AI Programming with Python

Lesson 1: Anaconda

• Learn how to use Anaconda to manage packages and environments for use with Python

Lesson 2: Jupyter Notebooks

• Learn how to use Jupyter Notebooks to create documents combining code, text, images, and more.

Lesson 3: Numpy Basics

• Learn the value of NumPy and how to use it to manipulate data for AI problems. 
• Mini-project: Use NumPy to mean normalize an ndarray and separate it into several smaller ndarrays.

Lesson 4: Panda Basics

• Learn to use Pandas to load and process data for machine learning problems. 
• Mini-project: Use Pandas to plot and get statistics from stock data.

Lesson 5: Matplotlib Basics

• Learn how to use Matplotlib to choose appropriate plots for one and two variables based on the types of data you have.

Lesson 1: Introduction

• Learn the basics of the beautiful world of linear algebra and learn why it is such an important mathematical tool.

Lesson 2: Vectors

• Learn about the basic building block of linear algebra.

Lesson 3: Linear Combination

• Learn how to scale and add vectors and how to visualise them in 2 and 3 dimensions

Lesson 4: Linear Transformation and Matrices

• Learn what a linear transformation is and how is it directly related to matrices.
• Learn how to apply the math and visualise the concept.

Lesson 5: Linear Algebra in Neural Networks

• Learn about the world of neural networks and see how it related directly to linear algebra.

Lesson 6: Labs

• Vector Lab: Learn how to graph 2D and 3D vectors. 
• Linear Combination Lab: Learn how to computationally determine a vector’s span and solve a simple system of equations. 
• Linear Mapping Lab: Learn how to solve problems computationally using vectors and matrices

Lesson 1: Introduction

• Visualise the essence of calculus and why it is such a powerful concept in mathematics.

Lesson 2: Derivatives through Geometry

• Learn about the derivative, one of the most important tools in calculus. – See how a derivative can measure the steepness of a function and why it is such an important indicator in the world of machine learning.

Lesson 3: Chain Rule and Dot Product

• Examine the derivative of a composition of two or more functions, and its importance on a neural network.

Lesson 4: More on Derivatives

• Learn more about derivatives while focusing on exponential and implicit functions

Lesson 5: Limits

• Learn about the formal definition of a derivative through understanding limits.

Lesson 6: Integration

• Learn about the inverse of a derivative: the integral.

Lesson 7: Calculus in Neural Networks

• Learn more about the world of neural networks and see how it relates directly to calculus through an explicit example.

Lesson 1: Introduction to Neural Networks

• Acquire a solid foundation in deep learning and neural networks. Implement gradient descent and backpropogation in Python

Lesson 2: Implementing Gradient Descent

• Implement gradient descent using numpy matrix multiplication

Lesson 3: Training Neural networks

• Learn about techniques for how to improve training of a neural network

Lesson 4: Deep Learning with Pytorch

• Learn how to use pytorch for deep learning models

Lesson 1: Introduction to Transformer Neural Networks

• Explore transformer neural networks

Lesson 2: Building Transformer Neural Networks with Pytorch

• Learn to build a Transformer model with PyTorch, covering tokenization, embeddings, multi-head attention, training, and text generation for NLP tasks

Lesson 1: Using Pre-Trained transformers

• Master the use of pre-trained transformers

Successful software developers need to know how to incorporate deep learning models into everyday applications. Any device with a camera will be using image classification, object detection, and face recognition, all based on deep learning models. 

In this project learners will implement an image classification application. This application will train a deep learning model on a dataset of images. It will then use the trained model to classify new images. First learners will develop their code in a Jupyter notebook to ensure their training implementation works well. Then, they will convert their code into a Python application that they will run from the command line of their system.