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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)
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What You'll Learn

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.

Who Should Attend

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression and Classification techniques in a business setting.
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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.

Learning Journey

Coming Soon...

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

Frequently Asked Questions (FAQs)

  • Why get IBM certified?

    IBM certifications validate your skills and expertise in a wide range of technologies, including hybrid cloud, data & AI, security, IT infrastructure, and business applications.

    These certifications are globally recognized and can help you advance your career, increase your earning potential, and demonstrate your commitment to professional development.

    IBM-certified professionals are in high demand, making them valuable assets to any organization seeking to leverage IBM's innovative solutions.

  • What to expect for the examination?

    IBM offers a variety of certification exams at different levels (Basic, Intermediate, and Advanced) covering various technologies and job roles.

    Exams typically consist of multiple-choice questions and may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is IBM certification valid for?

    The validity period of IBM certifications varies. Some certifications, particularly those focused on specific product versions or technologies, may have expiration dates.

    However, many IBM certifications, especially those focused on broader skills or newer technologies, do not have an expiration date.

    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 16 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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