🚨 26 Claude Code slash commands you should know already. Save this post !

Most Claude Code users are only using 10% of what it can do
Here are the shortcuts that change how you work :

📌 Session control:
→ /compact [instructions]: compresses your conversation, you control what stays
→ /resume [session]: pick up exactly where you left off
→ /branch [name]: explore an alternative path without touching your main session
→ /rewind: roll back to an earlier point in code or conversation

📌 Cost and usage:
→ /cost: see token usage for the session
→ /usage: check your plan limits and rate limit status
→ /extra-usage: keep working even when rate limits hit

📌 Project setup:
→ /init: creates a CLAUDE.md guide for your project
→ /memory: edits CLAUDE.md files, enables or disables auto-memory
→ /add-dir <path>: adds a working directory for file access

📌 Code quality:
→ /diff: interactive diff viewer, uncommitted changes and per-turn diffs
→ /security-review: scans pending changes for vulnerabilities
→ /plan [description]: enters plan mode before Claude writes a single line

📌 Advanced control:
→ /permissions: manages allow, ask, and deny rules for tool use
→ /agents: configures sub-agent behavior
→ /mcp: manages MCP server connections and OAuth
→ /skills: lists all available skills, built-in and custom

Most people open Claude Code and just start typing
The ones shipping faster are using these before they write the first prompt

Which one did you not know about?
♻️ Repost if this helped someone on your team
https://t.me/DataAnalyticsX 🔰

🔖 10 Stanford courses on AI and ML — with official pages and all materials

▶️ CS221: Artificial Intelligence
▶️ CS229: Machine Learning
▶️ CS229M: Theory of Machine Learning
▶️ CS230: Deep Learning
▶️ CS234: Reinforcement Learning
▶️ CS224N: Natural Language Processing
▶️ CS231N: Deep Learning for Computer Vision
▶️ CME295: Large Language Models
▶️ CS236: Deep Generative Models
▶️ CS336: Modeling Language from Scratch

They cover the entire spectrum: classic ML, LLM, and generative models — with theory and practice.

tags: #python #ML #LLM #AI

➡ https://t.me/MachineLearning9

Language Models Interview Handbook

151 Interview Questions, Foundation Roadmaps, Python Examples,
Architecture Diagrams and Production Playbooks for Modern LLM

Help us grow

https://t.me/DataAnalyticsX ✅

If you want to become a Data Analyst or Data Scientist, these are the Python concepts you should master first:

🧹 Data Cleaning
✔️ dropna() → remove missing values
✔️ fillna() → handle nulls properly
✔️ astype() → fix data types
✔️ unique() → explore categories

📊 Exploratory Data Analysis (EDA)
✔️ describe() → quick statistics
✔️ groupby() → analyze patterns
✔️ corr() → find relationships
✔️ hist() & scatter() → visualize distributions

📈 Data Visualization
✔️ bar() → compare categories
✔️ sns.barplot() → statistical plots
✔️ sns.lineplot() → trends over time
✔️ plotly.express.scatter() → interactive charts

https://t.me/DataAnalyticsX 🤩

LLMs are the new operating system for work. 🚀💻

But most people still don’t know the difference between RAG, Embeddings, and Hallucinations. 🤔🧠

Here’s the vocabulary cheat sheet everyone in AI should know 📚✨

These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know 👩‍💼👨‍💻🎨🚀

#LLM #DataScience #AI #ML

https://t.me/DataAnalyticsX 📎

Here’s a NumPy cheat sheet that depicts the 40 most commonly used methods from NumPy 📝🐍

#NumPy #DataAnalytics #AI #math 📊🤖

https://t.me/DataAnalyticsX 🔗📲

🤔 Where do I learn Claude?

🤖 You could've just asked!

🎓 13 free courses.
✅ With certificates.
📡 Straight from the source.

🧠 Claude teaches Claude. Who knew?

Here's the stash: 📚

1️⃣ Claude 101
https://lnkd.in/gCPUQsRg

2️⃣ AI Fluency: Frameworks & Foundations
https://lnkd.in/gS6ceZ_M

3️⃣ Introduction to Agent Skills
https://lnkd.in/g_wWNiEb

4️⃣ Building with the Claude API
https://lnkd.in/gDr5K_B4

5️⃣ Claude Code in Action
https://lnkd.in/g9wWZbK9

6️⃣ Model Context Protocol
https://lnkd.in/gAj5HqMY

7️⃣ MCP: Advanced Topics
https://lnkd.in/g3eDwBFY

8️⃣ AI Fluency for Students
https://lnkd.in/gKKujHGG

9️⃣ AI Fluency for Educators
https://lnkd.in/gVcKnuhA

🔟 Teaching AI Fluency
https://lnkd.in/g9P4gJFM

1️⃣1️⃣ AI Fluency for Nonprofits
https://lnkd.in/gpsm_BVf

1️⃣2️⃣ Claude with Amazon Bedrock
https://lnkd.in/gbfPjSFt

1️⃣3️⃣ Claude with Google Vertex AI
https://lnkd.in/gvVgB4Ub

🚫 No waitlists.
⏳ No countdown timers.
🚫 No "enrollment closes at midnight."

