# Python Machine Learning Bootcamp (Self-Paced)

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

## Overview

This skill set is in high demand, as machine learning algorithms now power most trading on Wall Street and drive product recommendations at major companies like Amazon, Spotify, and Netflix.

 

The course begins with linear and logistic regression, two of the most proven and reliable approaches to tackling machine learning problems. You’ll then advance to algorithms with different theoretical foundations, including k-nearest neighbors, decision trees, and random forests. Along the way, you’ll explore key statistical concepts such as bias, variance, and overfitting. You’ll also learn how to measure model accuracy and gain practical tips for selecting the most effective features and algorithms.

 

Focused on practical, real-world problem-solving, the course explains the mathematical foundations of each algorithm visually, without a formal math requirement.

## 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](/courses/python-data-science-bootcamp)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

## Pricing

**Tuition:** $1895