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Classify Data and Make Predictions: Ensemble Methods and Naive Bayes

Solve classification problems with several common and powerful approaches: Decision Trees, Ensemble Methods, and Naive Bayes Classifiers. Modal  - A better way to learn technical skills.
When
October 14, 2024 - November 24, 2024
Registration closes on October 3, 2024
Course Tuition
$1,950
Want to take more than one course? Send an email to support@modal.com to buy our 1-year subscription for $3,900.
No Up-front Payment
Modal now offers a deferred direct bill payment option for Booz Allen employees.
‍Learn more
Who Is This For?

Data Professionals interested in building on their foundational supervised ML knowledge by learning new models for classifying data and making predictions.

Any prerequisites?

Machine Learning

• Knowledge of linear and logistic regression and basic principles of machine learning.

• Familiarity with supervised learning algorithms.

• Identifying regression vs. classification ML problems.

• Model evaluation methods including train/test split and statistics such as mean absolute error, accuracy, precision, recall, and F1 score.

• The concept of overfitting and underfitting.

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Python

• Strong familiarity with Python, including data structures, loops, functions, code debugging, and reading error messages.

• Experience with data manipulation using the Pandas library.

What will I be able to do after this Course?

By the end of the course, you’ll know how to develop ML pipelines to implement Decision Trees, Ensemble Methods, and Naive Bayes Classifiers to solve classification and prediction problems, and reliably choose the right algorithm for the job.

NEED HELP DECIDING?
Book time with a learning expert.

A Typical Week

Monday
Self Study
Kick-off new topic with self-study & online learning
  • Coaches support learners hitting roadblocks
  • Manager check-in to bring learning into company context
Tuesday
Wednesday
Labs
Learning material leads into practice environment & labs
  • Coaches support learners hitting roadblocks
  • Pair programming to bring learning into company context
  • Community allows students to help each other
Thursday
Live Event
Interactive live session hosted by Coaches
  • Community allows students to help each other
  • Community Groups host expert AMAs & guided community discussions
Friday
Projects
Work on a weekly project
  • Community allows students to help each other
  • Group projects
  • Coaches support learners hitting roadblocks
Saturday
sunday
Work at your own pace
Expert coaching and actionable feedback from Coaches
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    Course Schedule

    Live Sessions every
    Sprint 1: Naive Bayes Classifiers
    Learn how to implement a machine learning pipeline using a Naive Bayes model and understand the math behind it. You’ll then build Naive Bayes models to help an energy company predict wind turbine failure.
    Sprint 2: Decision Trees, Random Forests, and Ensemble Methods
    Build your conceptual knowledge of decision trees, random forests, and ensemble methods, and develop an ML pipeline to implement them. You’ll then use decision trees, random forests, and ensemble methods to improve your models to predict wind turbine failures.
    Sprint 3: Boosting
    Build your conceptual knowledge of boosting and learn how to implement boosted decision tree models to improve performance. You’ll apply your knowledge by improving your existing models, and then making recommendations and cost estimates for turbine maintenance.

    Why Modal?

    Projects & Practice
    Real world exercises contextualize learning in real-world context.
    On-Demand Coach Support
    You are never alone. Coaches are always present and can help you!
    Live Sessions
    Hear from guest speakers and expert instructors through engaging lectures.
    Technical Labs
    Technical Labs
    Hands-on labs allow you to play with new tools and concepts to build real skills.
    Modal Community
    Community of Peers
    You will be part of a learning community were support is abundant.
    Asynchronous Learning
    Asynchronous Learning
    Self-paced learning is scheduled for each learner, with a dashboard to help you keep on track.

    Other Courses

    Python for Data Science
    Applying Statistical Thinking
    Data Analysis With SQL

    “I love the quantity & quality of learning materials, the interactivity, the live sessions, the coaches, are invaluable. I can really feel the difference in the level of engagement that Modal has to every participant compared to an ordinary course."

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    - Veselina Stoyanova - Reporting Analyst, EMAG

    Learn more about FlexEd

    We are excited that Modal now offers a deferred direct bill payment option for Booz Allen employees.

    The deferred direct bill payment option enables employees to enroll in learning opportunities with no upfront costs. This payment option will require the employee to sign a Family Educational Rights and Privacy Act (FERPA) agreement with Modal to release grades/completion to Booz Allen to satisfy the FlexEd Program completion requirement.

    Note, Modal may also be used for the FlexEd Program reimbursement payment option. See the full FlexEd Program Policy & FAQs.
    Learn more about FlexEd
    Coming Soon!
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    Check back in a few weeks or reach out to support@modal.io if you have questions.
    Need help? Contact us
    Apply for FlexEd
    FlexEd Application
    Learn more about FlexEd on Degreed or review the
    FlexEd Program Policy & FAQs

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