Free Certification Courses to Learn Data Analytics in 2025:

1. Python
🔗 https://imp.i384100.net/5gmXXo

2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql

3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r

4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics

5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

8. R
🔗https://imp.i384100.net/rQqomy

9. Tableau
🔗https://imp.i384100.net/MmW9b3

10. PowerBI
🔗 https://lnkd.in/dpmnthEA

11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N

12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science

13. Mathematics
🔗http://matlabacademy.mathworks.com

14. Statistics
🔗 https://lnkd.in/df6qksMB

15. Data Visualization
🔗https://imp.i384100.net/k0X6vx

16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9

17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR

18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling

20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra

23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY

26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2

27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

🗂 A fresh deep learning course from MIT is now publicly available

A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.

The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.

➡️ Link to the course

tags: #Python #DataScience #DeepLearning #AI

Assembling GPT-like LLMs from scratch on PyTorch 🔥

https://github.com/analyticalrohit/llms-from-scratch

📚 10 notebooks. Step-by-step explanation.

🧩 Breaks down the architecture of LLMs into simple parts.

✅ Suitable for beginners.

🛠 Completely hands-on.

#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇

AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖

It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨

Key features: 🌟

• TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥
• Books section – lists AI/ML/DL books with short notes on where each one helps 📚
• Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓
• Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️
• Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄

Free public GitHub repo. 🆓

https://github.com/ArturoNereu/AI-Study-Group

#AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Create an LLM from Scratch!

I came across a great find from Vizuara — a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. 🧠✨

Most people use ChatGPT.
But only a few actually understand how it works under the hood. ⚙️

This playlist step by step breaks down all the key concepts without overloading with complex explanations.

📚 What you will learn:
→ The architecture of Transformer 🏗️
→ The internal structure of GPT
→ Tokenization and BPE 🧩
→ Attention mechanisms 🔍
→ The process of training an LLM 📈
→ Full implementations in Python 🐍

✅ Suitable for:
• ML engineers
• AI enthusiasts
• Developers entering the GenAI field
• Anyone who is tired of explaining AI as a "black box" 🕵️

If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini — this material is worth watching. 👀

🔗 Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu

#LLM #AI #MachineLearning #Python #GenAI #DeepLearning

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

The only LLM cheat sheet you'll ever need 🚀

Covers the main concepts, architectures, and practical applications.

### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)

### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)

### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)

### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech

Stop studying LLM from random articles and videos that only explain individual pieces of the puzzle.

📚 LLM from Scratch — this is a practical course on PyTorch for those who want to understand the entire path of modern LLMs: from the first Transformer block to RLHF.

Instead of endless theory, here we gather a complete model training chain:

🔹 Pretraining → Finetuning → Alignment in one course
🔹 Transformer from scratch: positional embeddings, self-attention, multi-head attention, MLP, residual connections, LayerNorm, and full Transformer blocks
🔹 Own training loop without Trainer magic: tokenization, batches, cross-entropy, validation loss, text generation
🔹 Modern architecture improvements: RMSNorm, RoPE, SwiGLU, KV Cache, sliding-window attention, and streaming cache
🔹 Full section on alignment: SFT, reward models, PPO-style RLHF, and GRPO with an analysis of how it looks in the training loop in practice

https://github.com/vivekkalyanarangan30/llm_from_scratch

#LLM #PyTorch #MachineLearning #DeepLearning #AI #Transformer

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Google has published a free guide on scaling AI models and working with GPUs. 🚀

📘 How to Scale Your Model
https://jax-ml.github.io/scaling-book/

📘 How to Think About GPUs
https://jax-ml.github.io/scaling-book/gpus/

The materials discuss the principles of model scaling, the structure of GPUs, computational limitations, memory bandwidth, parallelism, and other topics that are useful when training and running modern AI models. 💡

It's completely free and available online. 🌐

#AI #MachineLearning #GPU #Scaling #DeepLearning #Tech

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

A large collection of materials on LLM Systems,

• model training (pre-training, RLHF, fault tolerance, stragglers)
• inference and serving
• agent systems
• edge deployment
• multimodal models
• technical reports from major laboratories
• reviews, benchmarks, and leaderboards
• courses on MLSys and collections of articles from conferences

https://github.com/AmberLJC/LLMSys-PaperList

#LLMSys #LLM #MachineLearning #AIResearch #DeepLearning #TechReports

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

🎓 A Free AI Course for Beginners by Microsoft

For those just getting into artificial intelligence, Microsoft offers a free course.

It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.

The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.

Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.

➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md

What's your AI level right now?

❤️ — Advanced user
🔥 — Almost zero

#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

🔖 Building our own GPT-like model in PyTorch

We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.

Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.

