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Feature Engineering for ML
When
December 9, 2024 - January 19, 2025
Course Tuition
$1,950
No Up-front Payment
Modal now offers a deferred direct bill payment option for Booz Allen employees.
Learn more
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Who Is This For?
Data Scientists interested in improving the performance of their machine learning models.
Any prerequisites?
Python
- Intermediate Python knowledge, including data structures, loops, and functions.
- Experience with data manipulation using the Pandas library.
Statistics and ML
- Knowledge of linear and logistic regression and basic principles of machine learning.
- Note: the following Modal courses would successfully prepare a learner for this course: Python for Data Science, Preparing Data for Analysis with Python, Applying Statistical Thinking with Python, and Introduction to Supervised Machine Learning.
What will I be able to do after this Course?
- Prepare data for input into a machine learning model, including feature creation, transformation, scaling, and categorical encoding.
- Use feature importance, wrapper methods, and embedded methods to filter and select features for inclusion in a machine-learning model.
- Reduce the dimensionality of a dataset using principal component analysis and partial least squares.
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
Sprint 1: Data Transformations and Encoding
Create new features, transform existing features using methods such as standardization and normalization, and encode categorical variables using a variety of methods.
Sprint 2: Feature Selection
Select features for inclusion in a machine learning model using filter methods, step-forward and backward algorithms, and regularization.
Sprint 3: Dimensionality Reduction
Use methods such as principal component analysis (PCA) and partial least squares to reduce the dimensionality of a dataset without losing a large amount of information.
Why Modal?
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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
Hands-on labs allow you to play with new tools and concepts to build real skills.
Community of Peers
You will be part of a learning community were support is abundant.
Asynchronous Learning
Self-paced learning is scheduled for each learner, with a dashboard to help you keep on track.
Other Courses
“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 FlexEdThe 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.
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