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 is part of the Applied Data Science with Python Specialization
About this Course
What you will learn
Describe how machine learning is different than descriptive statistics
Create and evaluate data clusters
Explain different approaches for creating predictive models
Build features that meet analysis needs
Skills you will gain
- Python Programming
- Machine Learning (ML) Algorithms
- Machine Learning
Start working towards your Master's degree
Syllabus - What you will learn from this course
Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
Module 2: Supervised Machine Learning - Part 1
Module 3: Evaluation
Module 4: Supervised Machine Learning - Part 2
- 5 stars71.60%
- 4 stars21.15%
- 3 stars4.84%
- 2 stars1.15%
- 1 star1.24%
TOP REVIEWS FROM APPLIED MACHINE LEARNING IN PYTHON
Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.
Globally, very good course - strongly recommanded
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.
It feels good to learn something new and highly skilled demand in Engineering. Thanks to Coursera and instructor for providing such a wonderful opportunity of learning through your platform.
This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses
About the Applied Data Science with Python Specialization
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.