Live Online Scikit-learn Course for Data Analytics

Live Course Module: Scikit-learn Course for Data Analytics

Total Duration: 24 Hours (4 Weeks)

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Week 1: Introduction to Scikit-learn & Data Preprocessing (6 Hours)

Session 1 (2 hrs): Getting Started with Scikit-learn

• Overview of Scikit-learn and its ecosystem

• Installing and importing Scikit-learn

• Understanding Scikit-learn’s workflow (fit(), transform(), predict())

• Working with datasets (load_iris, load_boston, etc.)

• Hands-on: Basic ML workflow example (Iris classification)

Session 2 (2 hrs): Data Preprocessing Essentials

• Handling missing data (SimpleImputer, KNNImputer)

• Encoding categorical features (LabelEncoder, OneHotEncoder)

• Feature scaling (StandardScaler, MinMaxScaler, RobustScaler)

• Train-test splitting and data pipelines

Session 3 (2 hrs): Feature Engineering Techniques

• Feature extraction and transformation

• Polynomial features and interaction terms

• Feature selection (SelectKBest, RFE)

• Practical: Preparing a dataset for modeling

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Week 2: Supervised Learning – Regression and Classification (6 Hours)

Session 4 (2 hrs): Linear Models for Regression

• Simple and multiple linear regression

• Regularization: Ridge, Lasso, and ElasticNet

• Model evaluation: MAE, MSE, R²

• Hands-on: Predicting housing prices

Session 5 (2 hrs): Classification Algorithms

• Logistic Regression, KNN, and Decision Tree classifiers

• Confusion matrix, precision, recall, F1-score

• ROC-AUC analysis and cross-validation

• Hands-on: Classifying customer churn

Session 6 (2 hrs): Ensemble Methods

• Random Forest and Gradient Boosting

• Bagging vs Boosting

• Feature importance analysis

• Practical: Ensemble models for sentiment prediction

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Week 3: Unsupervised Learning & Model Optimization (6 Hours)

Session 7 (2 hrs): Clustering Techniques

• K-Means clustering

• Hierarchical clustering

• DBSCAN and its use cases

• Visualizing clusters and interpreting results

Session 8 (2 hrs): Dimensionality Reduction

• PCA (Principal Component Analysis)

• t-SNE for visualization

• Applying PCA before modeling

• Hands-on: Visualizing high-dimensional data

Session 9 (2 hrs): Model Selection and Hyperparameter Tuning

• Cross-validation strategies (KFold, StratifiedKFold)

• Grid Search and Random Search (GridSearchCV, RandomizedSearchCV)

• Pipeline integration for automation

• Practical: Model tuning and evaluation on real data

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Week 4: Advanced Topics and Capstone Project (6 Hours)

Session 10 (2 hrs): Model Persistence and Deployment

• Saving and loading models (joblib, pickle)

• Using Scikit-learn in production pipelines

• Integrating with Streamlit or Flask for visualization

Session 11 (2 hrs): Real-World Case Studies

• End-to-end ML workflow using Scikit-learn

• Case study: Credit risk modeling / customer segmentation

• Interpreting model results and generating insights

Session 12 (2 hrs): Capstone Project & Assessment

• Capstone: Build a predictive analytics model using real dataset

• Model presentation and peer review

• Q&A, wrap-up, and certification assessment

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🧠 Tools & Technologies Used

• Python 3.8+

• Jupyter Notebook

• Scikit-learn

• NumPy, Pandas, Matplotlib, Seaborn

• Streamlit (optional for project)

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🏁 Final Deliverables

• End-to-end ML project report

• Jupyter notebook with code and visualizations

• Certificate of completion

Learning Outcomes:

By the end of this course, learners will be able to:
✅ Understand Scikit-learn’s core architecture and workflow
✅ Preprocess and transform real-world datasets
✅ Apply machine learning algorithms for classification, regression, and clustering
✅ Evaluate and tune model performance
✅ Integrate Scikit-learn models into analytics pipelines