JL
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.
Applied Machine Learning in Python
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. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Status: Applied Machine Learning
Status: Feature Engineering
IntermediateCourse32 hours
Featured reviews
RS
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.
AS
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.
FL
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!!
AA
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.
IP
5.0Reviewed Mar 28, 2020
Very well structured and informative course ! All the lectures are concise and give enough context for self-exploration. The assignments provide are a good hands-on experience as well !!
PS
5.0Reviewed Apr 2, 2018
Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!
VS
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.
MB
5.0Reviewed Jun 18, 2017
Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.
S
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!
DB
5.0Reviewed Oct 22, 2020
EXTREMELY USEFUL AND GOOD COURSE, CONGRATULATIONS TO ALL THE PEOPLE INVOLVE.Honestly, I never thought I could learn so much in an online course, excited for the rest of the specialization
MA
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