Download Coursera – Applied Machine Learning in Python 2022-8

Description

Applied Machine Learning in Python course. In this course, you will learn about machine learning in a practical way. It should also be noted that this course focuses more on techniques and methods than on the statistical and mathematical topics behind these methods. This course begins by discussing how machine learning differs from descriptive statistics and introduces the scikit library. The issue of data dimensions will be discussed and the task of data clustering as well as the evaluation of clusters will be examined. Supervised approaches to building predictive models are described, and participants can apply predictive modeling methods (such as cross validation and overfitting) while understanding process issues related to data generalizability. The course will conclude with a look at more advanced techniques such as ensemble construction and the practical limitations of predictive models. At the end of this course, students will be able to identify the difference between supervised (classification) and unsupervised (clustering) techniques, identify which technique to apply to a particular data set and need, the characteristics Engineer to meet that requirement, and write Python code to build your model.

What you will learn in the Applied Machine Learning in Python training series

Course details

• Publisher: Coursera
• English language
• Duration: 4 hours
• Number of courses: 4
• teacher : Kevyn Collins-Thompson
• File format: mp4
• Course Level: Introductory to Advanced
• Presenting Institution/University: Michigan University

Courses available in the Applied Machine Learning in Python educational collection

Prerequisites of the training set

Pictures

Sample video of the course

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Download file – 489 MB

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489 MB