Just click and learn. 🚀

♻️ Repost for everyone still searching for Claude courses!

https://t.me/DataAnalyticsX 🔗

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

🚀 𝐑𝐀𝐆 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐚𝐬 𝐠𝐨𝐨𝐝 𝐚𝐬 𝐲𝐨𝐮𝐫 𝐂𝐇𝐔𝐍𝐊𝐈𝐍𝐆 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲⁣

⁣𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆) 𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐡𝐨𝐰 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬—𝐛𝐮𝐭 𝐡𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐢𝐬𝐬:⁣

⁣👉 𝐓𝐡𝐞 𝐰𝐚𝐲 𝐲𝐨𝐮 𝐬𝐩𝐥𝐢𝐭 𝐲𝐨𝐮𝐫 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 (𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠) 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐞𝐬 𝐡𝐨𝐰 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞, 𝐟𝐚𝐬𝐭, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐲𝐨𝐮𝐫 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦 𝐰𝐢𝐥𝐥 𝐛𝐞.⁣
⁣
💡 𝐁𝐚𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 = 𝐢𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬, 𝐰𝐚𝐬𝐭𝐞𝐝 𝐭𝐨𝐤𝐞𝐧𝐬, 𝐚𝐧𝐝 𝐡𝐢𝐠𝐡𝐞𝐫 𝐜𝐨𝐬𝐭𝐬.⁣
💡 𝐒𝐦𝐚𝐫𝐭 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 = 𝐩𝐫𝐞𝐜𝐢𝐬𝐞 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥, 𝐜𝐨𝐧𝐭𝐞𝐱𝐭-𝐫𝐢𝐜𝐡 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞𝐬, 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲.⁣
⁣
𝐀𝐟𝐭𝐞𝐫 𝐝𝐞𝐞𝐩 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡, 𝐈 𝐩𝐮𝐭 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫 𝐚 𝐠𝐮𝐢𝐝𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐓𝐎𝐏 𝟏𝟓 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰:⁣
⁣
🔹 𝘍𝘪𝘹𝘦𝘥-𝘚𝘪𝘻𝘦 𝘊𝘩𝘶𝘯𝘬𝘪𝘯𝘨 – 𝘴𝘪𝘮𝘱𝘭𝘦, 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘢𝘣𝘭𝘦⁣
🔹 𝘙𝘦𝘤𝘶𝘳𝘴𝘪𝘷𝘦 𝘊𝘩𝘢𝘳𝘢𝘤𝘵𝘦𝘳 𝘚𝘱𝘭𝘪𝘵𝘵𝘪𝘯𝘨 – 𝘧𝘢𝘴𝘵 & 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦⁣
🔹 𝘚𝘦𝘮𝘢𝘯𝘵𝘪𝘤 𝘊𝘩𝘶𝘯𝘬𝘪𝘯𝘨 – 𝘮𝘦𝘢𝘯𝘪𝘯𝘨-𝘣𝘢𝘴𝘦𝘥 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯⁣
🔹 𝘋𝘰𝘤𝘶𝘮𝘦𝘯𝘵-𝘚𝘱𝘦𝘤𝘪𝘧𝘪𝘤 – 𝘭𝘦𝘷𝘦𝘳𝘢𝘨𝘦 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 (𝘗𝘋𝘍𝘴, 𝘏𝘛𝘔𝘓, 𝘔𝘢𝘳𝘬𝘥𝘰𝘸𝘯)⁣
🔹 𝘏𝘪𝘦𝘳𝘢𝘳𝘤𝘩𝘪𝘤𝘢𝘭 – 𝘱𝘢𝘳𝘦𝘯𝘵-𝘤𝘩𝘪𝘭𝘥 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴⁣
🔹 𝘚𝘦𝘯𝘵𝘦𝘯𝘤𝘦-𝘈𝘸𝘢𝘳𝘦 – 𝘳𝘦𝘢𝘥𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘱𝘳𝘦𝘴𝘦𝘳𝘷𝘦𝘥⁣
🔹 𝘛𝘰𝘬𝘦𝘯-𝘉𝘢𝘴𝘦𝘥 – 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘸𝘪𝘵𝘩 𝘓𝘓𝘔 𝘵𝘰𝘬𝘦𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯⁣
🔹 𝘚𝘭𝘪𝘥𝘪𝘯𝘨 𝘞𝘪𝘯𝘥𝘰𝘸 – 𝘰𝘷𝘦𝘳𝘭𝘢𝘱𝘱𝘪𝘯𝘨 𝘤𝘰𝘯𝘵𝘦𝘹𝘵⁣
🔹 𝘛𝘰𝘱𝘪𝘤-𝘉𝘢𝘴𝘦𝘥 – 𝘵𝘩𝘦𝘮𝘢𝘵𝘪𝘤 𝘤𝘭𝘶𝘴𝘵𝘦𝘳𝘪𝘯𝘨⁣
🔹 𝘗𝘳𝘰𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯-𝘉𝘢𝘴𝘦𝘥 – 𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘶𝘯𝘪𝘵 𝘴𝘱𝘭𝘪𝘵𝘴⁣
🔹 𝘊𝘰𝘯𝘵𝘦𝘹𝘵-𝘈𝘸𝘢𝘳𝘦 – 𝘕𝘓𝘗-𝘥𝘳𝘪𝘷𝘦𝘯 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴⁣
🔹 𝘈𝘨𝘦𝘯𝘵𝘪𝘤 – 𝘓𝘓𝘔𝘴 𝘤𝘩𝘶𝘯𝘬 𝘭𝘪𝘬𝘦 𝘩𝘶𝘮𝘢𝘯𝘴⁣
🔹 𝘚𝘮𝘢𝘭𝘭-𝘵𝘰-𝘉𝘪𝘨 – 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 + 𝘤𝘰𝘯𝘵𝘦𝘹𝘵⁣
🔹 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘢𝘭 – 𝘥𝘢𝘵𝘢-𝘥𝘳𝘪𝘷𝘦𝘯 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘪𝘦𝘴⁣
🔹 𝘔𝘰𝘥𝘢𝘭𝘪𝘵𝘺-𝘚𝘱𝘦𝘤𝘪𝘧𝘪𝘤 – 𝘵𝘦𝘹𝘵, 𝘵𝘢𝘣𝘭𝘦𝘴, 𝘪𝘮𝘢𝘨𝘦𝘴, 𝘤𝘰𝘥𝘦⁣
⁣
✨ 𝐏𝐫𝐨 𝐭𝐢𝐩: 𝐓𝐡𝐞𝐫𝐞’𝐬 𝐧𝐨 𝐨𝐧𝐞-𝐬𝐢𝐳𝐞-𝐟𝐢𝐭𝐬-𝐚𝐥𝐥. 𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐮𝐬𝐞 𝐡𝐲𝐛𝐫𝐢𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐚𝐢𝐥𝐨𝐫𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞𝐢𝐫 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐧𝐝 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞.⁣

