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Udacity
Udacity

Supervised Machine Learning

Course

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
    June 14, 2023

    Get access to classroom immediately on enrollment

  • Skills acquired
    Naive Bayes Classifiers, Support Vector Machines, Decision trees
In collaboration with
  • Kaggle
  • AWS

What You Will Learn

  1. Supervised Machine Learning

    1 month 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

    Intermediate Python, Statistics, Calculus, Linear Algebra.

    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.

                    All Our Courses 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.

                    • Real-time support

                      On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.

                    • 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

                      • Student community
                      • Real-time support

                    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

                    Learn with the best.

                    Learn with the best.

                    • 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.

                    • 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 Machine Learning

                    Get started today

                      • Learn

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

                      • Average Time

                        On average, successful students take 1 month to complete this program.

                      • Benefits include

                        • Real-world projects from industry experts
                        • Real-time 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 Machine Learning course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 1 month.

                        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 Machine Learning

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