# Python Machine Learning Bootcamp

Canonical URL: <https://www.graduateschool.edu/courses/python-machine-learning>

## Overview

This course begins with linear and logistic regression, two of the most established tools for tackling machine learning problems. You’ll then explore algorithms with different theoretical foundations, including k-nearest neighbors, decision trees, and random forest. Key statistical concepts such as bias, variance, and overfitting will be emphasized, along with methods for measuring model accuracy and selecting effective features and algorithms.

Designed with practical application in mind, the course focuses on solving real-world problems using machine learning. Mathematical foundations will be explained visually, without requiring a formal math background.

## What you'll learn

- Explore foundational techniques like linear and logistic regression for modeling numerical and categorical data
- Understand the difference between regression and classification problems and when to apply each approach
- Build and evaluate models using k-nearest neighbors, decision trees, and ensemble methods like random forest
- Learn key concepts such as cross-validation, training vs. test sets, and performance metrics like mean squared error
- Apply feature engineering techniques to improve model accuracy while managing overfitting and bias-variance tradeoffs
- Use Python's essential data science libraries, NumPy, Pandas, and scikit-learn, to structure data and implement algorithms
- Gain insights into how machine learning powers systems at companies like Netflix, Spotify, and Amazon
- Complete a final portfolio project that demonstrates your ability to apply machine learning to solve real problems

## Prerequisites

This course requires 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 a student to enroll in our [Python for Data Science Bootcamp](/courses/python-data-science-bootcamp)before taking this course.

## Curriculum

### 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

## Schedule
- May 26, 2026 – Jun 25, 2026 — Live Online
- Jun 15, 2026 – Jun 19, 2026 — Live Online
- Aug 3, 2026 – Aug 7, 2026 — Live Online
- Aug 30, 2026 – Oct 11, 2026 — Live Online
- Sep 8, 2026 – Oct 8, 2026 — Live Online
- Sep 22, 2026 – Sep 28, 2026 — Live Online
- Nov 9, 2026 – Nov 13, 2026 — Live Online
- Dec 29, 2026 – Feb 2, 2027 — Live Online
- Jan 17, 2027 – Feb 21, 2027 — Live Online

## Pricing

**Tuition:** $1895