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Python | Machine Learning | Coding | R

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Latest Posts

Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

📷 Photo

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.

#MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML

🐍

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Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

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Master Python the Right Way – Without Procrastination. 🐍✨

When I first started learning Python, I quickly realized:

You can't master a programming language just by reading syntax or watching tutorials. 📚🚫

Real growth happens when you practice, build, and solve problems on your own. 🛠💻

That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠

What is this collection about? 🤔

✔️ Beginner to advanced programs with clear explanations
✔️ Pattern-based exercises to strengthen core fundamentals
✔️ Problem-solving programs that sharpen logical thinking

Why is this important? 🌟

You don't just learn "how to code", you start learning "how to think like a programmer". 🧠⚡️

This is perfect for: 🎯

• Preparing for technical interviews 🤝
• Participating in coding challenges 🏆
• Building real-world Python projects 🚀

https://t.me/pythonRe

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Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

🎥 Video

Stop asking "CNN or VLM?" — the answer is both. 🤔

Everyone's talking about Vision Language Models replacing traditional computer vision. 📢
Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀
CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯
Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠
The smartest production systems combine both:
→ A lightweight CNN runs first (fast, cheap) ⚡️
→ A VLM handles the complex reasoning (flexible, expensive) 💎
This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣
Dr. Satya Mallick breaks it down in under 2 minutes. 👇
#ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering

✅

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Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

🎥 Video

🧐 Python Cheatsheet — a convenient cheat sheet for Python that really saves time at work!

The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code.

Repo: https://github.com/onyxwizard/python-cheatsheet

👩‍💻

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Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

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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

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Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

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🧐 Confusion Matrix: Less confusing 🤯

Many data science beginners struggle to understand true negative (TN), false negative (FN), false positive (FP), and true positive (TP). 🤔

You can easily understand the values using the confusion matrix. 📊

💡 It is a 2x2 matrix for a binary classifier:

- True Negative (TN): True Negative prediction ✅
- False Negative (FN): False Negative prediction ❌
- False Positive (FP): False Positive prediction 🚨
- True Positive (TP): True Positive prediction 🎯

❓ For each prediction, ask two questions:
1. Did the model do it right? Yes (True) or No (False)
2. What was the predicted class? Positive or Negative

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Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

Hugging Face has literally https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about 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 https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about 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 💰

🟢

4,150

Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

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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 🔗

Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

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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?

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Python | Machine Learning | Coding | R

May 18, 2026, 12:44 PM

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🔖 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 🏆

▶️ 👨‍💻

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Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

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🐼 Cheat Sheet on Data Wrangling — for everyone who works with Pandas

Everything you need is collected in one file: creating and merging DataFrames, filtering, grouping, handling missing values, and visualization.

It's convenient when you need to quickly refresh your syntax and don't want to dig into the documentation.

The cheat sheet in good quality
https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

tags: #useful

For more please ❤️

➡

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Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

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2,440

Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

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RAG won't work in 2026 if you're still using old approaches.

Yes, many companies are still failing with RAG — not because they're doing it wrong, but because they're stuck on outdated techniques.

Here's what usually happens: most companies start with a chatbot / chat app when talking about AI implementation. And here RAG becomes key — to connect their data via a database and enable the chat app to retrieve relevant documents.

But today, RAG is no longer limited to just chats. The applications of RAG are practically limitless, and that's a good thing.

RAG still remains the foundation for everything you build on LLMs and AI agents. The only thing that's changed is the RAG techniques themselves. The old approach no longer works — more advanced techniques are needed, what's now called advanced RAG.

The essence of RAG is to enrich the system with your data via a database so it can find relevant documents or their parts. The results are simple and often "okay", especially if the documents are well-structured and there aren't many of them.

But when the documents are unstructured and it's important to get not just accurate documents but also the right context, advanced techniques come into play:

- query decomposition
- metadata enrichment
- hybrid indexing
- reranking
- context fusion

These approaches allow the RAG system to find and generate more accurate and contextually relevant answers.

Therefore, advanced RAG is important. RAG isn't dead and can't die. Just use smarter techniques.

Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

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💾 TensorTonic — a platform with 200+ algorithms and tasks for ML developers

What's inside:
▶️ Analysis of research and step-by-step reproduction of model architectures;
▶️ Explanation of topics and concepts with interactive visualizations;
▶️ A progress and achievement system — what would we do without gamification.

A great option to hone your ML skills in the evening ⚡

https://www.tensortonic.com/

2,650

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Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

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Classical filters & convolution: The heart of computer vision

Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.

