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Introduction to Machine Learning with Pytorch

Nanodegree Program

The Intro to Machine Learning with Pytorch program covers machine learning concepts and techniques, with a focus on supervised and unsupervised learning. The program includes three courses and covers topics such as linear regression, logistic regression, decision trees, Naive Bayes, support vector machines, neural networks, and clustering. The courses include projects that allow learners to apply these techniques to real-world problems, such as identifying potential donors for a charity and clustering customers based on their spending habits. The program uses Python and PyTorch for implementation and includes lessons on model evaluation and tuning.

The Intro to Machine Learning with Pytorch program covers machine learning concepts and techniques, with a focus on supervised and unsupervised learning. The program includes three courses and covers topics such as linear regression, logistic regression, decision trees, Naive Bayes, support vector machines, neural networks, and clustering. The courses include projects that allow learners to apply these techniques to real-world problems, such as identifying potential donors for a charity and clustering customers based on their spending habits. The program uses Python and PyTorch for implementation and includes lessons on model evaluation and tuning.

Intermediate

3 months

Real-world Projects

Completion Certificate

Last Updated June 16, 2024

Skills you'll learn:
Naive bayes classifiers • Gaussian mixture models • Model evaluation • Support vector machines
Prerequisites:
Multivariable calculus • Python for data science • Basic supervised machine learning

Courses In This Program

Course 1 1 hour

Introduction to Machine Learning

Welcome to Machine learning with Pytorch

Lesson 1

Welcome to Machine Learning

Welcome to the Machine Learning Engineer Nanodegree program! Learn about the program structure and the projects you'll work on in this program.

Lesson 2

Get Help with Your Account

What to do if you have questions about your account or general questions about the program.

Lesson 3

Setting Up Your Computer

In this lesson, get your computer set up with Python 3 using Anaconda, as well as setting up a text editor.

Course 2 3 weeks

Supervised Learning

In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.

Lesson 1

Introduction to Supervised Learning

Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field.

Lesson 2

Linear Regression

Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works!

Lesson 3

Perceptron Algorithm

The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.

Lesson 4

Decision Trees

Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

Lesson 5

Naive Bayes

Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems.

Lesson 6

Support Vector Machines

Support vector machines are a common method used for classification problems. They have been proven effective using what is known as the 'kernel' trick!

Lesson 7

Ensemble Methods

Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own.

Lesson 8

Model Evaluation Metrics

Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!

Lesson 9

Training and Tuning

Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.

Lesson 10 • Project

Finding Donors Project

You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!

Course 3 4 weeks

Introduction to Neural Networks with PyTorch

Learn the fundamentals of neural networks with Python and PyTorch, and then use your new skills to create your own image classifier—an application that will first train a deep learning model on a dataset of images and then use the trained model to classify new images.

Lesson 1

Course Introduction

Meet your instructors, get a short overview of what you'll be learning, check your prerequisites, and learn how to use the workspaces and notebooks found throughout the lessons.

Lesson 2

Introduction to Neural Networks

In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in Python right here in the classroom.

Lesson 3

Implementing Gradient Descent

Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.

Lesson 4

Training Neural Networks

Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.

Lesson 5

Deep Learning with PyTorch

Learn how to use PyTorch for building deep learning models.

Lesson 6 • Project

Create Your Own Image Classifier

In this project, you'll create your own image classifier and then train—and evaluate its performance—using one of the most classic and well-studied computer vision data sets, CIFAR-10.

Course 4 4 weeks

Unsupervised Learning

In this course, you'll learn how to apply unsupervised learning to solve real-world problems.

Lesson 1

Clustering

Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.

Lesson 2

Hierarchical and Density Based Clustering

We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).

Lesson 3

Gaussian Mixture Models and Cluster Validation

In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.

Lesson 4

Dimensionality Reduction and PCA

Often we need to reduce a large number of features in our data to a smaller, more relevant set. Principal Component Analysis, or PCA, is a method of feature extraction and dimensionality reduction.

Lesson 5

Random Projection and ICA

In this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).

Lesson 6 • Project

Project: Identify Customer Segments

In this project, you'll apply your unsupervised learning skills to two demographics datasets, to identify segments and clusters in the population, and see how customers of a company map to them.

Taught By The Best

Photo of Josh Bernhard

Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

Photo of Mat Leonard

Mat Leonard

Content Developer

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

Photo of Andrew Paster

Andrew Paster

Instructor

Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity's Self-Driving Car Engineer Nanodegree program.

Photo of Jennifer Staab

Jennifer Staab

Instructor

Jennifer has a PhD in Computer Science and a Masters in Biostatistics; she was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.

Photo of Dan Romuald Mbanga

Dan Romuald Mbanga

Instructor

Dan leads Amazon AI's Business Development efforts for Machine Learning Services. Day to day, he works with customers—from startups to enterprises—to ensure they are successful at building and deploying models on Amazon SageMaker.

Photo of Cezanne Camacho

Cezanne Camacho

Curriculum Lead

Cezanne is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she's applied computer vision and deep learning to medical diagnostic applications.

Photo of Sean Carrell

Sean Carrell

Instructor

Sean Carrell is a former research mathematician specializing in Algebraic Combinatorics. He completed his PhD and Postdoctoral Fellowship at the University of Waterloo, Canada.

Photo of Jay Alammar

Jay Alammar

Instructor

Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

Photo of Luis Serrano

Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Ratings & Reviews

Average Rating: 4.7 Stars

235 Reviews

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