Machine Learning - Introduction

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

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

Learn what resources are available to you via Udacity's career-related tools.

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

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.

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

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

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

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.

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

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.

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

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

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

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.

Project Description - Optimize Your GitHub Profile

Project Rubric - Optimize Your GitHub Profile

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.

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

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

Learn how to use PyTorch for building deep learning models.

In this project, you'll build a Python application that can train an image classifier on a dataset, then predict new images using the trained model.

Project Description - Create Your Own Image Classifier

Project Rubric - Create Your Own Image Classifier

You're building your online presence. Now learn how to share your story, understand the tech landscape better, and meet industry professionals.

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Project Description - Improve Your LinkedIn Profile

Project Rubric - Improve Your LinkedIn Profile

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

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

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

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.

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

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

You've now reached the end of this program!

Welcome to Introduction to Python! Here's an overview of the course.

Familiarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, built-in functions, and style guidelines.

Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.

Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.

Setup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.

Learn the basics of NumPy and how to use it to create and manipulate arrays.

Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.

In this section, you will gain knowledge about SQL basics for working with a single table. You will learn the key commands to filter a table in many different ways.

In this lesson, you will learn how to combine data from multiple tables together.

In this lesson, you will learn how to aggregate data using SQL functions like SUM, AVG, and COUNT. Additionally, CASE, HAVING, and DATE functions provide you an incredible problem solving toolkit.

In this lesson, you will be learning to answer much more complex business questions using nested querying methods - also known as subqueries.

Cleaning data is an important part of the data analysis process. You will be learning how to perform data cleaning using SQL in this lesson.

Compare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis: window functions.

Learn advanced joins and how to make queries that run quickly across giant datasets. Most of the examples in the lesson involve edge cases, some of which come up in interviews.

The Unix shell is a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer.

Version control is an incredibly important part of a professional programmer's life. In this lesson, you'll learn about the benefits of version control and install the version control tool Git!

Now that you've learned the benefits of Version Control and gotten Git installed, it's time you learn how to create a repository.

Knowing how to review an existing Git repository's history of commits is extremely important. You'll learn how to do just that in this lesson.

A repository is nothing without commits. In this lesson, you'll learn how to make commits, write descriptive commit messages, and verify the changes you're about to save to the repository.

Being able to work on your project in isolation from other changes will multiply your productivity. You'll learn how to do this isolated development with Git's branches.

Help! Disaster has struck! You don't have to worry, though, because your project is tracked in version control! You'll learn how to undo and modify changes that have been saved to the repository.

You'll learn how to create remote repositories on GitHub and how to get and send changes to the remote repository.

In this lesson, you'll learn how to fork another developer's project. Collaborating with other developers can be a tricky process, so you'll learn how to contribute to a public project.

You'll learn how to send suggested changes to another developer by using pull requests. You'll also learn how to use the powerful git rebase command to squash commits together.

In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.

Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.

In this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.

In this lesson, build up from your understanding of individual variables and learn how to use matplotlib and seaborn to look at relationships between two variables.

In this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.

Previous lessons covered how you could use visualizations to learn about your data. In this lesson, see how to polish up those plots to convey your findings to others!

Put to practice the concepts you've learned about exploratory and explanatory data visualization in this case study on factors that impact diamond prices.

In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.

In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.

Learn to ask the right questions, as you learn about Simpson's Paradox.

Gain the basics of probability using coins and die.

Learn about one of the most popular distributions in probability - the Binomial Distribution.

Not all events are independent. Learn the probability rules for dependent events.

Learn one of the most popular rules in all of statistics - Bayes rule.

Take what you have learned in the last lessons and put it to practice in Python.

Learn the mathematics behind moving from a coin flip to a normal distribution.

Learn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.

Learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.

Learn the necessary skills to create and analyze the results in hypothesis testing.

Work through a case study of how A/B testing works for an online education company called Audacity.

Use python to fit linear regression models, as well as understand how to interpret the results of linear models.

Learn to apply multiple linear regression models in python. Learn to interpret the results and understand if your model fits well.

Learn to apply logistic regression models in python. Learn to interpret the results and understand if your model fits well.

Take a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.

Learn about vectors, the basic building block of Linear Algebra.

Learn how to scale and add vectors and how to visualize the process.

What is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.