About this Course
최근 조회 27,189

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

You will need mathematical and statistical knowledge and skills at least at high-school level.

완료하는 데 약 19시간 필요

권장: 5 Weeks of study, 5-6 hours per week...

영어

자막: 영어

배울 내용

  • Check

    Define and explain the key concepts of data clustering

  • Check

    Demonstrate understanding of the key constructs and features of the Python language.

  • Check

    Implement in Python the principle steps of the K-means algorithm.

  • Check

    Design and execute a whole data clustering workflow and interpret the outputs.

귀하가 습득할 기술

K-Means ClusteringMachine LearningProgramming in Python

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

You will need mathematical and statistical knowledge and skills at least at high-school level.

완료하는 데 약 19시간 필요

권장: 5 Weeks of study, 5-6 hours per week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 7시간 필요

Week 1: Foundations of Data Science: K-Means Clustering in Python

This week we will introduce you to the course and to the team who will be guiding you through the course over the next 5 weeks. The aim of this week's material is to gently introduce you to Data Science through some real-world examples of where Data Science is used, and also by highlighting some of the main concepts involved.

...
9 videos (Total 22 min), 4 quizzes
9개의 동영상
Introduction to Data Science2m
What is Data?1m
Types of Data1m
Machine Learning3m
Supervised vs Unsupervised Learning2m
K-Means Clustering4m
Preparing your Data1m
A Real World Dataset53
4개 연습문제
Types of Data – Review Information15m
Supervised vs Unsupervised – Review Information15m
K-Means Clustering – Review Information30m
Week 1 Summative Assessment40m
2
완료하는 데 4시간 필요

Week 2: Means and Deviations in Mathematics and Python

...
11 videos (Total 37 min), 2 readings, 11 quizzes
11개의 동영상
2.1 – Introduction to Mathematical Concepts of Data Clustering1m
2.2 – Mean of One Dimensional Lists2m
2.3 – Variance and Standard Deviation3m
2.4 Jupyter Notebooks6m
2.5 Variables4m
2.6 Lists4m
2.7 Computing the Mean3m
2.8 Better Lists: NumPy3m
2.9 Computing the Standard Deviation6m
Week 2 Conclusion31
2개의 읽기 자료
Python Style Guide10m
Numpy and Array Creation20m
10개 연습문제
Population vs Sample – Review Information5m
Mean of One Dimensional Lists – Review Information3m
Variance and Standard Deviation – Review Information4m
Jupyter Notebooks – Review Information20m
Variables – Review Information10m
Lists – Review Information10m
Computing the Mean – Review Information10m
Better Lists – Review Information10m
Computing the Standard Deviation – Review Information10m
Week 2 Summative Assessment40m
3
완료하는 데 3시간 필요

Week 3: Moving from One to Two Dimensional Data

...
16 videos (Total 53 min), 3 readings, 15 quizzes
16개의 동영상
3.1 Multidimensional Data Points and Features2m
3.2 Multidimensional Mean2m
3.3 Dispersion: Multidimensional Variables3m
3.4 Distance Metrics5m
3.5 Normalisation1m
3.6 Outliers1m
3.7 Basic Plotting2m
3.7a Storing 2D Coordinates in a Single Data Structure6m
3.8 Multidimensional Mean4m
3.9 Adding Graphical Overlays5m
3.10 Calculating the Distance to the Mean3m
3.11 List Comprehension3m
3.12 Normalisation in Python5m
3.13 Outliers and Plotting Normalised Data2m
Week 3 Conclusion30
3개의 읽기 자료
Matplotlib Scatter Plot Documentation20m
Matplotlib Patches Documentation10m
List Comprehension Documentation20m
15개 연습문제
Multidimensional Data Points and Features – Review Information3m
Multidimensional Mean – Review Information3m
Dispersion: Multidimensional Variables – Review Information5m
Distance Metrics – Review Information6m
Normalisation – Review Information3m
Outliers – Review Information4m
Basic Plotting – Review Information5m
Storing 2D Coordinates – Review Information4m
Multidimensional Mean – Review Information4m
Adding Graphical Overlays – Review Information6m
Calculating Distance – Review Information6m
List Comprehension – Review Information4m
Normalisation in Python – Review Information4m
Outliers – Review Information2m
Week 3 Summative Assessment25m
4
완료하는 데 5시간 필요

Week 4: Introducing Pandas and Using K-Means to Analyse Data

...
8 videos (Total 37 min), 6 readings, 8 quizzes
8개의 동영상
4.1: Using the Pandas Library to Read csv Files5m
4.1a: Sorting and Filtering Data Using Pandas8m
4.1b: Labelling Points on a Graph4m
4.1c: Labelling all the Points on a Graph3m
4.2: Eyeballing the Data5m
4.3: Using K-Means to Interpret the Data8m
Week 4: Conclusion35
6개의 읽기 자료
Week 4 Code Resources5m
Pandas Read_CSV Function15m
More Pandas Library Documentation10m
The Pyplot Text Function10m
For Loops in Python10m
Documentation for sklearn.cluster.KMeans10m
7개 연습문제
Using the Pandas Library to Read csv Files – Review Information5m
Sorting and Filtering Data Using Pandas – Review Information10m
Labelling Points on a Graph – Review Information5m
Labelling all the Points on a Graph – Review Information5m
Eyeballing the Data – Review Information5m
Using K-Means to Interpret the Data – Review Information5m
Week 4 Summative Assessment40m
5.0
1개의 리뷰Chevron Right

최상위 리뷰

대학: AAJun 4th 2019

This course is at right level for a beginner (python and analytics) while going into details around K means clustering

강사

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Dr Matthew Yee-King

Lecturer
Computing Department, Goldsmiths, University of London
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Dr Betty Fyn-Sydney

Lecturer in Mathematics
Department of Computing, Goldsmiths, University of London
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Dr Jamie A Ward

Lecturer in Computer Science
Department of Computing, Goldsmiths, University of London
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Dr Larisa Soldatova

Reader in Data Science
Department of Computing, Goldsmiths, University of London

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자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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