Linear Regression
Simple linear regression with housing price data.
Open notebook →Regression
Linear models and optimization from first principles.
Multivariate Regression
Multiple features and multivariate linear regression.
Open notebook →Gradient Descent
Implement and visualize gradient descent optimization.
Open notebook →Data preprocessing & models
Encoding, splits, persistence, and evaluation workflows.
Save & Load Models
Serialize and restore models with pickle.
Open notebook →One-Hot Encoding
Encode categorical variables for ML pipelines.
Open notebook →Train / Test Split
Split datasets for training and evaluation.
Open notebook →K-Fold Cross-Validation
Robust model evaluation with k-fold CV.
Open notebook →Grid Search
Hyperparameter tuning with GridSearchCV.
Open notebook →Classification
Logistic regression through SVM, trees, and naive Bayes.
Logistic Regression
Binary classification on insurance data.
Open notebook →Multiclass Logistic
Multi-class logistic regression.
Open notebook →Decision Tree
Decision tree classifier on salary data.
Open notebook →Support Vector Machines
SVM classification fundamentals.
Open notebook →Titanic Survival
Naive Bayes on the Titanic dataset.
Open notebook →Email Spam Filter
Text classification with naive Bayes.
Open notebook →K-Nearest Neighbors
KNN classification tutorial.
Open notebook →Ensembles & clustering
Random forests, bagging, and unsupervised clustering.
Random Forest
Random forest classifier and regressor.
Open notebook →Bagging
Bagging for diabetes prediction.
Open notebook →K-Means
K-Means clustering on income data.
Open notebook →Advanced topics
Regularization, dimensionality reduction, and feature engineering.
L1 & L2 Regularization
Lasso and Ridge on housing data.
Open notebook →Principal Component Analysis
Dimensionality reduction on digits data.
Open notebook →Outliers (Percentile)
Percentile-based outlier detection.
Open notebook →Outliers (Z-Score)
Z-score based outlier detection.
Open notebook →Outliers (IQR)
Interquartile range outlier detection.
Open notebook →Tech stack
- Python 3
- NumPy
- Pandas
- scikit-learn
- Matplotlib
- Seaborn
- Jupyter
Run locally
git clone https://github.com/lokeshpuma/Machine_Learning.git
cd Machine_Learning
pip install jupyter scikit-learn pandas numpy matplotlib seaborn
jupyter notebook