Course Code
mllbg
Duration
14 hours (usually 2 days including breaks)
Requirements
Knowledge and awareness of Machine Learning fundmentals
Overview
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
Course Outline
- 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
- 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
- Data Preprocessing
- Data preparation and transformation
- Feature engineering
- Feature Scaling
- Dimensionality reduction and variable selection
- Data visualization
- Exploratory analysis
- 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
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