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Learner reviews & feedback for Applied Machine Learning in Python

4.68,780 reviews

About this course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

Featured reviews

4.0Reviewed Aug 14, 2019

- more technical materials, comparisons and better classified details should've been provided, especially to be more proportional to the assignments.-again, subtitles were full of typos

5.0Reviewed Nov 26, 2020

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

5.0Reviewed Aug 19, 2018

Concise and clear presentation of the material with the majority of time focused around using TDD to learn and practice concepts through developing solutions to open ended coding challenges.

4.0Reviewed Jun 22, 2018

It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.

5.0Reviewed Jun 9, 2020

The course was really interesting to go through. All the related assignments whether be Quizzes or the Hands-On really test the knowledge. Kudos to the mentor for teaching us in in such a lucid way.

5.0Reviewed Aug 26, 2017

A lot of techniques packed into a relatively short course. Weeks 2 & 4 are noticably tougher than the other two, so allow plenty of extra time for assignment and quiz in those 2 weeks.

5.0Reviewed Sep 17, 2020

Great content and good instruction. Need to fix the files in the assignments though. It's hard to keep track in the forums and frustrating go back and forth to find out why it's not working.

5.0Reviewed Oct 13, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

4.0Reviewed Jul 6, 2020

assignments were so good. I think there was not enough information given for the quiz tests. And also the code given was not properly explained. But the materials were so good for practice

5.0Reviewed Nov 6, 2021

Great experience i learned a lot in machine learning in python with different terminologies used in applied machine learning. I understand each and every topic which was told by Kevin Collins Sir!

5.0Reviewed Jun 3, 2019

This is an excellent course. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material. Also, the forums are pretty interactive.

5.0Reviewed Jan 12, 2019

In depth course that covers a lot in a short amount of time. If you take some extra time to delve deeper into these topics, you can ensure a great overview of machine learning with python.