More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

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Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

https://t.me/philgodlewskii4

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Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

📱 Python enthusiasts, this is for you — 15 BEST REPOSITORIES on GitHub for learning Python

▶️ Awesome Python — https://github.com/vinta/awesome-python
— the largest and most authoritative collection of frameworks, libraries, and resources for Python — a must-save

▶️ TheAlgorithms/Python — https://github.com/TheAlgorithms/Python
— a huge collection of algorithms and data structures written in Python

▶️ Project-Based-Learning — https://github.com/practical-tutorials/project-based-learning
— learning Python (and not only) through real projects

▶️ Real Python Guide — https://github.com/realpython/python-guide
— a high-quality guide to the Python ecosystem, tools, and best practices

▶️ Materials from Real Python — https://github.com/realpython/materials
— a collection of code and projects for Real Python articles and courses

▶️ Learn Python — https://github.com/trekhleb/learn-python
— a reference with explanations, examples, and exercises

▶️ Learn Python 3 — https://github.com/jerry-git/learn-python3
— a convenient guide to modern Python 3 with tasks

▶️ Python Reference — https://github.com/rasbt/python_reference
— cheat sheets, scripts, and useful tips from one of the most respected Python authors

▶️ 30-Days-Of-Python — https://github.com/Asabeneh/30-Days-Of-Python
— a 30-day challenge: from syntax to more complex topics

▶️ Python Programming Exercises — https://github.com/zhiwehu/Python-programming-exercises
— 100+ Python tasks with answers

▶️ Coding Problems — https://github.com/MTrajK/coding-problems
— tasks on algorithms and data structures, including for preparation for interviews

▶️ Projects — https://github.com/karan/Projects
— a list of ideas for pet projects (not just Python). Great for practice

▶️ 100-Days-Of-ML-Code — https://github.com/Avik-Jain/100-Days-Of-ML-Code
— machine learning in Python in the format of a challenge

▶️ 30-Seconds-of-Python — https://github.com/30-seconds/30-seconds-of-python
— useful snippets and tricks for everyday tasks

▶️ Geekcomputers/Python — https://github.com/geekcomputers/Python
— various scripts: from working with the network to automation tasks

React ♥️ for more posts like this 💛

Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

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🗂 Harvard has released a textbook on ML systems — from autograd to production

Not just another "what is a neural network" course — this is about how to build combat-ready ML systems around models.

What's inside:
▶️ Building autograd, optimizers, attention, and mini-PyTorch from scratch;
▶️ Batches, computational accuracy, architectures, and training;
▶️ Performance optimization, hardware acceleration, and benchmarking.

You can read the book and the code for free right now.

✈️ Link to GitHub
https://github.com/harvard-edge/cs249r_book

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Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

📎 File

📱 A huge cheat sheet for Python, Django, Plotly, Matplotlib, Pygame

Many topics are covered inside:
🔸 All basic constructs: variables, conditions, loops, lists, dictionaries, functions, and classes — with clear examples;

🔸 Working with files, exceptions, and data input — understandable even for beginners;

🔸 #Django, #Pygame, #Matplotlib, and #Plotly — brief instructions on how to get started with each of the frameworks;

🔸 Tips on #Git, project structure, and unit testing.

❤️

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Python | Machine Learning | Coding | R

Apr 2, 2026, 10:52 AM

🎥 Video

Visualizing the complexity of algorithms

✅

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Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

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

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Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

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📱 TorchCode — a PyTorch training tool for preparing for ML interviews

40 tasks for implementing operators and architectures that are actually asked in interviews. Automatic checking, hints, and reference solutions — all in the browser without installation.

If you're preparing for an ML interview, it's useful to go through at least half of them.

Link: https://github.com/duoan/TorchCode

tags: #useful #pytorch

✅

2,810

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Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

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Do you want to understand the methods used to train LLMs?

The training of large language models (LLMs) is based on various approaches that help models understand and generate text.

Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.

Here are 4 common methods of training LLMs in simple language 👇

1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.

2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.

3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".

4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.

These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.

Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live.
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other.

Vision Transformers threw that whole approach out.

ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence.
Every patch can attend to every other patch from the very first layer. No stacking required.

That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬:

- Introduction to Vision Transformers and comparison with CNNs
- Adapting transformers to images: patch embeddings and flattening
- Positional encodings in Vision Transformers
- Encoder-only structure for classification
- Benefits and drawbacks of ViT
- Real-world applications of Vision Transformers
- Hands-on: fine-tuning ViT for image classification

The Image below shows

Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face.

The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out.

Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps.

The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images.

𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬
ViT paper dissection
https://youtube.com/watch?v=U_sdodhcBC4

Build ViT from Scratch
https://youtube.com/watch?v=ZRo74xnN2SI

Original Paper
https://arxiv.org/abs/2010.11929

Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

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🎁 23 Years of SPOTO – Claim Your Free IT Certs Prep Kit!

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6,940

Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

🎥 Video

🤖 Python libraries for AI agents — what to study

If you want to develop AI agents in Python, it's important to understand the order of studying libraries.

Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas.

The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows.

The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines.

