# Python Machine Learning Course Online (Self-Paced)

Canonical URL: <https://www.creativelive.com/classes/python-machine-learning-online>

## Overview

This skill set is in high demand, as machine learning algorithms now drive much of the trading activity on Wall Street and power recommendation systems at major companies such as Amazon, Spotify, and Netflix.

This course begins with linear and logistic regression, two of the most established and reliable methods for solving machine learning problems. From there, the class moves into algorithms with different theoretical foundations, including k-nearest neighbors, decision trees, and random forests. Along the way, you will explore important statistical concepts such as bias, variance, and overfitting. You will also learn how to evaluate model accuracy and how to choose effective features and algorithms.

The course focuses on the practical skills needed to solve real-world machine learning problems. The mathematical ideas behind each algorithm are explained visually, but there is no formal math component. Students should already be comfortable writing Python programs and using the NumPy and Pandas libraries.

This course requires prior experience with Python and its core data science libraries, NumPy and Pandas. Students who have not previously worked with Python should complete our Python for Data Science Bootcamp before enrolling.

## What you'll learn

- How to clean and balance your data using the Pandas library
- Applying machine learning algorithms such as logistic regression and random forests using the scikit-learn library
- Choosing good features to use as input for your algorithms
- Properly splitting data into training, testing, and cross-validation sets
- Important theoretical concepts like overfitting, variance, and bias
- Evaluating the performance of your machine learning models

## 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](/classes/python-data-science-bootcamp-nyc)before taking this course.

## Curriculum

### Fundamentals

#### Basic Regression Analysis

- Linear Regression
- Mean squared error
- Training set vs Test set
- Cross validation

#### Advanced Regression Analysis

- Multi-linear regression
- Feature engineering
- Overfitting

### Classification

#### Logistic Regression

- Regression vs Classification
- Logistic Regression
- Sigmoid function

#### K-nearest Neighbors

- K-nearest neighbors
- Model-based vs memory-based
- Parametric vs non-parametric
- Evaluating performance

### Decision Trees

#### Decision Trees

- Decision tree
- Interpretability
- Bias-variance tradeoff

#### Random forest

- Random forest
- Ensemble methods
- Hyperparameters

### Final Portfolio Project

## Schedule
- Jun 13, 2026 12:00am–12:00am — Live Online
- Jun 25, 2026 12:00am–12:00am — Live Online
- Jul 18, 2026 12:00am–12:00am — Live Online
- Jul 25, 2026 12:00am–12:00am — Live Online
- Aug 12, 2026 12:00am–12:00am — Live Online

## Pricing

**Tuition:** $1895