Machine Learning with Python

Algorithms by Jeff Erickson - one of the best algorithm books out there 📚.

The illustrations make complex concepts surprisingly easy to follow 🎨. Highly recommend this 👍.

Link: https://jeffe.cs.illinois.edu/teaching/algorithms/ 🔗

https://t.me/MachineLearning9

Hugging Face has literally gathered all the key "secrets". 🤔

It's important to understand the evaluation of large language models. 📊

While you're working with language models:
> training or retraining your models, 🔄
> selecting a model for a task, 🎯
> or trying to understand the current state of the field, 🌍

the question almost inevitably arises:
how to understand that a model is good? ❓

The answer is quality evaluation. It's everywhere:
> leaderboards with model ratings, 🏆
> benchmarks that supposedly measure reasoning, 🧠
> knowledge, coding or mathematics, 👨‍💻
> articles with claimed new best results. 📈

But what is evaluation actually? 🤷‍♂️
And what does it really show? 🔍

This guide helps to understand everything. 📚
https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about


What is model evaluation all about 🤖
Basic concepts of large language models for understanding evaluation 🏗️
Evaluation through ready-made benchmarks 📏
Creating your own evaluation system 🔧
The main problem of evaluation ⚠️
Evaluation of free text 📝
Statistical correctness of evaluation 📉
Cost and efficiency of evaluation 💰

https://t.me/CodeProgrammer 🟢

Overfitting and Generalization in Machine Learning

My ML model had 100% accuracy.
And was completely useless.

That's not a paradox; that's overfitting.

The model didn't learn. It memorized.

Here's the mathematical core most tutorials skip:

E[loss] = Bias² + Variance + σ²

→ Bias² = too simple → Underfitting
→ Variance = too complex → Overfitting
→ σ² = irreducible → always there

What this actually means in practice:

→ A degree-9 polynomial on 6 data points hits R² = 1.0 and oscillates wildly between them
→ A linear model on sine-wave data has near-zero variance — but massive bias
→ The optimal model isn't the simplest. Not the most complex. It's the one minimizing Bias² + Variance

And the generalization gap?

Formally defined as:
gen_gap(f) = R(f) − R_emp(f)

When this value is ≫ 0, your model is learning noise, not signal.

The fix isn't "collect more data and hope."
The fix is regularization, which I derive fully in my paper: L1, L2, Dropout, and Early Stopping, all from first principles.

Which regularization strategy do you use most and why?

https://t.me/CodeProgrammer

Most AI engineers never fully understood the maths behind what they build! 🤯🧮

This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨

Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗

What it covers:
- Vectors, linear algebra, calculus, and optimization 📐📉
- Classical machine learning and deep learning 🤖
- Transformer architectures and LLMs 🦄
- Efficient architectures, quantization, and distillation ⚡️
- CUDA, GPU programming, and SIMD 🚀
- AI inference and deployment 🌐

Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗

Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium 🔗

https://t.me/CodeProgrammer

🔖 A huge repository of resources on Data Science 📈

Awesome DataScience — a structured list of open-source data, datasets, libraries, and tutorials for solving real-world problems. 🛠️

It's useful for both beginners and those already familiar with the field — you'll find something new here. 🌱

⛓️ Link to GitHub: https://github.com/academic/awesome-datascience 🔗

tags: #DataScientist 🤖 #AI 🧠 #TechCommunity 🌐 #GrowthMindset 📈 #OpenSource 🏆

▶️ https://t.me/CodeProgrammer 👨‍💻

🔖 3 websites with tasks for improving ML skills

A good selection for those who want to improve their skills in practice, rather than just reading theory:

▶️ Deep-ML — a complete stack from matrices to neural networks;
▶️ Tensorgym — practical exercises in ML;
▶️ NeetCode ML — the ML section from the authors of a well-known platform for preparing for interviews.

tags: #ML #DataScience #DataAnalysis

➡ https://t.me/CodeProgrammer

Machine Learning with Python pinned a photo

💡 Level Up Your IT Career in 2026 – For FREE

Areas covered: #Python #AI #Cisco #PMP #Fortinet #AWS #Azure #Excel #CompTIA #ITIL #Cloud + more

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Here are the 25 ML feature engineering techniques

https://t.me/CodeProgrammer

Softmax vs Hardmax by hand ✍️ ~ interactive calculator 👉 https://byhand.ai/vhUJDH

Softmax turns a set of raw scores (z) into a probability distribution (Y) over choices (a, b, c, d, e). Instead of just saying which option is best, it tells us how likely each option is to be chosen. In this example, most of the probability mass is concentrated on c, while the other options are still possible but clearly less likely. That's the point of softmax: it converts relative scores into meaningful, comparable probabilities that sum to 100%.

Think of a raffle. Hardmax is when the person who bought the most tickets always wins the prize — the top score takes it, every time. Softmax is when everyone's chance is proportional to the tickets they hold: even if I bought just one ticket, I may still get lucky. Who knows. That's the psychology of softmax.

