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Supervised Learning

Discover how surprisingly easy & fun machine learning can be! By the end of this course, you’ll be shocked at how well you understand and can apply a wide range of supervised-learning techniques — from simple linear regression to support vector machines (SVM).
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  • DAYS
  • HRS
  • MIN
  • SEC
  • Estimated Time
    21 hours

  • Enroll by
    July 6, 2022

    Get access to classroom immediately on enrollment

  • Prerequisites
    Intermediate Python, Statistics, Calculus, Linear Algebra
In collaboration with
  • Kaggle
  • AWS

What You Will Learn


Supervised Learning

“Machine learning” sounds intimidating, but in reality it is far more accessible than people think. This course is tailored for both students and professionals looking to improve their understanding of supervised machine learning methods (i.e. regression and classification techniques) so they can run their own predictive algorithms, as well as contribute meaningfully to other teams’ ML projects. In addition to working through a range of hands-on exercises, you’ll also apply what you’ve learned to predict potential donors for a fictional charity based on census data.

This course is tailored for both students and professionals looking to improve their understanding of supervised machine learning methods (i.e. regression and classification techniques) so they can run their own predictive algorithms, as well as contribute meaningfully to other teams’ ML projects.

Prerequisite Knowledge

Intermediate Python, Statistics, Calculus, Linear Algebra.

  • Regression

    Learn the difference between Regression and Classification, train a Linear Regression model to predict values, and learn to predict states using Logistic Regression.

  • Perceptron Algorithms

    Learn the definition of a perceptron as a building block for neural networks and the perceptron algorithm for classification.

  • Decision Trees

    Train Decision Trees to predict states and use Entropy to build decision trees, recursively.

  • Naive Bayes

    Learn Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm. Train models using Bayesian Learning and complete an exercise that uses Bayesian Learning for natural language processing.

  • Support Vector Machines

    Learn to train a Support Vector Machines to separate data, linearly. Use Kernel Methods in order to train SVMs on data that is not linearly separable.

  • Ensemble of Learners

    Build professional presentations and data visualizations for quantitative and categorical data. Create pie, bar, line, scatter, histogram, and boxplot charts.

  • Evaluation Metrics

    Calculate accuracy, precision and recall to measure the performance of your models.

  • Training and Tuning Models

    Train and test models with Scikit-learn. Choose the best model using evaluation techniques such as cross-validation and grid search.

  • Course Project: Find Donors for CharityML

    In this project, your goal will be to evaluate and optimize several different supervised learning algorithms to determine which algorithm will provide the highest donation yield while under some marketing constraints.

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LinkedIn ranked AI Specialist as the #1 Emerging Job in 2020, with 74% annual job growth.

Introducing new Udacity Single Courses

Our students asked and we listened. You can now get the in-demand tech skills you need faster and for less money by enrolling in one of our new, one-month Single Courses. You’ll get the specific job-ready skills you need in as little as four weeks and for a fraction of the cost.

Of course if you are looking for a more robust, in-depth education, you can still enroll in one of our 3-6 month Nanodegree programs.

Both programs are part-time and online, and they both offer 24/7 support, quality Udacity-produced content, courses created with the help of top tech companies, and more. You can always start with a Single Course and upgrade to a full Nanodegree program if you like.

All Our Courses Include

Real-world projects from industry experts

Real-world projects from industry experts

With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.
Technical mentor support

Technical mentor support

Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.

Workspaces to see your code in action

Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
Flexible learning program

Flexible learning program

Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Course OfferingsFull list of offerings included:
Enrollment Includes:
Class content
Real-world projects
Project reviews
Project feedback from experienced reviewers
Student services
Technical mentor support
Student community
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
Get timely feedback on your projects.
Reviews By the numbers
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Reviewer Services
  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
Mentors available to answer your questions.
Mentors by the numbers
1,400+ technical mentors
0.85 hours median response time
Mentorship Services
  • Support for all your technical questions
  • Questions answered quickly by our team of technical mentors

Supervised Learning

Get started today

  • Monthly access

    Pay as you go




    Enroll now
    • Maximum flexibility to learn at your own pace.
    • Cancel anytime.
  • Learn

    How to use supervised machine-learning methods to make accurate quantitative and categorical predictions.
  • Average Time

    On average, successful students take 21 hours to complete this program.
  • Benefits include

    • Real-world projects from industry experts
    • Technical mentor support

Program Details

  • Do I need to apply? What are the admission criteria?
    No. This Nanodegree program accepts all applicants regardless of experience and specific background.
  • What are the prerequisites for enrollment?
    In order to succeed in this program, we recommend having working experience Intermediate Python, Statistics, Calculus, and Linear Algebra.
  • How is this course structured?
    The Supervised Learning course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 21 hours.

    The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
  • How long is this course?
    Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.
  • Can I switch my start date? Can I get a refund?
    Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
  • What software and versions will I need in this course?
    You will need a computer running a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.x and supporting packages. Most modern Windows, OS X, and Linux laptops or desktop will work well; we do not recommend a tablet since they typically have less computing power. We will provide you with instructions to install the required software packages.

Supervised Learning

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