Intro to Machine Learning with TensorFlow
In this program we Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, we move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.
This program is intended for students with experience in Python, who have not yet studied Machine Learning topics.
First Project Find Donors for CharityML CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. The goal will be to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent to ask for donations.
Second Project Create Your Own Image Classifier As a machine learning engineer at a fictional self-driving car startup, you have been asked to help decide whether to build or buy an object detection algorithm for objects that may be on the side of the road. A company, Detectocorp, claims an 80% accuracy rate on the CIFAR-10 dataset, a benchmark used to evaluate the state of the art for computer vision systems. We try our hand at training a neural network to recognize objects in images and evaluate the model's performance compared to Detectocorp's model.
Third Project Create Customer Segments Use unsupervised learning techniques to see if any similarities exist between customers and use those similarities to segment customers into distinct categories using various clustering techniques. This segmentation is used to help the business make more informed marketing and product decisions.