This is how a language model chooses its next word. Each time a word appears in the training data, it earns a ticket. Hardmax would always speak the word with the most tickets — the same safe choice, over and over. Softmax gives every word a chance proportional to its tickets, so less common words can still be spoken. The word with the most tickets still has the highest chance of winning — just not 100%. That's what lets the model surprise us with its creativity (and also its hallucinations) instead of repeating itself.

https://t.me/CodeProgrammer 😱

Register for the FREE Python Demo Session!

📅 Date: 30 April 2026
⏰ Time: 7:30 PM
🔗 Zoom Link: https://us06web.zoom.us/meeting/register/HSOTmzzpTkGIGm9C9oGbaA

Everyone is welcome!

https://t.me/CodeProgrammer

Searched 35 free courses, so you don't have to! 🔍✨

Here are the 35 best free courses: 🎓

1. Data Science: Machine Learning 🤖
Link: https://lnkd.in/gUNVYgGB

2. Introduction to computer science 💻
Link: https://lnkd.in/gR66-htH

3. Introduction to programming with scratch 🧩
Link: https://lnkd.in/gBDUf_Wx

4. Computer science for business professionals 💼
Link: https://lnkd.in/g8gQ6N-H

5. How to conduct and write a literature review 📝
Link: https://lnkd.in/gsh63GET

6. Software Construction 🛠
Link: https://lnkd.in/ghtwpNFJ

7. Machine Learning with Python: from linear models to deep learning 🐍🧠
Link: https://lnkd.in/g_T7tAdm

8. Startup Success: How to launch a technology company in 6 steps 🚀
Link: https://lnkd.in/gN3-_Utz

9. Data analysis: statistical modeling and computation in applications 📊
Link: https://lnkd.in/gCeihcZN

10. The art and science of searching in systematic reviews 🔎
Link: https://lnkd.in/giFW5q4y

11. Introduction to conducting systematic review 📋
Link: https://lnkd.in/g6EEgCkW

12. Introduction to computer science and programming using python 🖥
Link: https://lnkd.in/gwhMpWck

13. Introduction to computational thinking and data science 💡
Link: https://lnkd.in/gfjuDp5y

14. Becoming an Entrepreneur 💸
Link: https://lnkd.in/gqkYmVAW

15. High-dimensional data analysis 📈
Link: https://lnkd.in/gv9RV9Zc

16. Statistics and R 📉
Link: https://lnkd.in/gUY3jd8v

17. Conduct a literature review 📚
Link: https://lnkd.in/g4au3w2j

18. Systematic Literature Review: An Introduction 🧐
Link: https://lnkd.in/gVwGAzzY

19. Introduction to systematic review and meta-analysis 🧮
Link: https://lnkd.in/gnpN9ivf

20. Creating a systematic literature review ✍️
Link: https://lnkd.in/gbevCuy6

21. Systematic reviews and meta-analysis 📊
Link: https://lnkd.in/ggnNeX5j

22. Research methodologies 🕵️‍♂️
Link: https://lnkd.in/gqh3VKCC

23. Quantitative and Qualitative research for beginners 📊💬
Link: https://shorturl.at/uNT58

24. Writing case studies: science of delivery 📑
Link: https://shorturl.at/ejnMY

25. research methodology: complete research project blueprint 🗺
Link: https://lnkd.in/gFU8Nbrv

26. How to write a successful research paper 📜
Link: https://lnkd.in/g-ni3u5q

27. Research proposal bootcamp: how to write a research proposal 🏃‍♂️
Link: https://lnkd.in/gNRitBwX

28. Understanding technology 📱
Link: https://lnkd.in/gfjUnHfd

29. Introduction to artificial intelligence with Python 🤖🐍
Link: https://lnkd.in/gygaeAcY

30. Introduction to programming with Python 💻
Link: https://lnkd.in/gAdyf6xR

31. Web programming with Python and JavaScript 🌐
Link: https://lnkd.in/g_i5-SeG

32. Understanding Research methods 🔬
Link: https://lnkd.in/g-xBFj4v

33. How to write and publish a scientific paper 📢
Link: https://lnkd.in/giwTe2is

34. Introduction to systematic review and meta-analysis 📊
Link: https://lnkd.in/gnpN9ivf

35. Research for impact 🌍
Link: https://lnkd.in/gRsWsUsq

GitHub repositories to enhance your Python proficiency:

- Web development with Django — https://github.com/django/django
- Data Science tools — https://github.com/rasbt/python-machine-learning-book
- Algorithmic challenges — https://github.com/TheAlgorithms/Python
- Machine learning recipes — https://github.com/ageron/handson-ml2
- Testing best practices — https://github.com/pytest-dev/pytest
- Automation scripts — https://github.com/soimort/you-get
- Advanced Python concepts — https://github.com/faif/python-patterns

Bookmark and share
https://t.me/CodeProgrammer 🌟

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Self Attention vs Cross Attention by hand ✍️
Resize the matrices yourself 👉 https://byhand.ai/aMisxP

Two attention mechanisms, side by side. Both project X into queries; both compute attention via S = Kᵀ × Q and F = V × A. The only difference is the source of K and V.

