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Applied Machine Learning in Python(으)로 돌아가기

Applied Machine Learning in Python, 미시건 대학교

4.7
(3,335개의 평가)

About this Course

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....

최상위 리뷰

대학: FL

Oct 14, 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!!

대학: SS

Aug 19, 2017

the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action

필터링 기준:

585개의 리뷰

대학: Hanchi Wang

May 18, 2019

Good content, some coding assignments are hard to submit(csv file not found)

대학: jose H Chiriboga

May 17, 2019

Comprehensive & thorough

대학: Xia liu

May 16, 2019

GREAT

대학: Andrew Ghattas

May 16, 2019

T

대학: Junaid Latif Shaikh

May 14, 2019

G

대학: Edgar Miguel del Jesús Guzmán Blanco

May 13, 2019

Excelente

대학: Light0617

May 13, 2019

nice

대학: Davide Poletti

May 11, 2019

The course covers a many topics of the ML world.

The exposition of the arguments is well organized.

The assignaments and quizzes are difficult enough to force you to really understand the lessons and learn the arguments but are not impossible to be accomplished.

The teacher are always ready to help you in the course forum.

대학: Magdiel Bruno do Nascimento Américo

May 10, 2019

ok

대학: Quan Sun

May 08, 2019

Course materials are very systematic and instructive, and the professor teaches very clearly. I like this course and recommend it.