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 stars71.59%
- 4 stars21.15%
- 3 stars4.84%
- 2 stars1.15%
- 1 star1.25%
TOP REVIEWS FROM APPLIED MACHINE LEARNING IN PYTHON
- 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
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.
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
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
About the Applied Data Science with Python Specialization
Frequently Asked Questions
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