Self attention uses X for everything. Q, K, and V all come from projecting X. Each X token attends to every other X token. The score matrix S is square — 128 × 128.

Cross attention uses X for queries and a second sequence E for keys and values. Each X token attends to every E token instead. The score matrix S is rectangular — 64 × 128.

Notice what's shared and what's not:

X is the same in both — same 36 × 128 input.

Q and K share the 16 dimension — that's what makes the dot product Kᵀ × Q valid in either case.

V dimensions are independent: self-attention uses 12, cross-attention uses 12. The choice doesn't depend on which mechanism you're using; it depends on what output dimension your downstream layer expects.

https://t.me/CodeProgrammer

11 Plots Data Scientists Use 90% of the Time 📊🚀

Here’s the secret → Data scientists don’t actually use 100+ types of charts. 🤫

When real decisions are on the line, it always comes back to the same 11.

https://t.me/DataScienceM

Most AI channels optimize for attention.
We optimize for signal.

• real tools
• reproducible workflows
• technical breakdowns

If you care about depth, not hype
✅ this is for you.

🔣 Join the channel

LLM Engineering Roadmap (2026 Practical Guide) 🗺✨

If your goal is to build real LLM apps (not just prompts), follow this order. 🚀

1️⃣ Python + APIs 🐍🔌

You’ll spend most of your time wiring systems.

Learn:
→ functions, classes
→ working with APIs (requests, JSON)
→ async basics
→ environment variables

Resources
→ Python for Everybody
https://lnkd.in/gUqkvnGG
→ Introduction to Python
https://lnkd.in/g7xfYJVZ
→ MLTUT Python Basics Course
https://lnkd.in/gCqfyCGZ

2️⃣ Text Basics (NLP) 📝🧠

You don’t need heavy theory, just the essentials.

Learn:
→ tokenization
→ text cleaning
→ similarity (cosine)
→ basic embeddings idea

Resources
→ Natural Language Processing Specialization
https://lnkd.in/gz_xmqD9
→ NLP in Python
https://lnkd.in/gnpcJxhz

3️⃣ Transformers (What’s happening behind the API) 🤖🔍

Enough to not treat it like a black box.

Learn:
→ tokens, context window
→ attention (high level)
→ why embeddings work
→ limits of LLMs

Resources
→ Generative AI with Large Language Models
https://lnkd.in/gk3PPtyf
→ Hugging Face Transformers Course
https://lnkd.in/ggSR5JNb

4️⃣ Prompting (Make outputs reliable) 💬🎯

Treat prompts like code.

Learn:
→ few-shot examples
→ structured outputs (JSON)
→ system vs user instructions
→ simple evals (does it break?)

Resources
→ Prompt Engineering for ChatGPT
https://lnkd.in/gyg4EiJS
→ Prompt Engineering with LLMs
https://lnkd.in/gn67Mxga

5️⃣ Embeddings + Vector DBs 📊🗄

This is how you add your data.

Learn:
→ embedding generation
→ similarity search
→ indexing
Tools:
→ FAISS
→ Pinecone
→ Chroma

Resources
→ Working with Embeddings
https://lnkd.in/gnngPW4E
→ Vector Databases & Semantic Search
https://lnkd.in/gP2HdMmD

6️⃣ RAG Pipelines 🔗🔄

Most useful apps use this pattern.

Learn:
→ chunking documents
→ retrieval + ranking
→ prompt + context design
→ basic evaluation

Resources
→ Generative AI for Software Development
https://lnkd.in/g3uduecv
→ Build RAG Apps with LangChain
https://lnkd.in/ggXJjgDN

7️⃣ Build Real Applications 🛠💻

Keep them small and usable.

Build:
→ document Q&A (PDF → answers)
→ internal knowledge bot
→ code assistant (repo Q&A)
→ support chatbot

Tools:
→ LangChain
→ LlamaIndex
→ OpenAI APIs

Resources
→ Build LLM Apps with LangChain & Python
https://lnkd.in/g6xXVX_8
→ LLM Applications
https://lnkd.in/gzs8_SRk

8️⃣ Deployment 🚢☁️

Make it usable by others.

Learn:
→ FastAPI endpoints
→ streaming responses
→ caching (reduce cost)
→ logging + monitoring

Tools:
→ FastAPI
→ Docker
→ AWS / GCP

Resources
→Machine Learning Engineering for Production (MLOps)
https://lnkd.in/gCMtYSk5
→ MLOps Fundamentals
https://lnkd.in/g8TGrUzT

https://t.me/DataAnalyticsX ✅

Machine Learning with Python pinned a photo