AI Programming with Python

Welcome to AI Programming with Python

Welcome to the AI Programming with Python Nanodegree program!

Getting Help

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

Get Help with Your Account

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

Why Python Programming

Welcome to Introduction to Python! Here's an overview of the course.

Data Types and Operators

Familiarize yourself with the building blocks of Python! Learn about data types and operators, built-in functions, type conversion, whitespace, and style guidelines.

Data Structures in Python

Use data structures to order and group different data types together! Learn about the types of data structures in Python, along with more useful built-in functions and operators.

Control Flow

Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.

Functions

Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.

Scripting

Set up your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.

Introduction to Object-Oriented Programming

Learn the basics of object-oriented programming so that you can build your own Python package.

Use a Pre-trained Image Classifier to Identify Dog Breeds

In this project, you will use Python code and a created image classifier to identify dog breeds.

Anaconda

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

Jupyter Notebooks

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!

NumPy

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

Pandas

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

Matplotlib and Seaborn Part 1

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.

Matplotlib and Seaborn Part 2

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

Introduction

Take a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.

Vectors

Learn about vectors, the basic building block of Linear Algebra.

Linear Combination

Learn how to scale and add vectors and how to visualize the process.

Linear Transformation and Matrices

What is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.

Vectors Lab

Learn how to graph 2D 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 some problems computationally using vectors and matrices.

Linear Algebra in Neural Networks

Take a peek into the world of Neural Networks and see how it related directly to Linear Algebra!

Calculus

Calculus in Neural Networks

Welcome to Neural Networks

Introduction to Neural Networks

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.

Implementing Gradient Descent

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

Training Neural Networks

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

Deep Learning with PyTorch

Learn how to use PyTorch for building deep learning models.

Create Your Own Image Classifier

In this project, you'll build a Python application that can train an image classifier on a dataset, then predict new images using the trained model.

How Do I Continue From Here?

Industry Research

You're building your online presence. Now learn how to share your story, understand the tech landscape better, and meet industry professionals.

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.

What is Version Control

Version control is an incredibly important part of a professional programmer's life. In this lesson, you'll learn about the benefits of version control and install the version control tool Git!

Create a Git Repo

Now that you've learned the benefits of Version Control and gotten Git installed, it's time you learn how to create a repository.

Commits, Tags, Conflicts

Knowing how to review an existing Git repository's history of commits is extremely important. You'll learn how to do just that in this lesson.

Remotes and Developer Repos

In this lesson, you'll learn how to fork another developer's project. Collaborating with other developers can be a tricky process, so you'll learn how to contribute to a public project.

Writing READMEs for Repos

Learn the importance of well documented code and see how to craft meaningful READMEs.

Intro

An introduction to what you'll learn in this course!

Linear Regression

Learn how effective linear regression algorithms are in predicting numerical data

Logistic Regression

Learn about one of the most basic forms of regression modeling - logistic regression

Decision Trees

Learn how decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

Naive Bayes

Learn how powerful Naive Bayesian Algorithms are for creating classifiers for incoming labeled data.

Support Vector Machines

Learn about how support vector machines can be effective models for classification.

Ensemble Methods

Learn about bagging and boosting, two common ensemble methods for improving the accuracy of supervised learning approaches.

Outro

Let's recap and wrap up what we've learned.

Visualizing The Importance Of The Learning Rate

Visualizing the importance of the learning rate.