Intro to Machine Learning with PyTorch Nanodegree for free

Prerequisite Knowledge

To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and basic knowledge of probability and statistics.

Intermediate Python programming knowledge, including:

• At least 40 hours of programming experience
• Familiarity with data structures like dictionaries and lists
• Experience with libraries like NumPy and pandas

Basic knowledge of probability and statistics, including:

• Experience calculating the probability of an event
• Familiarity with terms like the mean and variance of a probability distribution

Supervised Learning

In this lesson, you will learn about supervised learning, a common class of methods for model construction.

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. Your 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.

Deep Learning

In this lesson, you’ll learn the foundations of neural network design and training in PyTorch.

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.

You will try your

hand at training a neural network to recognize objects in images and evaluate the model’s performance compared to Detectocorp’s model.

Unsupervised Learning

In this lesson, you will learn to implement unsupervised learning methods for different kinds of problem domains.

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.