Welcome to the Machine Learning Content 2025 repository! This repo contains structured machine learning sessions designed for IEEE FCIH SB members, covering foundational to advanced ML topics with slides, code, and tasks.
📌 Goal: Provide a hands-on learning experience for students and enthusiasts in AI & Machine Learning.
| 📌 Session Title | 🏷️ Tags | 📜 Slides | 💻 Code | 🎯 Task |
|---|---|---|---|---|
| Session 1: Introduction to ML | Intro to Machine Learning,Intro to AI, Supervised Learning , AI market, Python |
Slides | Code | Task |
| Session 2: Linear Algebra for ML | Vectors, Linear Combinations, Linear Transformations, matrix multiplication, Norms, Norms and distance, Dot product, cosine similarity, Determinants |
Slides | Code | Task |
| Session 3: Calculus for ML | Limits, Continuity, Differentiation, Numerical differentiation, Partial derivative, Chain rule, Gradient, Jacobian, Optimization, Gradient Ascent, Gradient Descent, learning rate effect |
Slides 📺Video ✨GD visualisation✨ |
Code | Task |
| Session 4: Statistics and visualisation | Sampling, Bootsrapping, Measures of location, Measures of variation, Degree of freedom (DoF), Measures of position, Percentiles, Quartiles, Box plot, Outlier, Standard Scores, Standardization, conditional probability, Bayes' Theorem, probability mass function, Mathmatical expectation, Bernoulli distribution, Binomial distribution, Poisson distribution , Normal distribution, central limit theorem (CLT), likelihood, Maximum likelihood estimation MLE |
Slides 📺Video |
code 📺Video |
🚫No task |
| Session 5: Exploratory Data Analysis (EDA) | missing data, data skewness, outliers, plotting |
🚫 | Code Dataset 📺Video |
🔜 |
| Session 6: Linear Regression | regression,Linear regression, Loss function, Cost function, Mean Absolute Error (MAE), Mean Squared Error (MSE), residuals, Normal equation, Gradient descent, Stochastic Gradient Descent, mini-batch Gradient Descent, Polynomial regressions, Piecewise Linear Regression |
Slides | Code | Task |
| Session 7: Logistic Regression | Classification, logistic function, sigmoid functions, Odds, logits, log loss, log likelihood, negative likelihood, Cross entropy, Newton method, polynomial regression |
Slides | Code | Task |
🔹 More sessions will be added soon! Stay tuned!
Stay updated with our latest events and activities!
📌 Join us and be part of an amazing learning experience! 🚀
✨ Enjoy Learning & Keep Exploring AI! ✨