https://t.me/DataAnalyticsX ⭐️

AI for Data Processing and Analytics 🤖📊

Hex — a platform that helps analyze data through SQL and Python, automating most routine tasks 🚀💻

What it can do: ✨🛠
• generate SQL queries and Python code 💾🧩
• build charts and dashboards 📈📉
• explain results and answer questions in simple language 🗣🧠
• allow you to quickly create a report or a data app 📝📱

Link: https://hex.tech/ 🔗🌐

#DataAnalytics #HexTech #SQL #Python #Automation #DataScience

https://t.me/DataAnalyticsX ✈️

Cheat sheet for working with data in Python (Data Science) 🐍📊

🔹 importing NumPy and pandas libraries — basic tools for data processing 🛠️

🔹 text files — reading/writing plain text and working via context manager 📄

🔹 tabular CSV/flat files — loading and processing structured data into DataFrame 📊

🔹 Excel files — working with sheets and tables 📑

🔹 SAS/Stata files — importing statistical formats 📉

🔹 HDF5 and Pickle — saving and loading complex data structures 💾

🔹 MATLAB files — reading .mat via SciPy 🧮

🔹 relational databases (SQL) — connecting, querying, and converting results into DataFrame 🗄️

🔹 Python dictionaries — accessing keys, values, and nested structures 🔑

🔹 data exploration (NumPy arrays and pandas DataFrames) — viewing types, sizes, and basic statistics 🔍

🔹 file system navigation — magic commands and os module for working with files and directories 📂

#Python #DataScience #Coding #Programming #Tech #Learning

https://t.me/DataAnalyticsX ✅