1. Coding with Python, drawing upon libraries and automation scripts to solve complex problems.
2. Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, Pandas, and Matplotlib.
3. Learn the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.
4. 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.
Click here for course syllabus
Learners shall have to prove their skills by completing the following projects:
- Using a Pre-trained Image Classifier to Identify Dog Breeds - Test acquired python coding skills by using a trained image classifier. Learners will need to use the trained neural network to classify images of dogs (by breeds) and compare the output with the known dog breed classification. Learners will have a chance to build their own functions, use command line arguments, test the runtime of the code, create a dictionary of lists, and more.
- Create Your Own Image Classifier - Learner will implement an image classification application. This application will train a deep learning model on a dataset of images. It will then use the trained model to classify new images. First learners will develop code in a Jupyter notebook to ensure the training implementation works well. Then, convert code into a Python application that will run from the command line of a system.
- Learners will be awarded Certificate of Completion co-issued by TP and Udacity in Artificial Intelligence Programming with Python upon completion of at least ONE project.
- Learners will be awarded the Nanodegree conferred by Udacity in Artificial Intelligence Programming with Python upon completion of ALL projects.