Introduction to Scikit-Learn

• Overview of Scikit-Learn and its role in machine learning
• Understanding the Scikit-Learn API and its components
• Installing and setting up Scikit-Learn

Data Preprocessing and Feature Engineering

• Handling missing data and data cleaning techniques
• Feature scaling and normalization
• Encoding categorical variables
• Handling imbalanced data

Supervised Learning Algorithms

• Linear regression for regression tasks
• Logistic regression for classification tasks
• Decision trees and random forests
• Support Vector Machines (SVM)
• Naive Bayes classifiers
• k-Nearest Neighbors (k-NN) algorithm

Unsupervised Learning Algorithms

• Principal Component Analysis (PCA) for dimensionality reduction
• Clustering algorithms (k-means, hierarchical clustering)
• Gaussian Mixture Models (GMM) for probabilistic clustering
• Association rule mining with Apriori algorithm

Model Evaluation and Validation

• Cross-validation techniques for model evaluation
• Performance metrics for classification and regression tasks
• Hyperparameter tuning and model selection
• Model evaluation on test data

Ensemble Methods

• Bagging and boosting techniques
• Random Forests and Gradient Boosting
• Stacking and voting classifiers
• Model averaging and weighted averaging

Feature Selection and Dimensionality Reduction

• Feature selection techniques (filter, wrapper, embedded methods)
• Recursive Feature Elimination (RFE) and feature importance
• Manifold learning and t-SNE visualization

Model Pipelines and Workflow

• Building model pipelines for efficient data processing
• Feature unions and parallel processing
• Saving and loading models

Text Mining and Natural Language Processing (NLP)

• Working with text data using Scikit-Learn
• Text preprocessing and tokenization
• Text classification and sentiment analysis

Time Series Analysis with Scikit-Learn

• Handling time series data in machine learning tasks
• Feature extraction and engineering for time series data
• Time series forecasting and anomaly detection

Model Interpretability and Explainability

• Techniques for model interpretability (feature importance, partial dependence)
• Model explainability with SHAP values and LIME

Handling Imbalanced and Biased Data

• Techniques for handling imbalanced datasets
• Addressing bias in machine learning models
• Evaluation metrics for imbalanced data

Scikit-Learn and Big Data

• Scaling Scikit-Learn with distributed computing frameworks (Spark, Dask)
• Handling large datasets and out-of-memory scenarios

Advanced Scikit-Learn Topics

• Handling multi-label classification problems
• Time series classification and regression with Scikit-Learn
• Advanced model evaluation techniques (ROC curves, precision-recall curves)