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
Design Thinking and Predictive Analytics for Data Products
This course is part of Python Data Products for Predictive Analytics Specialization
Instructors: Julian McAuley
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4.5
(65 reviews)
Intermediate level
Some related experience required
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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Shareable certificate
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Assessments
12 assignments
Taught in English
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This course is part of the Python Data Products for Predictive Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
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.
What's included
5 videos4 readings3 assignments2 discussion prompts
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.
What's included
4 videos1 reading3 assignments
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.
What's included
4 videos3 assignments1 discussion prompt
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.
What's included
5 videos3 assignments
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.
What's included
2 readings1 peer review1 discussion prompt
Instructors
Instructor ratings
4.5 (13 ratings)Offered by
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Reviewed on May 7, 2021
It was great course ,helped me in getting better understanding of data and do predictive modeling.
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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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