This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. 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.
Design Thinking and Predictive Analytics for Data Products
University of California, San Diego via Coursera
Overview
Syllabus
- 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.
Taught by
Julian McAuley and Ilkay Altintas
