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Machine Learning Specialist - Supervised Learning: Regression and Classification
Course Description
Overview
This course introduces you to two of the main types of modelling families of supervised Machine Learning: Regression and Classification. You start by learning how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. You then learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:
- IBM Learning for Data and AI Individual Subscription (SUBR022G)
- IBM Learning for Data and AI Enterprise Subscription (SUBR004G)
- IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)
Objectives
By the end of this course you should be able to:
- Differentiate uses and applications of classification and regression in the context of supervised machine learning.
- Describe and use linear regression models, and use decision tree and tree-ensemble models.
- Use a variety of error metrics to compare and select a linear regression model or classification model that best suits your data.
- Articulate why regularization might help prevent overfitting.
- Use regularization regressions: Ridge, LASSO, and Elastic net.
- Use oversampling as techniques to handle unbalanced classes in a data set.
Audience
This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression and Classification techniques in a business setting.
Prerequisites
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
Topics
1. Introduction to Supervised Machine Learning and Linear Regression
2. Data Splits and Cross Validation
3. Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
4. Logistic Regression
5. K Nearest Neighbors
6. Support Vector Machines
7. Decision Trees
8. Ensemble Models
9. Modeling Unbalanced Classes
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Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Self-Paced Training Terms & Conditions
THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 365 DAYS TO ACCESS THE COURSE.
This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
Upon receipt of the Order Confirmation Letter which includes your Enrollment Key (Access code); the course begins its twelve (12) month access period. IMPORTANT!!! If your course provides access to a hands-on lab (Virtual Lab Environment), you will have a specific number of days (typically 30 days) on the remote lab platform to complete your hands-on labs. Do not start your lab until you are ready to use your lab time effectively. Time allotted in the virtual lab environment will be indicated once you apply the enrollment key. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Note: This does not add additional days to your Lab Environment time frame.
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