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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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  • Estimated time
    21 hours

  • Enroll by
    September 28, 2022

    Get access to classroom immediately on enrollment

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

What You Will Learn

  1. Supervised Learning

    21 hours to complete

    “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.

    Prerequisite knowledge

    1. 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.


                    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 programs include:

                    • 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

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

                    • Workspaces

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

                    • 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 offerings

                    • 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.

                    • Personalized feedback
                    • Unlimited submissions and feedback loops
                    • Practical tips and industry best practices
                    • Additional suggested resources to improve
                    • 1,400+

                      project reviewers

                    • 2.7M

                      projects reviewed

                    • 88/100

                      reviewer rating

                    • 1.1 hours

                      avg project review turnaround time

                    Mentors available to answer your questions.

                    • Support for all your technical questions
                    • Questions answered quickly by our team of technical mentors
                    • 1,400+

                      technical mentors

                    • 0.85 hours

                      median response time

                    Learn with the best.

                    Learn with the best.

                    • Luis Serrano


                      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.

                    • Josh Bernhard

                      Data Scientist at Nerd Wallet

                      Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.

                    Supervised Learning

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                    • 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?
                    • What are the prerequisites for enrollment?
                    • How is this course structured?
                    • How long is this course?
                    • Can I switch my start date? Can I get a refund?
                    • What software and versions will I need in this course?

                    Supervised Learning

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