I continued to look for free materials on Python 3 with AI/machine learning applications. While it’s definitely possible to find and gobble together a string of free resources, it didn’t really work for me and I felt I was spending a lot of time just doing so. I decided to enroll in the Udacity nano degree program on AI programming with Python. After I confirmed that we were going to learn Python 3 specifically and read through the course outline and reviews, I felt that it was worth signing up.
The platform is clean, the videos are engaging, and coding is done right in the same module.
The “student hub” seems a mess though. It’s like a chat with 2100 people and I haven’t been able to find it useful yet. Quite the difference between the MIT course, where student groups were small enough to make meaningful connections. I’ll give it another shot later in the course.
Lastly, the deadlines are all self imposed and there isn’t a weekly assignment. This could be good or bad… what I actually liked about the MIT course is that there was a real person grading my assignment on a weekly basis, keeping that pace. That is not the case with the Udacity course. Course outline below.
Part 1: Welcome to AI Programming
Part 2: Introduction to Python
Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly
Part 3: Jupyter Notebooks, Anaconda, Numpy, Pandas
Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, Pandas, and Matplotlib.
Part 4: Linear Algebra Essentials
Learn the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.
Part 5: Calculus Essentials
Learn the foundations of calculus to understand how to train a neural network: plotting, derivatives, the chain rule, and more. See how these mathematical skills visually come to life with a neural network example.
Part 6: 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 7: 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.
Stay tuned for updates on this course…