Machine Learning - Live

• Class 1: The Machine Learning Workflow & Core Concepts: Understand the essential steps and terms used in any machine learning project.
• Class 2: Introduction to Scikit-Learn & Your First Model: Learn to use the primary Python library for ML and build a simple classification model.

Project 1: Iris Flower Classification: Use the k-NN algorithm to predict flower species based on measurements.

• Class 1: Data Cleaning: Learn techniques to handle missing data, correct errors, and structure raw datasets.
• Class 2: Feature Engineering: Create new, informative features from existing data to improve model performance.

Project 2: Titanic Survival Data Prep: Clean and transform the raw Titanic dataset into a format ready for machine learning.

• Class 1: Linear Models: Explore fundamental models that predict outcomes by finding linear relationships in data.
• Class 2: Logistic Regression: Use a linear model to solve binary classification problems.

Project 3: Predicting Titanic Survival: Build a model to predict Titanic passenger survival and interpret what factors were most important.

• Class 1: Advanced Regression & Regularization (Ridge, Lasso): Build advanced regression models and prevent overfitting with regularization.
• Class 2: Evaluating Regression Models: Learn the key metrics used to measure the accuracy and performance of regression models.

Project 4: House Price Prediction: Predict house prices using various regression techniques and compare their effectiveness.