Intro to Machine Learning with Pytorch v7.0.16

Nanodegree key: nd229

Version: 7.0.16

Locale: en-us

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.

Content

Part 01 : Introduction to Machine Learning

Welcome to Machine learning with Pytorch

Module 01: Introduction to Machine Learning
- Lesson 01: 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 02: Get Help with Your Account
  What to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support
- Lesson 03: 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.

Part 02 : Supervised Learning

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

Module 01: Supervised Learning
- Lesson 01: 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 02: Linear Regression
  Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works!
- Lesson 03: Perceptron Algorithm
  The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.
- Lesson 04: Decision Trees
  Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
- Lesson 05: 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 06: 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 07: 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 08: Model Evaluation Metrics
  Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!
- Lesson 09: 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: Finding Donors Project
  You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!
  
  Project Description - Finding Donors for CharityML
  
  Project Rubric - Finding Donors for CharityML

Part 03 : 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.

Module 01: Deep Learning
- Lesson 01: 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 02: 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 03: Implementing Gradient Descent
  Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.
- Lesson 04: Training Neural Networks
  Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
- Lesson 05: Deep Learning with PyTorch
  Learn how to use PyTorch for building deep learning models.
- Lesson 06: 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.
  
  Project Description - Create Your Own Image Classifier
  
  Project Rubric - Create Your Own Image Classifier

Part 04 : Unsupervised Learning

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

Module 01: Unsupervised Learning
- Lesson 01: Clustering
  Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
- Lesson 02: Hierarchical and Density Based Clustering
  We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).
- Lesson 03: 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 04: 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 05: 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 06: 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.
  
  Project Description - Creating Customer Segments with Arvato
  
  Project Rubric - Creating Customer Segments with Arvato

Part 05 : Congratulations!

Module 01: Congratulations!
- Lesson 01: Congratulations!
  You've now reached the end of this program!
  - Concept 01: Congratulations!

Content

Part 01 : Introduction to Machine Learning

Part 02 : Supervised Learning

Part 03 : Introduction to Neural Networks with PyTorch

Part 04 : Unsupervised Learning

Part 05 : Congratulations!

Part 06 (Elective): Prerequisite: Python for Data Analysis

Part 07 (Elective): Prerequisite: SQL for Data Analysis

Part 08 (Elective): Prerequisite: Command Line Essentials

Part 09 (Elective): Prerequisite: Git & Github

Part 10 (Elective): Additional Material: Python for Data Visualization

Part 11 (Elective): Additional Material: Statistics for Data Analysis

Part 12 (Elective): Additional Material: Linear Algebra

Part 13 (Career): Career Services

**Part 06 (Elective):** Prerequisite: Python for Data Analysis

**Part 07 (Elective):** Prerequisite: SQL for Data Analysis

**Part 08 (Elective):** Prerequisite: Command Line Essentials

**Part 09 (Elective):** Prerequisite: Git & Github

**Part 10 (Elective):** Additional Material: Python for Data Visualization

**Part 11 (Elective):** Additional Material: Statistics for Data Analysis

**Part 12 (Elective):** Additional Material: Linear Algebra

**Part 13 (Career):** Career Services