AI Programming with Python

Welcome to the AI Programming with Python Nanodegree program!

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

What to do if you have questions about your account or general questions about the program.

Start your journey into the world of Python programming. Define fundamental terms and understand Python's logical structure. Learn how to declare and use variables to store different types of data.

Learn the fundamentals of Boolean logic and numeric operations in Python. Explore Boolean operators, relational operators, type casting, I/O operations, and limitations of floating-point numbers.

Master the art of decision-making and repetition in Python. Explore conditional statements like if, elif, and else to create branching logic in your programs.

Explore fundamental Python data structures like lists, tuples, and dictionaries. Learn to create, manipulate, and iterate over lists, understanding indexing, slicing, and list comprehensions.

Explore functions and function handling. You'll learn to break complex problems into smaller, reusable functions.

Explore Python's exception handling mechanism, common exception types, and how to handle them using try-except blocks. Learn advanced techniques like propagating and delegating exceptions.

Anaconda is a package and environment manager built specifically for data. Learn how to use Anaconda to improve your data analysis workflow.

Jupyter Notebooks are a great tool for getting started with writing python code. Though in production you often will write code in scripts, notebooks are wonderful for sharing insights and data viz!

Learn the basics of NumPy and how to use it to create and manipulate arrays.

Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.

Learn how to use matplotlib and seaborn to visualize your data. In this lesson, you will learn how to create visualizations to depict the distributions of single variables.

In this lesson, you will use matplotlib and seaborn to create visualizations to depict the relationships between two variables.

Get started with neural networks: foundations, perceptrons, deep learning, PyTorch, and practical skills. Meet your instructors and set up your learning environment.

In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in Python right here in the classroom.

Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.

Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.

Learn how to use PyTorch for building deep learning models.

Explore Transformer neural networks, their architecture, and applications like ChatGPT. Delve into NLP basics, tokenization, and model training using PyTorch for AI advancements.

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

Master the use of pre-trained transformers, including their training, fine-tuning, limitations, and applying them to NLP tasks like text generation and QA.