Course Outline

  1. Introduction to ML
    • Machine learning as part of Artificial intelligence
    • Types of ML
    • ML algorithms
    • Challenges and potential use of ML
    • Overfitting and bias-variance trade-off in ML
  2. Techniques of Machine learning
    • The Machine Learning Workflow
    • Supervised learning – Classification, Regression
    • Unsupervised learning – Clustering, Anomaly detection
    • Semi-supervised learning and Reinforcement Learning
    • Consideration in Machine Learning
  3. Data Preprocessing
    • Data preparation and transformation
    • Feature engineering
    • Feature Scaling
    • Dimensionality reduction and variable selection
    • Data visualization
    • Exploratory analysis
  4. Case studies
    • Advanced feature engineering and impact on results in linear regression for prediction
    • Time series analysis and Forecasting monthly volume of sales  - basic methods, seasonal adjustment, regression, exponential smoothing, ARIMA, neural networks
    • Market basket analysis and association rules mining
    • Segmentation analysis using clustering and self-organising maps
    • Classification which customer is likely to default using logistic regression, decision trees, xgboost, svm



Knowledge and awareness of Machine Learning fundmentals

 14 Hours

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