Coursera MOOC / Non-credit 0

Machine Learning Models in Science

Name: Machine Learning Models in Science
Price: 49 USD
Availability: InStock

About this course

This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering. Along the way, we'll build our mathematical and programming toolbox to prepare ourselves to work with more complicated models. Finally, we'll explored advanced methods such as random forests and neural networks. Throughout the way, we'll be using medical and astronomical datasets. In the final project, we'll apply our skills to compare different machine learning models in Python.

75/100

CourseAsk score

What the provider tells you: 39/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

data preprocessing techniques
implement support vector machines
apply K-means clustering
understand random forests
utilize neural networks
compare machine learning models in Python

Course objectives

to understand the complete machine learning pipeline
to build a mathematical and programming toolbox
to apply machine learning to scientific problems

Machine Learning #python #machine learning #pca #neural networks #data preprocessing #random forests #k-means #lda #svm #medical datasets #astronomical datasets

$49.00

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