課程大綱： Machine Learning in business – AI/Robotics培訓
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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