Design Thinking and Predictive Analytics for Data Products

Por: Coursera . en: , ,

  • Week 1: Supervised Learning & Regression
    • Welcome to the second course in this specialization! This week, 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.
  • Week 2: Features
    • This week, we will learn what features are in a dataset and how we can work with them through cleaning, manipulation, and analysis in Jupyter notebooks.
  • Week 3: Classification
    • This week, we will learn about classification and several ways you can implement it, such as K-nearest neighbors, logistic regression, and support vector machines.
  • Week 4: Gradient Descent
    • This week, we will learn the importance of properly training and testing a model. We will also implement gradient descent in both Python and TensorFlow.
  • Final Project
    • In the final week of this course, you will continue building on the project from the first course of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data.