In this course you will learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).

Necessary preparation

Formal prerequisites include basic knowledge of algebra, and basic programming in any language.

The Program 

1. Introduction to Python.
2. Jupyter Notebooks, NumPy, Anaconda, pandas, and Matplotlib.
3. Linear Algebra Essentials.
4. Calculus Essentials.
5. Neural Networks.

What will you learn 

• Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly.
• Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, pandas, and Matplotlib.
• Learn the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.
• Learn the foundations of calculus to understand how to train a neural network: plotting, derivatives, the chain rule, and more. See how these mathematical skills visually come to life with a neural network example.
• Gain a solid foundation in the hottest fields in AI: neural networks, deep learning, and PyTorch.