binei / python-machine-learning-public

Gradiva za tečaj: Strojno učenje v Python-u

Strojno učenje v Python-u

Termini

• 1. termin: 28.8.2023
• 2. termin: 4.9.2023
• 3. termin: 11.9.2023
• 4. termin: 18.9.2023
• 5. termin: 25.9.2023
• 6. termin: 2.10.2023
• 7. termin: 9.10.2023
• 8. termin: 16.10.2023
• 9. termin: 23.10.2023
• 10. termin: 6.11.2023
• 11. termin (po potrebi, izpit): po dogovoru

Vsebina

Del 1: Uvod v strojno učenje

• Teoretičen uvod v strojno učenje ✅
• Workflow of a machine learning project ✅
• What is machine learning? ✅
• What are machine learning models? ✅
• Why Machine Learning? ✅
• Problems Machine Learning Can Solve ✅
• scikit-learn ✅
• A First Application: Classifying Iris Species ✅

Del 2: Nadzorovano učenje

• Uvod v nadzorovano učenje ✅
• Linear models for regression ✅
• Feature scaling ✅
• Regularization ✅
• Polynomial regression ✅
• Linear models for classification ✅
• Example: North American pumpkin prices ✅
• k-Nearest Neighbors ✅
• Naive Bayes Classifiers ✅
• Kernelized Support Vector Machines ✅
• Decision Trees ✅
• Vaja: Phone prices ✅

Del 3: Feature Engineering

• Intro to Feature Engineering ✅
• Foreseeing Variable Problems When Building ML Models ✅
• Missing data imputation ✅
• Encoding Categorical Variables ✅
• Transforming Numerical Variables ✅
• Variable Discretization ✅
• Handling outliers ✅
• Creating features from date and time ✅
• Working with latitudes and longitudes ✅

Del 4: Overdnotenje in optimizacija modelov

• Cross-Validation ✅
• Grid Search ✅
• Hyperparameter Optimization ✅
• Evaluation Metrics and Scoring ✅
• Automatic Feature Selection ✅

Del 5: Pipelines in scikit-learn

• Intro To Pipelines ✅
• Example: Pipelines usage ✅

Del 6: Ensemble Learning

• Introduction to Ensemble Learning ✅
• Ensembles of Decision Trees ✅
• XGBoost ✅

Del 7: Recommender systems

• Recommender systems ✅
• Recommender systems Exercise ✅

Del 8: Nenadzorovano učenje

• Uvod v nenadzorovano učenje ✅
• Clustering ✅
• Dimension Reduction ✅

Del 9: Time Series Forecasting

• Intro to Time Series Forecasting ✅
• Understanding time series forecasting ✅
• Modeling a moving average process ✅
• Modeling an autoregressive process ✅
• Modeling complex time series ✅
• Forecasting non-stationary time series ✅
• Accounting for seasonality ✅
• Adding external variables to models ✅

Del 10: Real-world applications for ML

• End-to-End Machine Learning Project
• Overview of Machine Learning