TDWI Online Learning - Supervised Machine Learning with Python

Course Description

Data analytics has transformed modern business, and machine learning has emerged as a core competency. In this seven-week hands-on certificate course, you will learn how to perform supervised machine learning using Python, one of the most popular languages in data science and predictive analytics.

You will build both classification and regression models, learn how to test model performance, understand how to prevent overfitting, and explore feature engineering strategies.

What to Expect

You will receive weekly lectures and course work that you can complete on your own schedule, which will be reviewed by the instructor. For your capstone project, you will build a classification or regression model using a provided data set. You will finish the course with complete code examples that you can use and build on in your own work.

You should expect to spend 4-6 hours per week for the duration of this course, beginning on February 10.

Each week you will:

View
Lecture
Complete
Coursework
Submit to
Instructor
Receive
Feedback

4–6 hours per week

This course includes a final capstone project and all students who complete assignments on time will earn a certificate of completion from TDWI.

You Will Learn

What classification and regression models are, and how they are used in supervised machine learning
How to design, train, and validate classification models using logistic regression and decision tree classifier techniques
How to design, train, and validate regression models using linear regression and neural networks
Feature engineering techniques to improve model performance, and why they are important
How to properly set up a workflow for training, cross-validation, and testing
Techniques to avoid overfit, including regularization and early stopping

Geared To*

Data analysts
Aspiring data scientists
Data engineers
Data management professionals
BI practitioners who want to learn about the world of data science and machine learning
Anyone new to machine learning, or new to using Python for machine learning

*This course assumes basic experience using Python.

Enroll Now

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Instructor

William Henry is the director of data science at Riskpulse. As Director, he contributes to company and product strategy, leads data-centric projects, manages data science resources, and gets down in the trenches coding and analyzing data himself. Riskpulse provides shipment intelligence by identifying shipment risks before they occur and providing solutions to avoid those risks. Riskpulse works with many clients, including Anheuser Busch, Honda, MillerCoors, etc.

FAQ

1. How is this course different from other TDWI online learning courses?

Most TDWI online courses are self-paced and can be started/completed at any time. This seven-week course has a weekly schedule beginning on February 10 and ending on March 27, and involves interaction with the instructor and other students. Each week, the instructor will upload a new video and assignment to be viewed and submitted by the end of that week. Students will receive grades and feedback directly from the instructor as the course progresses. The final assignment will be a capstone project due at the end of the course.
2. How will I turn in assignments each week?

Students will log into the TDWI online learning platform and attach assignments into the course portal. All course work and communication will occur in this same portal for easy, centralized access.
3. How do I earn and receive the certificate for this course?

Each student who completes assignments that meet the standard of the instructor will receive a digital certificate of completion from TDWI within 10 business days after the course has finished.
4. Are there any discounts for this course?

Just like with TDWI events, you can save 10% with Early Bird pricing – register by January 17! No coupon code needed.
Teams of three or more save 10% with code TEAM.
TDWI Members save 10% with code MEMBER.
Sign up for course updates above to receive any additional offers.
5. Is there a syllabus for this course?

Absolutely! You can view a detailed syllabus of the topics covered in the course here. Click on the tab labeled "Syllabus."
6. What is the cancellation policy?

If you need to transfer your registration, you may substitute one person in your place by January 24, 2020. If you must cancel your registration, your refund request must be sent via email to [email protected] no later than January 24, 2020. Your payment will be returned, less a 20% cancellation fee. No refunds will be issued after February 3, 2020.
7. Is this course included in subscriptions or bundles?

No, unfortunately, it is not. As this course requires a substantial time commitment for our instructor throughout the seven weeks, this course is not included in subscriptions or bundles (i.e., Summer Sale or other bundle packages). Thank you for understanding.
8. What are the system requirements for my computer?

Your course work and projects will require a computer that meets the following requirements: Windows, MacOS, or Linux / 32- or 64-bit computer / 8 MB available RAM / 600 MB available storage space to download and install Anaconda and data files used in the course.

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