A beginner-friendly series covering machine learning concepts step by step using Python and scikit-learn.
Each folder contains a Jupyter notebook, dataset, and explanation of the concept.
| # | Topic | Folder |
|---|---|---|
| 1 | Linear Regression (Single Variable) | 01. linear regression single variable |
| 2 | Linear Regression (Multiple Variables) + Saving Model with Pickle | 02. linear regression multi variable |
| 3 | One Hot Encoding (Pandas get_dummies + Sklearn OneHotEncoder) | 03. one hot encoding |
| 4 | Logistic Regression — Employee Retention Prediction | 04. logistic regression |
| 5 | Logistic Regression Multiclass — Iris Flower Classification + Confusion Matrix | 05. logistic regression multiclass |
| 6 | Decision Tree — Titanic Survival Prediction | 06. decision tree |
| 7 | Support Vector Machine (SVM) — Handwritten Digit Recognition | 07. support vector machine (svm) |
| 8 | K-Fold Cross Validation — Iris Flower Classification | 08. k-fold cross validation |
| 9 | Naive Bayes — Wine Classification | 09. naive bayes wine classification |
| 10 | Naive Bayes — Email Spam Detection | 10. naive bayes email spam |
| 11 | K Nearest Neighbors (KNN) — Handwritten Digit Recognition | 11. k nearest neighbors |
| 12 | Random Forest — Iris Flower Classification | 12. random forest |
| 13 | K-Means Clustering — Iris Flower Grouping | 13. k-means clustering |
| 14 | Bagging — Heart Disease Prediction | 14. bagging |
| 15 | Grid Search CV — Hyperparameter Tuning | 15. grid search cv |
| 16 | L1 and L2 Regularization — Melbourne House Price Prediction | 16. l1 l2 regularization |
| 17 | PCA — Principal Component Analysis — Heart Disease | 17. pca |
Anyone who is starting their machine learning journey and wants to learn by doing — with real datasets and clean, simple code.
pip install pandas numpy matplotlib scikit-learn jupyter