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

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K-Means ClusteringMachine LearningProgramming in Python
공유 가능한 수료증
완료 시 수료증 획득
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 29시간 필요
영어

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런던 대학교

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강의 계획 - 이 강좌에서 배울 내용

콘텐츠 평가Thumbs Up95%(5,070개의 평가)Info
1

1

완료하는 데 7시간 필요

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

완료하는 데 7시간 필요
9개 동영상 (총 22분)
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

2

완료하는 데 4시간 필요

Week 2: Means and Deviations in Mathematics and Python

완료하는 데 4시간 필요
11개 동영상 (총 37분), 4 개의 읽기 자료, 11 개의 테스트
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
4개의 읽기 자료
Population vs Sample, Bias10m
Variability, Standard Deviation and Bias10m
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

3

완료하는 데 8시간 필요

Week 3: Moving from One to Two Dimensional Data

완료하는 데 8시간 필요
16개 동영상 (총 53분), 10 개의 읽기 자료, 15 개의 테스트
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
10개의 읽기 자료
Multidimensional Data Points and Features Recap10m
Multidimensional Mean Recap10m
Multidimensional Variables Recap10m
Distance Metrics Recap10m
Normalisation Recap10m
Note on Matplotlib10m
Matplotlib Scatter Plot Documentation20m
Matplotlib Patches Documentation10m
List Comprehension Documentation20m
3.12 Errata10m
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 Information30m
Basic Plotting – Review Information5m
Storing 2D Coordinates – Review Information30m
Multidimensional Mean – Review Information30m
Adding Graphical Overlays – Review Information30m
Calculating Distance – Review Information30m
List Comprehension – Review Information30m
Normalisation in Python – Review Information30m
Outliers – Review Information30m
Week 3 Summative Assessment25m
4

4

완료하는 데 4시간 필요

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

완료하는 데 4시간 필요
8개 동영상 (총 37분), 6 개의 읽기 자료, 8 개의 테스트
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

검토

FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON의 최상위 리뷰

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