Julian McAuley
8.5/10
(Our Score)
Product is rated as #194 in category Python
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.
Instructor Details
Votes: 0
Courses : 3
Specification: Design Thinking and Predictive Analytics for Data Products
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5 reviews for Design Thinking and Predictive Analytics for Data Products
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| Price | Free |
|---|---|
| Provider | |
| Duration | 10 hours |
| Year | 2019 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
Design Thinking and Predictive Analytics for Data Products
FREE
Surendar R –
Course contents are very good, able to learn a lot. However, very frustrating system is project assignment submissions of last week has to wait for infinite time to be graded by peers. Wait time to get feedback on your submission is extremely long and very annoying to have such a long wait. Either, mentors of this course should step forward and help in this review process at periodic intervals or, this system should go away and it should NOT be mandatory requirement to complete this course. For poor grading system that is in place for project submission am submitting 2 stars, otherwise I would have gone for 4 or 5 hands down
Reinhold L –
Very informative course and very good documentation as well as practical examples.
Pratik P –
This course takes you from learning to do many data analytics and Machine learning tasks manually to all the way doing it much more efficiently using the standard libraries. Overall, a great course to give you a rock solid foundation in this field.
Clarence E Y –
This course provides practical techniques used for regression and classification of datasets. These techniques are important to gain understanding and experience in building a data pipeline in the design process. Logistic Regression, Support Vector Machines, and K Means approaches are covered along with Jaccard, F 1 error evaluation and Gradient Descent.
Olugbenga O A –
The Technical parts felt too rushed.