LangChain — simple agents, tools, and memory
http://github.com/langchain-ai/langchain

CrewAI — multi-agent systems with roles
http://github.com/joaomdmoura/crewAI

SmolAgents — lightweight agents for quick experiments
http://github.com/huggingface/smolagents

LangGraph — orchestration and stateful workflow
http://github.com/langchain-ai/langgraph

LlamaIndex — RAG and knowledge-agents
http://github.com/run-llama/llama_index

Semantic Kernel — AI workflow and plugins
http://github.com/microsoft/semantic-kernel

AutoGen — autonomous multi-agent systems
http://github.com/microsoft/autogen

DSPy — optimizing LLM pipelines
http://github.com/stanfordnlp/dspy

A2A — protocol for interaction between agents
http://github.com/a2aproject/A2A

🌟

Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

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The Python + Generative AI series by Azure AI Foundry has ended, but all materials are open

Now you can calmly rewatch the recordings, download the slides, and try the code from each session — from LLM and RAG to AI agents and MCP.

All resources are here: http://aka.ms/pythonai/resources

👉

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Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

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PhD Students - Do you need datasets for your research?

Here are 30 datasets for research from NexData.

Use discount code for 20% off: G5W924C3ZI

1. Korean Exam Question Dataset for AI Training

https://lnkd.in/d_paSwt7

2. Multilingual Grammar Correction Dataset

https://lnkd.in/dV43iqTp

3. High quality video caption dataset

https://lnkd.in/dY9kxkhx

4. 3D models and scenes datasets for AI and simulation

https://lnkd.in/dT-zscH4

5. Image editing datasets – object removal, addition & modification

https://lnkd.in/dd8iCGMS

6. QA dataset – visual & text reasoning

https://lnkd.in/dc3TNWFD

7. English instruction tuning dataset

https://lnkd.in/dTeTgd2M

8. Large scale vision language dataset for AI training

https://lnkd.in/dBJuxazN

9. News dataset

https://lnkd.in/dYBJe5gd

10. Global building photos dataset

https://lnkd.in/dVJsDXnC

11. Facial landmarks dataset

https://lnkd.in/dz_KGCS4

12. 3D Human Pose & Landmarks dataset

https://lnkd.in/dXE9ir8Z

13. 3D Hand Pose & Gesture Recognition dataset

https://lnkd.in/d_QdGGb9

14. 14. Driver monitoring dataset – dangerous, fatigue

https://lnkd.in/d6kF-9PW

15. Japanese handwriting OCR dataset

https://lnkd.in/dHnriqrH

16. American English Male voice TTS dataset

https://lnkd.in/dqyvg862

17. Riddles and brain teasers dataset

https://lnkd.in/dKBHY3DE

18. Chinese test questions text

https://lnkd.in/dQpUd8xC

19. Chinese medical question answering data

https://lnkd.in/dsbWUCpz

20. Multi-round interpersonal dialogues text data

https://lnkd.in/dQiUq_Jg

21. Human activity recognition dataset

https://lnkd.in/dHM52MfV

22. Facial expression recognition dataset

https://lnkd.in/dqQAfMau

23. Urban surveillance dataset

https://lnkd.in/dc2RCnTk

24. Human body segmentation dataset

https://lnkd.in/d6sSrDxS

25. Fashion segmentation – clothing & accessories

https://lnkd.in/dptNUTz8

26. Fight video dataset – action recognition

https://lnkd.in/dnY_m5hZ

27. Gesture recognition dataset

https://lnkd.in/dFVPivYg

28. Facial skin defects dataset

https://lnkd.in/dKCbUvU6

29. Smoke detection and behaviour recognition dataset

https://lnkd.in/ddGg56R4

30. Weight loss transformation video dataset

https://lnkd.in/dqqT4ed9

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Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

📷 Photo

How a CNN sees images simplified 🧠

1. Input → Image breaks into pixels (RGB numbers)

2. Feature Extraction

· Convolution → Detects edges/patterns
· ReLU → Kills negatives, adds non-linearity
· Pooling → Shrinks data, keeps what matters

3. Fully Connected → Flattens features into meaning

4. Output → Probability scores: Cat? Dog? Car?

Why powerful: Learns hierarchically — edges → shapes → objects

Pixels to predictions. That's it. 👇

#DeepLearning #CNN #ComputerVision #AI

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13

Python | Machine Learning | Coding | R

Mar 28, 2026, 10:36 AM

📷 Photo

CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️

Two architectures. One goal: identify the cat. But they see things differently:

🧠 CNN (Convolutional Neural Network)

· Scans the image with filters
· Detects local patterns first (edges → textures → shapes)
· Builds understanding layer by layer

🔄 Vision Transformer (ViT)

· Splits image into patches (like words in a sentence)
· Detects global patterns from the start
· Sees the whole picture using attention mechanisms

Same input. Same output. Different journey.

CNNs think locally and build up.
Transformers think globally from the get-go.

Which one wins? Depends on the task — but both are shaping the future of how machines see.

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