English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 12h 24m | 1.95 GB
Learning to program with Python, one of the most widely used languages in Artificial Intelligence, is the core of this program. You’ll also focus on neural networks—AI’s main building blocks. By learning foundational AI and math skills, you lay the groundwork for advancing your career—whether you’re just starting out, or readying for a full-time role.
Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (linear algebra, vectors, matrices), and the key techniques of neural networks (gradient descent and backpropagation).
Part 01: Introduction to AI Programming
Welcome to the AI programming with python Nanodegree Program!
Come and explore the beautiful world of AI.
Part 02: Intro to Python
Learn Python- one of the most widely used programming languages in the industry, particularly in AI.
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).
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.
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.
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.
Part 07: Next Steps!
Congratulations!!!!! You finished your first nanodegree in the School of AI! What are the next steps?
Part 08 (Elective): GitHub
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.
Part 10 (Elective): Intro to Machine Learning
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?