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Machine Learning in business – AI/Robotics Training Course

This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge

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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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Knowledge and awareness of Machine Learning fundmentals

14 Hours

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