Design Thinking and Predictive Analytics for Data Products

In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Topics
Supervised Learning & Regression
We will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of supervised learning and regression.
Features
We will learn what features are in a dataset and how we can work with them through cleaning, manipulation, and analysis in Jupyter notebooks.
Classification
We will learn about classification and several ways you can implement it, such as K-nearest neighbors, logistic regression, and support vector machines.
Gradients
We will learn the importance of properly training and testing a model. We will also implement gradient descent in both Python and TensorFlow.


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