You will learn

• How to apply machine learning algorithms to real-world data
• The possibilities of generative AI
• How to build, train, and optimize predictive models
• How to understand the strengths and limitations of various algorithms

Topics

• Introduction to machine learning (supervised, unsupervised, and reinforcement learning)
• Regression: predicting numerical values
• Classification: dividing data into categories
• Unsupervised learning and clustering, including k-means
• Introduction to generative AI (GPT, DALL·E) and applications with Python
• Model evaluation with ROC curve, AUC, confusion matrix
• Improving models with feature engineering and hyperparameter tuning
• Deploying machine learning models to production
• Ensemble methods: stacking, bagging, and boosting