Once registered, students and educators will receive credentials to access the below Learning Modules hosted on Anaconda’s platform via the email they registered with.
Getting Started with Anaconda
Time: Self paced, 40 minutes
In this entry-level course, we’ll show you when and how to use Anaconda. You’ll learn about packages, conda environments, Jupyter Notebooks, integrated development environments (IDEs), and more. We’ll walk you through a Python program (in a notebook and in an IDE) and see what happens when a bug in Python code is identified.
By the end of this course, you’ll understand how to:
- Understand how different parts of the technology stack work together
- Understand basic data science concepts and tools
- Install Anaconda
- List at least 5 common Python packages and their uses
- Identify useful libraries for data science and machine learning
- Open and work with a Jupyter notebook or IDE
Introduction to Python Programming
Time: Self paced, approximately 5 hours
This beginner course is designed to help you learn the foundations of Python quickly so that you can start using Python in the real world as soon as possible! You’ll learn how to read and write code, how to choose and use data structures, and how to organize and refactor programs. More importantly, we’ll help you understand what to learn “now,” “next,” and “later” so you can focus on the fundamentals first.
By the end of this online course, you’ll understand:
- How to read and write Python code
- How to solve problems with loops and functions
- How to continuously learn and grow with the language
And you’ll be able to:
- Choose the right data structure for the right problem
- Effectively create and use objects and functions
- Refactor problematic pieces of code
Introduction to Machine Learning
Time: Self paced, approximately 2 hours
Why is it important to understand machine learning? Many machine learning techniques can solve interesting problems, from identifying email spam to classifying images. It is important to understand how libraries like scikit-learn work under the hood, to know their strengths and weaknesses, and to determine if they should be used for a given problem.
By the end of this course, you’ll understand:
- What machine learning is and the various algorithms under its umbrella
- How regression and classification techniques work, from linear regression to neural networks
- The API patterns of scikit-learn
And you’ll be able to:
- Use scikit-learn to make predictions on different types of problems, from email spam to image recognition
- Pair the proper application of supervised machine learning algorithms to a given problem and context appropriately
- Explain how different machine learning algorithms work, including decision trees and neural networks