This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳

It includes:
✅ Supervised & Unsupervised algorithms
✅ Regression, Classification & Clustering techniques
✅ PCA & Dimensionality Reduction
✅ Neural Networks, CNN, RNN & Transformers
✅ Assumptions, Pros/Cons & Real-world use cases

Whether you're:
🔹 Preparing for data science interviews
🔹 Working on ML projects
🔹 Or strengthening your fundamentals
this one-page guide is a must-save.

♻️ Repost and share with your ML circle.

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https://t.me/CodeProgrammer 🐍

🔥 Convolutional Neural Networks: Clearly explained!

🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.

➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network.

🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved.

📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection.

⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance.

Want to learn more about CNNs?

Then, check out super-detailed article about it. 👇
https://lnkd.in/eyA_DnYj

https://t.me/CodeProgrammer 🧠

Machine Learning Specialization — Study Notes & Labs 📚🔬

Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng. 🧑‍🎓

📂 Repo: https://github.com/TruongDat05/machine-learning-notes-and-code

https://t.me/CodeProgrammer 📁

Automate research with NotebookLM + Python 🤖🐍

Notebooklm-py — is a unofficial library for working with Google NotebookLM,📚🧠 which allows automating research tasks, generating content, and connecting AI agents. Suitable for prototypes, pet projects, and personal tools — works both via Python and CLI ⌨️

What it can do:
• integration with AI agents and Claude Code 🤖
• automatic import and processing of sources 📥
• generation of podcasts, videos, and educational materials 🎙️🎥📖
• working via Python API and command line 💻
• using unofficial Google APIs 🔧
https://github.com/teng-lin/notebooklm-py

https://t.me/CodeProgrammer 📱

Google Gemma 4's pre-training is completely free

All you need is a browser and access to more than 500 models to choose from.

The process is simple:

1. Open the notebook of Unsloth in Colab
2. Select a model and a dataset
3. Start the trainin

Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb

It's done 😂

👉 https://t.me/MachineLearning9

Stop. I found “BTTS NO” picks that aren’t hype

Recently I stayed up watching a “sure goal” match… and it ended 0–0 😅⚽ That’s when I quit noisy tipsters and started tracking tight defences & low xG myself 📉🧤

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I spent weeks filtering stats so you don’t have to 💸🎯

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