Machine Learning with Python

All times GMT (UTC)

10am:  
Introduction to machine learning concepts and data preprocessing

• Overview of machine learning concepts and applications 

• Introduction to common machine learning algorithms such as regression, classification, and clustering 

• Exploring data preprocessing techniques including data cleaning, feature scaling, and feature engineering 

• Hands-on exercise(s): implementing data preprocessing techniques in Python

11.15am:
Break

11.45am:
Supervised learning algortihms in Python

• Understanding supervised learning algorithms such as linear regression, logistic regression, decision trees, and random forests 

• Implementing supervised learning algorithms using Python’s sci-kit learn package

• Evaluating model performance using metrics such as accuracy, precision, recall, and F1-score 

• Practical exercise(s): building and evaluating predictive models with real-world datasets

1pm:
Lunch break

2pm:
Unsupervised learning and model evaluation

• Exploring unsupervised learning algorithms such as k-means clustering and hierarchical clustering 

• Implementing unsupervised learning algorithms in Python for tasks like customer segmentation and anomaly detection 

• Techniques and metrics for evaluating clustering performance 

• Hands-on activity(s): applying unsupervised learning techniques to real-world datasets 

3.15pm:
Break

3.45pm:
Advanced topics in machine learning with Python

• Overview of advanced machine learning topics such as ensemble learning, dimensionality reduction, and feature selection 

• Implementing advanced machine learning techniques in Python using sci-kit learn 

• Strategies for hyperparameter tuning and model optimization 

• Case studies and practical examples: applying advanced machine learning techniques to solve complex problems in various domains 

5pm:
End of day

Documentation and tutorials here: https://scikit-learn.org/stable/user_guide.html