COURSE OUTLINE

Day 1: The Python ML Ecosystem & Data Foundations

Focus: Setting up the environment and mastering the data structures.

• The ML Pipeline: Overview of Data Collection → Preprocessing → Modeling → Evaluation.
• Vectorized Computing with NumPy: Why arrays are the "language" of machine learning.
• Data Manipulation with Pandas: Loading CSVs, filtering rows, and handling null values.
• Exploratory Data Analysis (EDA): Identifying outliers and skewness in your features.
• Hands-on: Cleaning a real-world dataset to make it "ML-ready."

Day 2: Supervised Learning – Regression

Focus: Predicting continuous numerical values.

• Simple vs. Multiple Linear Regression: Mapping relationships between variables.
• The Cost Function: Understanding how models minimize error (Residual Sum of Squares).
• Data Splitting: The critical importance of the Train-Test Split.
• Performance Metrics: Calculating Mean Squared Error (MSE) and $R^2$ Score.
• Hands-on: Building a housing price predictor based on historical market data.

Day 3: Supervised Learning – Classification

Focus: Categorizing data points into distinct classes.

• Logistic Regression: Using sigmoid functions to predict binary probabilities.
• Decision Trees: Mapping logic paths and understanding "Gini Impurity."
• Evaluation Metrics: Moving beyond accuracy to Precision, Recall, and F1-Score.
• The ROC Curve: Visualizing the tradeoff between True Positives and False Positives.
• Hands-on: Developing a "Customer Churn" model to predict which users will leave a service.

Day 4: Model Improvement & Feature Selection

Focus: Enhancing accuracy and preventing "Overfitting."

• The Bias-Variance Tradeoff: Finding the "sweet spot" of model complexity.
• Regularization: Using Ridge and Lasso to penalize over-complex models.
• Feature Selection: Using SelectKBest and recursive feature elimination.
• Cross-Validation: Using K-Fold techniques to ensure results aren't just "lucky."
• Hands-on: Optimizing your Day 3 classifier to improve its F1-score by 10% or more.

Day 5: Unsupervised Learning & Computer Vision Basics

Focus: Finding structure in unlabelled data and handling images.

• Clustering with K-Means: Identifying natural groupings in your data without labels.
• Dimensionality Reduction: Using PCA to visualize high-dimensional data in 2D or 3D.
• Intro to OpenCV: Basics of image processing (thresholding, edge detection).
• Final Project: Building a "Face Detection" tool or a "Customer Segmentation" engine.
• Course Wrap-up: Discussion on ethical AI and the path to Deep Learning.