GitHub: https://github.com/analyticalrohit/llms-from-scratch

#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Attention Heatmap vs Token Pruning 🔍✂️

🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms

#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Exporting the model from PyTorch to the universal ONNX format for independent inference 🚀

Deploying PyTorch models in production often requires installing a large framework and relying on a Python environment. The ONNX (Open Neural Network Exchange) format converts the computation graph into an intermediate binary format suitable for running on any device and programming language. We will export the PyTorch neural network and run its inference using the lightweight ONNX Runtime. 🛠

To export and run the neural network, we will install the PyTorch framework, the ONNX library, and the cross-platform ONNX Runtime engine.

pip install torch onnx onnxruntime

The packages for converting and high-performance execution of graphs have been successfully installed. ✅

We will write a Python script that creates a test PyTorch model, exports it to a .onnx file, and immediately performs a verification of the output.

import torch, torch.nn as nn, onnxruntime as ort, numpy as np

model = nn.Sequential(nn.Linear(10, 5), nn.ReLU())
x = torch.randn(1, 10)
torch.onnx.export(model, x, "model.onnx", input_names=["input"], output_names=["output"])

session = ort.InferenceSession("model.onnx")
res = session.run(None, {"input": x.numpy()})
print("ONNX Output shape:", res[0].shape)

The model graph has been successfully serialized into a binary file, and the runtime performed the prediction without involving PyTorch. 📦

# verification (checks the correctness and structure of the saved ONNX model)

python3 -c "import onnx; model = onnx.load('model.onnx'); onnx.checker.check_model(model); print('ONNX Model Status: Valid')"

Expected output: ONNX Model Status: Valid

# cleanup (deletes the generated model file and cleans up binaries)

rm -f model.onnx

Converting neural networks to ONNX allows you to decouple inference from Python and run models in C++, Rust, Go, or directly in a web browser. Be sure to specify the names of the input and output tensors when exporting to simplify integration with the service. 💻

#PyTorch #ONNX #MachineLearning #DeepLearning #AI #DevOps

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

One of the key moments when I truly understood how transformers work: 🧠✨

"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one."

"Start thinking of it as a residual flow." 🌊

Each block in a transformer has a residual connection that adds the input of the block to its output:

x' = f(x) + x

Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕

Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions.

Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗

I owe these ideas to an older article by Anthropic about the architecture of transformers:
transformer-circuits.pub/2021/framework

#Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

I thought I understood how GPU loading works until I started writing this guide. 🤔

I was constantly confusing three different concepts:

• The amount of work we are launching.
• The amount of work that is actually placed on the GPU.
• The amount of work that is ready to be executed right now.

But these are completely different things. ⚠️

Consider a CUDA kernel with 240 blocks, each containing 256 threads. This results in 61,440 logical threads, or 1920 warps. However, the number of launched threads doesn't tell you how many warps can actually be present on the GPU simultaneously. 🧮

Blocks are placed on streaming multiprocessors (SMs) based on the available registers, shared memory, and limitations on the number of threads, warps, and blocks.

Even after being placed on an SM, a warp can be waiting for a memory operation to complete, a dependency between arithmetic operations to be resolved, or for synchronization. The scheduler can only select warps that are actually ready to issue instructions for execution. ⏳

This changed my approach to analyzing performance metrics. 📊

Occupancy is a measure of the ratio of the number of active warps on an SM to the maximum number of warps that the SM can support.

GPU utilization measures something different. In NVML, this metric reflects the percentage of the measurement interval during which at least one CUDA kernel was running.

Therefore, the GPU can show 100% utilization even if the Tensor Cores are not being used to their full potential, the HBM bandwidth is not saturated, and performance is limited by one of the system components. At the same time, a significant portion of the GPU's resources may remain underutilized. 📉

Similar misconceptions arise with memory. Registers, shared memory, L1, L2, and HBM are not simply levels of memory that differ in speed. Each has its own areas of accessibility, capacity, management characteristics, and access methods. 💾

If a model fits in HBM, it only means that there is enough video memory for it. But it tells you very little about how many bytes need to be transferred, how efficiently memory accesses are combined, how often data is reused, and whether memory is actually limiting the performance of the CUDA kernel.

A good example is FlashAttention. This algorithm calculates the exact attention mechanism, but uses tiling and repeated computations to reduce the amount of data transferred between HBM and the GPU's internal memory. ⚡️

Tensor Cores turned out to be another technology that I previously understood too simplistically.

These are specialized computational units for matrix multiplication operations with accumulation, which play a huge role in AI tasks. However, they do not "execute the model" as a whole. 🧠

Reduction operations, indexing, element-wise calculations, synchronization, and data movement still involve other components of the GPU. In addition, launching CUDA kernels also introduces overhead.

Over time, I stopped asking myself: "Why isn't the GPU 100% utilized?" Instead, I started asking a more accurate question: "What is currently limiting the speed of performing useful work?" 🤔

Ultimately, all of this turned into a 42-page guide. 📄

It covers streaming multiprocessors (SMs), warps, and schedulers, the memory hierarchy and latency hiding, Tensor Cores, the distribution of GEMM computations across GPU hardware resources, numerical precision, and performance metrics that are very easy to misinterpret.

https://drive.google.com/file/d/17tyvxr__8vZ6o995GNq6I5PC5JOiv0ql/view

#GPU #CUDA #AI #DeepLearning #PerformanceOptimization #TechGuide

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A