Python Machine Learning Bootcamp

Overview

Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.

Syllabus

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