Advanced MCP

https://anthropic.skilljar.com/model-context-protocol-advanced-topics 

▶️ Claude Code in Practice

https://anthropic.skilljar.com/claude-code-in-action 

tags: #courses #ai

➡ https://news.1rj.ru/str/CodeProgrammer

🤖 Best GitHub repositories to learn AI from scratch in 2026

If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:

1) Karpathy – Neural Networks: Zero to Hero 
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero

2) Hugging Face Transformers 
The main library of modern NLP/LLM: models, tokenizers, fine-tuning 
https://github.com/huggingface/transformers

3) FastAI – Fastbook 
Practical DL training through projects and experiments 
https://github.com/fastai/fastbook

4) Made With ML 
ML as an engineering system: pipelines, production, deployment, monitoring 
https://github.com/GokuMohandas/Made-With-ML

5) Machine Learning System Design (Chip Huyen) 
How to build ML systems in real business: data, metrics, infrastructure 
https://github.com/chiphuyen/machine-learning-systems-design

6) Awesome Generative AI Guide 
A collection of materials on GenAI: from basics to practice 
https://github.com/aishwaryanr/awesome-generative-ai-guide

7) Dive into Deep Learning (D2L) 
One of the best books on DL + code + assignments 
https://github.com/d2l-ai/d2l-en

Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.

#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS

https://news.1rj.ru/str/CodeProgrammer

Excellent free courses on neural networks from Nvidia— the company decided to share knowledge that usually costs 90 dollars.

Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training.

We gain useful knowledge —
https://developer.nvidia.com/join-nvidia-developer-program

https://news.1rj.ru/str/CodeProgrammer 🌟

⚡️ MIT has released a full course on Deep Learning - for free

MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) — one of the most relevant and practical university courses on modern deep learning.

It includes full-fledged lectures at a top-university level, available for free.

What's in the course

- Fundamentals of deep learning and architectures 
- Transformers and modern models 
- Generative AI 
- Self-supervised learning 
- Scaling laws 
- Diffusion and generative models 
- RL and reinforcement learning 
- Practical analyses of modern approaches 

The lectures are led by MIT professors and researchers working with cutting-edge technologies.

Why it's valuable

This is not a basic course for beginners. 
This is material at the level of:
- ML engineers 
- researchers 
- developers of AI systems 

The course reflects the current state of the industry and explains how people who create modern models think.

It's perfect if you:
- already know Python and the basics of ML 
- want to transition to Deep Learning 
- work with LLMs / AI 
- want a systematic understanding instead of individual tutorials 

If you want FAANG / Research-level knowledge - learn from MIT.

https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/

https://news.1rj.ru/str/CodeProgrammer ✅

10 GitHub Repositories to Master System Design

Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.

Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models.

What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking.

The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community.

In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale.


Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design

https://news.1rj.ru/str/DataScienceM ✅