Machine Learning

The 25 Best Free Resources for Learning AI in 2026 Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers. Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

🔖 637 interactive modules on ML and AI On VizLearn, you can visually explore: 🫡 Gradient descent; 🫡 SVM and PCA; 🫡 Bayes' theorem; 🫡 BPE tokenization; 🫡 Query, Key, and Value; 🫡 KV cache; 🫡 Quantization; 🫡 Neural networks and LLMs. Change the input data and immediately see the results of the calculations. ⛓ Link to the tutorial https://vizlearn.in/

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Agentic AI is the future. If you want to become god-level with AI agents, save these 10 GitHub repos: 1 Awesome LLM Apps 🔗 https://github.com/Shubhamsaboo/awesome-llm-apps 2 Generative AI for Beginners 🔗 https://github.com/microsoft/generative-ai-for-beginners 3 LLM Course 🔗 https://github.com/mlabonne/llm-course 4 Prompt Engineering Guide 🔗 https://github.com/dair-ai/Prompt-Engineering-Guide 5 AI Agents for Beginners 🔗 https://github.com/microsoft/ai-agents-for-beginners 6 AI Agents Projects 🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects 7 Hugging Face Agents Course 🔗 https://github.com/huggingface/agents-course 8 Awesome AI Agents 🔗 https://github.com/e2b-dev/awesome-ai-agents 9 GenAI Agents 🔗 https://github.com/NirDiamant/GenAI_Agents 10 Agents Towards Production 🔗 https://github.com/NirDiamant/agents-towards-production Anything else you'd add?

📚 7 Best Websites to Learn Computer Science Subjects for Free More: https://enghusseinsheikho.substack.com/p/7-best-websites-to-learn-computer?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

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You can literally enter a sentence and see how the Transformer processes it. This interactive tool runs a GPT-like model directly in your browser and allows you to trace the entire process: Tokenization → Embeddings → Q/K/V → Attention Mechanism → MLP → Logits → Probabilities of the next token. You can explore attention maps, intermediate values, and adjust parameters like temperature, top-k, and top-p to see how they affect the generation. If Transformers still seem like a collection of blocks connected by arrows, you should definitely try this. https://poloclub.github.io/transformer-explainer/ https://t.me/DataAnalyticsX

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Tracking experiments and versioning ML models with MLflow. 🤖 Machine learning development requires saving hyperparameters, metrics, and artifacts for each run to compare results. The MLflow platform logs key metrics and registers trained models in a central registry. We will install MLflow, run a script to log parameters, and register the model. Let's install the

mlflow

and

scikit-learn

libraries to conduct and log a test experiment. 📦

pip install mlflow scikit-learn

The dependencies for managing ML experiments have been successfully installed. ✅ Now, let's create a Python script called

train.py

that will train a simple model, log metrics, and save it to MLflow. 🐍

import mlflow
from sklearn.ensemble import RandomForestClassifier

mlflow.set_experiment("demo_experiment")
with mlflow.start_run():
    params = {"n_estimators": 100, "max_depth": 5}
    mlflow.log_params(params)
    model = RandomForestClassifier(**params)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.sklearn.log_model(model, "rf_model")

The training script and metric logging are set up and ready to be executed. 🚀 Let's run the training script to capture the results and parameters in the local MLflow storage.

python3 train.py

The experiment has been successfully completed, and the parameters and model artifacts have been saved. 📊

# verification (check for registered runs in MLflow)
mlflow runs list --experiment-name demo_experiment

Expected output:

demo_experiment ... FINISHED

# cleanup (remove the generated directory with artifacts and the script)
rm -rf mlruns train.py

Using MLflow helps avoid chaos when tuning hyperparameters and ensures reproducibility of results. Deploy an MLflow server on a separate host for the entire team to collaborate on the project. 🌐 #MLflow #MachineLearning #Python #DataScience #MLOps #AI ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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