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Learn the Basics of Machine Learning with IBM Watson Studio
Course Description
Overview
This course introduces a case study, dataset, common machine learning algorithms, and developing a machine learning model with IBM Watson Studio.
In the first module, you will be introduced to Amsel Fit, our case study, a fictional company that produces dietary products, supplements, and healthy foods. The company faces a drop in sales and decides to analyze its marketing approach and predict which customers will or will not be likely to continue buying products. Also, you will be introduced to the dataset that we will be using to develop a machine learning model.
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In the next module, you will be introduced to machine learning models including supervised, unsupervised learning that include classification and regression models, deep learning and reinforcement learning approaches.
In the third module, based on module one and module two, you will develop a supervised machine learning model with the dataset provided to predict which customer will buy or will not buy again after a coupon is provided.
Objectives
- Define machine learning
- Explain key terms of machine learning
- Outline supervised and unsupervised models
- Define deep learning and reinforcement learning
- List model development steps
- Develop the Random Forest model
- Evaluate the results
Prerequisites
If you are unfamiliar with Watson Studio, please review the Watson Studio Primer course, W7118G.
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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
IBM Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.
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.
System Requirements
To participate in this course, the student workstation must meet the following hardware requirements:- A modern browser - A modern web browser, such as Chrome, Firefox, Microsoft Edge, or Safari.
- A good network connection - A good network connection for connecting to Virtual Machines. This means:
- At least 1.2 Mbps for each concurrent browser session with a Virtual Machine.
- Low latency (150ms or less) to the region where your Virtual Machines are located
- Use Connectivity Checker to test your browser and network access.
- Use Speed test to test your bandwidth and latency to our remote lab provider regions.
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