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UDACITY - AI Programming with Python Nanodegree (nd089 v1.0.0)

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AI Programming with Python Nanodegree

Nanodegree key: nd089

Version: 1.0.0

Locale: en-us

Learn Python, NumPy, Pandas, Matplotlib, PyTorch, and Linear Algebra—the foundations for building your own neural network.

Content
Part 01 : Introduction to AI Programming
Welcome to the AI programming with python Nanodegree Program!
Come and explore the beautiful world of AI.

Module 01: Introduction to the Nanodegree
Lesson 01: Welcome to AI Programming with Python
Welcome to the AI Programming with Python Nanodegree program!

Part 02 : Intro to Python
Learn Python- one of the most widely used programming languages in the industry, particularly in AI.

Module 01: Lessons
Lesson 01: Why Python Programming
Welcome to Introduction to Python! Here's an overview of the course.

Lesson 02: Data Types and Operators
Familiarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, built-in functions, and style guidelines.

Lesson 03: 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.

Lesson 04: Functions
Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.

Lesson 05: Scripting
Setup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.

Module 02: Lab
Lesson 01: Lab: Classifying Images
Learn how to use a pre-trained CNN image classifier to write a script that identifies whether images are of dogs. If the image is identified as a dog, your program will identify the dog's breed.

Part 03 : Numpy, Pandas, Matplotlib
Let's focus on library packages for Python, such as : Numpy (which adds support for large data),
Pandas (which is used for data manipulation and analysis)
And Matplotlib (which is used for data visualization).

Module 01: Lessons
Lesson 01: Anaconda
Anaconda is a package and environment manager built specifically for data. Learn how to use Anaconda to improve your data analysis workflow.

Lesson 02: Jupyter Notebooks
Learn how to use Jupyter Notebooks to create documents combining code, text, images, and more.

Lesson 03: NumPy
Learn the basics of NumPy and how to use it to create and manipulate arrays.

Lesson 04: Pandas
Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.

Lesson 05: 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.

Lesson 06: Matplotlib and Seaborn Part 2
In this lesson, you will use matplotlib and seaborn to create visualizations to depict the relationships between two variables.

Part 04 : Linear Algebra Essentials
Learn the basics of the beautiful world of Linear Algebra and
why it is such an important mathematical tool in the world of AI.

Module 01: Lessons
Lesson 01: Introduction
Take a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.

Lesson 02: Vectors
Learn about vectors, the basic building block of Linear Algebra.

Lesson 03: Linear Combination
Learn how to scale and add vectors and how to visualize the process.

Lesson 04: 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.

Module 02: Labs
Lesson 01: Vectors Lab
Learn how to graph 2D vectors.

Lesson 02: Linear Combination Lab
Learn how to computationally determine a vector's span and solve a simple system of equations.

Lesson 03: Linear Mapping Lab
Learn how to solve some problems computationally using vectors and matrices.

Lesson 04: Linear Algebra in Neural Networks
Take a peek into the world of Neural Networks and see how it related directly to Linear Algebra!

Part 05 : Neural Networks
Acquire a solid foundation in deep learning and neural networks.
Learn about techniques for how to improve the training of a neural
network, and how to use PyTorch for building deep learning models.

Module 01: Deep Learning
Lesson 01: 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.

Lesson 02: Implementing Gradient Descent
Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.

Lesson 03: Training Neural Networks
Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.

Lesson 04: Deep Learning with PyTorch
Learn how to use PyTorch for building deep learning models

Part 06 : Create Your Own Image Classifier
In the second and final project for this course, you'll build a state-of-the-art image classification application.

Module 01: Project
Lesson 01: 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.

Project Description - Create Your Own Image Classifier

Project Rubric - Create Your Own Image Classifier

Part 07 : Next Steps!
Congratulations!!!!! You finished your first nanodegree in the School of AI! What are the next steps?

Module 01: How Do I Continue From Here?
Lesson 01: How Do I Continue From Here?

Part 08 (Elective): GitHub
Module 01: Version Control with Git
Lesson 01: 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!

Lesson 02: 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.

Lesson 03: Review a Repo's History
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.

Lesson 04: Add Commits To A Repo
A repository is nothing without commits. In this lesson, you'll learn how to make commits, write descriptive commit messages, and verify the changes you're about to save to the repository.

Lesson 05: Tagging, Branching, and Merging
Being able to work on your project in isolation from other changes will multiply your productivity. You'll learn how to do this isolated development with Git's branches.

Lesson 06: Undoing Changes
Help! Disaster has struck! You don't have to worry, though, because your project is tracked in version control! You'll learn how to undo and modify changes that have been saved to the repository.

Module 02: GitHub & Collaboration
Lesson 01: Working With Remotes
You'll learn how to create remote repositories on GitHub and how to get and send changes to the remote repository.

Lesson 02: Working On Another Developer's Repository
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.

Lesson 03: Staying In Sync With A Remote Repository
You'll learn how to send suggested changes to another developer by using pull requests. You'll also learn how to use the powerful git rebase command to squash commits together.

Part 09 (Elective): Shell Workshop
The Unix shell is a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer.

Module 01: Unix Shell
Lesson 01: Shell Workshop
The Unix shell is a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer.

Part 10 (Elective): Intro to Machine Learning
Module 01: Intro to Machine Learning
Lesson 01: Intro
An introduction to what you'll learn in this course!

Lesson 02: Linear Regression
Learn how effective linear regression algorithms are in predicting numerical data

Lesson 03: Logistic Regression
Learn about one of the most basic forms of regression modeling - logistic regression

Lesson 04: Decision Trees
Learn how decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

Lesson 05: Naive Bayes
Learn how powerful Naive Bayesian Algorithms are for creating classifiers for incoming labeled data.

Lesson 06: Support Vector Machines
Learn about how support vector machines can be effective models for classification.

Lesson 07: Ensemble Methods
Learn about bagging and boosting, two common ensemble methods for improving the accuracy of supervised learning approaches.

Lesson 08: Outro
Let's recap and wrap up what we've learned.

Part 11 (Elective): Learning Rate
Still curious about the learning rate, how sensitive it is and what role it plays in the accuracy of the training process?

Module 01: Learning Rate
Lesson 01: Visualizing The Importance Of The Learning Rate

UDACITY - AI Programming with Python Nanodegree (nd089 v1.0.0).rar
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