Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🌟
- 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
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Searched 35 free courses, so you don't have to! 🔍✨
Here are the 35 best free courses: 🎓
1. Data Science: Machine Learning 🤖
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2. Introduction to computer science 💻
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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 🐍🧠
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8. Startup Success: How to launch a technology company in 6 steps 🚀
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9. Data analysis: statistical modeling and computation in applications 📊
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10. The art and science of searching in systematic reviews 🔎
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11. Introduction to conducting systematic review 📋
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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 💸
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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
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
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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
📅 Date: 30 April 2026
⏰ Time: 7:30 PM
🔗 Zoom Link: https://us06web.zoom.us/meeting/register/HSOTmzzpTkGIGm9C9oGbaA
Everyone is welcome!
https://t.me/CodeProgrammer
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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😱
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
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💡 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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A good selection for those who want to improve their skills in practice, rather than just reading theory:
tags: #ML #DataScience #DataAnalysis
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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
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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 🔗
https://t.me/CodeProgrammer
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
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Overfitting and Generalisation in ML.pdf
380.5 KB
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
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
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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🟢
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
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Forwarded from Machine Learning
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
The illustrations make complex concepts surprisingly easy to follow 🎨. Highly recommend this 👍.
Link: https://jeffe.cs.illinois.edu/teaching/algorithms/ 🔗
https://t.me/MachineLearning9
❤5
🧐 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
https://t.me/CodeProgrammer
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
https://t.me/CodeProgrammer
❤5
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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
https://t.me/CodeProgrammer✅
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
https://t.me/CodeProgrammer
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🧐 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
https://t.me/CodeProgrammer 👩💻
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
https://t.me/CodeProgrammer 👩💻
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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
https://t.me/CodeProgrammer🐍
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
https://t.me/CodeProgrammer
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Forwarded from Learn Python Coding
𝗠𝗮𝘀𝘁𝗲𝗿_𝗣𝘆𝘁𝗵𝗼𝗻_𝘁𝗵𝗲_𝗥𝗶𝗴𝗵𝘁_𝗪𝗮𝘆.pdf
6.6 MB
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
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
❤3