Machine Learning Course with Certificate

• Python basics and functions
• NumPy arrays
• pandas data wrangling
• Matplotlib and Seaborn
• Jupyter and Git

Project: Clean and analyse a public sales dataset and publish findings as a notebook on GitHub

• Descriptive statistics and distributions
• Hypothesis testing
• Bias-variance and overfitting
• Train-test splits
• Gradient descent

Project: Implement linear regression with gradient descent in NumPy and compare it with scikit-learn

• Missing values and encoding
• scikit-learn pipelines
• Linear and logistic regression
• k-NN, Naive Bayes and SVM
• Classification metrics

Project: Build a leak-proof churn prediction pipeline with ColumnTransformer and a tuned threshold

• Decision trees and random forests
• XGBoost and LightGBM
• Cross-validation strategies
• Optuna tuning
• SHAP explanations

Project: Predict loan default with LightGBM, tune with Optuna and explain predictions using SHAP

• k-means and DBSCAN
• PCA and UMAP
• Isolation Forest anomalies
• RFM segmentation
• Cluster profiling

Project: Segment e-commerce customers using RFM features and present named segments with campaign ideas

• Saving pipelines with joblib
• FastAPI model serving
• Docker containers
• MLflow tracking and registry
• Drift monitoring with PSI

Project: Deploy the churn model as a Dockerised FastAPI service with MLflow tracking and drift checks