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.

• 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

This course is intended for anyone who wants to get a higher level overview of machine learning algorithms. Some coding experience would be useful. However, some level of IBM Watson Studio knowledge is required for model development on Watson Studio.

If you are unfamiliar with Watson Studio, please review the Watson Studio Primer course, W7118G.

• Introduction to the course
• Introduction to the case study and data set
• Introduction to Machine Learning
• Developing the model