Course 1: Introduction to AI Programming Welcome to the AI programming with python Nanodegree Program! Come and explore the beautiful world of AI.2 hoursWelcome to AI Programming with PythonWelcome to the AI Programming with Python Nanodegree program!Getting HelpYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.Get Help with Your AccountWhat to do if you have questions about your account or general questions about the program.Course 2: Introduction to Python for AI Programmers Explore Python fundamentals tailored for AI beginners. Master data types, control flow, functions, and scripting through hands-on projects, gaining essential skills to lay the groundwork for AI coding success.31 hoursWhy Python Programming for AIWelcome to Introduction to Python! Here's an overview of the course.Using Python Data Types and Operators in AI ProgrammingFamiliarize yourself with the building blocks of Python! Learn about data types and operators, built-in functions, type conversion, whitespace, and style guidelines.Python Data Structures in AI ProgrammingUse 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.Using Control Flow in AI ProgrammingBuild logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.Using Python Functions in AI ProgrammingLearn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.Python Scripting for AI ProgrammingSet 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 Python for AI ProgrammingLearn the basics of object-oriented programming so that you can build your own Python package.Use a Pre-trained Image Classifier to Identify Dog BreedsIn this project, you will use Python code and a created image classifier to identify dog breeds.Course 3: 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). 8 hoursAnacondaAnaconda is a package and environment manager built specifically for data. Learn how to use Anaconda to improve your data analysis workflow.Jupyter NotebooksJupyter 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!NumPyLearn the basics of NumPy and how to use it to create and manipulate arrays.PandasLearn the basics of Pandas Series and DataFrames and how to use them to load and process data.Matplotlib and Seaborn Part 1Learn 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 2In this lesson, you will use matplotlib and seaborn to create visualizations to depict the relationships between two variables.Course 4: 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.9 hoursIntroduction 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 MatricesWhat is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.Vectors LabLearn how to graph 2D vectors.Linear Combination LabLearn how to computationally determine a vector's span and solve a simple system of equations.Linear Mapping LabLearn how to solve some problems computationally using vectors and matrices.Linear Algebra in Neural NetworksTake a peek into the world of Neural Networks and see how it related directly to Linear Algebra!Course 5: Calculus Essentials This course covers foundational topics in Calculus. You will learn the foundations of calculus and understand how to train a neural network: plotting, derivatives, the chain rule, and more. You will also see how these mathematical skills visually come to life with a neural network example.3 hoursCalculusCalculus in Neural NetworksCourse 6: Neural Networks - AI Programming with Python This course on neural networks explains how algorithms inspired by the human brain operate and puts to use those concepts when designing neural networks to solve particular problems.13 hoursWelcome to Neural NetworksIntroduction to Neural NetworksIn 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 DescentMat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.Training Neural NetworksNow that you know what neural networks are, in this lesson you will learn several techniques to improve their training.Deep Learning with PyTorchLearn how to use PyTorch for building deep learning models.Course 7: Programming Transformer Neural Networks with PyTorch This course will guide you through the essential concepts of Transformer Neural Networks and their implementation using PyTorch. Starting with an introduction to Transformers, you will learn to build and train Transformer models from scratch. Additionally, you will explore the advantages of using pre-trained Transformer models and how to leverage them effectively in your projects. By the end of this course, you will have a solid foundation in programming Transformer Neural Networks with PyTorch.4 hoursIntroduction to Transformer Neural NetworksExplore Transformer neural networks, their architecture, and applications like ChatGPT. Delve into NLP basics, tokenization, and model training using PyTorch for AI advancements.Building Transformer Neural Networks with PyTorchLearn to build a Transformer model with PyTorch, covering tokenization, embeddings, multi-head attention, training, and text generation for NLP tasks.Using Pre-Trained TransformersMaster the use of pre-trained transformers, including their training, fine-tuning, limitations, and applying them to NLP tasks like text generation and QA.Course 8: 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.4 hours Create Your Own Image ClassifierIn 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.Course 9: Next Steps! Congratulations!!!!! You finished your first nanodegree in the School of AI! What are the next steps?5 minutesHow Do I Continue From Here?Course 10: OptionalGit and GitHub OptionalProgrammers use version control software to manage changes to software projects large and small. In these lessons you will learn to keep track of changes to your code using the Git version control software, and collaborate with other programmers using GitHub.7 hoursWhat is Version ControlVersion 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 RepoNow 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, ConflictsKnowing 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 ReposIn 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 ReposLearn the importance of well documented code and see how to craft meaningful READMEs.Course 11: OptionalIntro to Machine Learning Optional12 minutesIntroAn introduction to what you'll learn in this course!Linear RegressionLearn how effective linear regression algorithms are in predicting numerical dataLogistic RegressionLearn about one of the most basic forms of regression modeling - logistic regressionDecision TreesLearn how decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.Naive BayesLearn how powerful Naive Bayesian Algorithms are for creating classifiers for incoming labeled data.Support Vector MachinesLearn about how support vector machines can be effective models for classification.Ensemble MethodsLearn about bagging and boosting, two common ensemble methods for improving the accuracy of supervised learning approaches.OutroLet's recap and wrap up what we've learned.Course 12: OptionalLearning Rate OptionalStill curious about the learning rate, how sensitive it is and what role it plays in the accuracy of the training process? 15 minutesVisualizing The Importance Of The Learning Rate Visualizing the importance of the learning rate.CompanyAbout Us Why Udacity? 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