Python Machine Learning Bootcamp

Course Overview

1. Course Kick‑off & Python Refresher

• Data Science tool recap - Pandas and indexing
• Exploratory data analysis (EDA): standard deviations and uniform vs. normal distributions using NumPy/Pandas
• Hands‑on: loading CSVs, basic plotting with Matplotlib

2. Data Visualization & Simple Linear Regression

• Crafting clear scatterplots: labels, grids, styling
• Single‑variable linear regression (attendance → concessions)
• Train‑test splitting and dealing with outliers
• Evaluating models with R²; interpreting residuals
• Extended example: car‑sales dataset, predicting price from one feature

3. Binary Classification & Logistic Regression

• From regression to classification: why logistic vs. linear
• Implementing logistic regression on an employee “stay/leave” dataset
• Classification metrics deep dive: accuracy, precision, recall, F1 score, ROC curve
• Understanding variability: train‑test ratios, data shuffling, sample size effects
• Confusion matrix analysis

4. k‑Nearest Neighbors & the Iris Dataset

• Introduction to k‑NN: distance metrics, choosing k
• Dataset exploration: sepal/petal measurements, plotting clusters
• Preprocessing: label encoding categorical data, feature scaling
• Model training, hyperparameter tuning, evaluating with confusion matrix and classification report
• Brief intro to decision‑tree logic (setting up for ensembles)

5. Ensemble Methods & Neural Networks

• Random forest classifiers on the Titanic dataset: feature engineering, importance scores
• Kaggle workflow: generating predictions, submitting to competition
•  Neural network primer: perceptron to multilayer architectures
• Hands‑on MNIST digit classification with Keras/TensorFlow in Colab

Prerequisites

This course does require students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require them to enroll in our Python for Data Science Bootcamp before taking this course.