This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.
IBM이 강좌에 대하여
최근 조회 99,229회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 8시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
귀하가 습득할 기술
Artificial Intelligence (AI)Machine LearningFeature EngineeringStatistical Hypothesis TestingExploratory Data Analysis
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 8시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
제공자:
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 1시간 필요
A Brief History of Modern AI and its Applications
Artificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module we will go over a quick introduction to AI and Machine Learning and we will visit a brief history of modern AI. We will also explore some of the current applications of AI and Machine Learning for you to think about how you want to leverage them in your day to day business practice or personal projects.
완료하는 데 1시간 필요
8개 동영상 (총 47분), 2 개의 읽기 자료, 3 개의 테스트
8개의 동영상
Introduction to Artificial Intelligence and Machine Learning5m
Machine Learning and Deep Learning10m
History of AI7m
History of Machine Learning and Deep Learning5m
Modern AI6m
Applications3m
Machine Learning Workflow6m
2개의 읽기 자료
Course Prerequisites4m
Summary/Review3m
3개 연습문제
Check for Understanding5m
Check for Understanding5m
Module 1 Quiz5m
완료하는 데 4시간 필요
Retrieving Data, Exploratory Data Analysis, and Feature Engineering
Good data is the fuel that powers Machine Learning and Artificial Intelligence. In this module you will learn how to retrieve data from different sources, how to clean it to ensure its quality, and how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling.
완료하는 데 4시간 필요
16개 동영상 (총 148분), 5 개의 읽기 자료, 5 개의 테스트
16개의 동영상
Demo: Reading Data Demo Jupyter Notebook7m
Lab Solution: Reading in Database Files5m
Data Cleaning6m
Handling Missing Values and Outliers10m
EDA - Part 15m
EDA - Part 212m
Solution: EDA Notebook - Part 15m
Solution: EDA Notebook - Part 210m
Solution: EDA Notebook - Part 36m
Solution: EDA Notebook - Part 48m
Feature Engineering and Variable Transformation - Part 18m
Feature Engineering and Variable Transformation - Part 211m
Solution: Feature Engineering Lab - Part 111m
Solution: Feature Engineering Lab - Part 212m
Solution: Feature Engineering Lab-Part 39m
5개의 읽기 자료
Demo: Reading in Database Files (Activity)10m
Lab: Reading in Database Files (Activity)10m
Exploratory Data Analysis Lab (Activity)10m
Feature Engineering Demo (Activity)10m
Summary/Review3m
5개 연습문제
Check for Understanding5m
Check for Understanding5m
Check for Understanding5m
Check for Understanding5m
Module 2 Quiz5m
완료하는 데 3시간 필요
Inferential Statistics and Hypothesis Testing
Inferential statistics and hypothesis testing are two types of data analysis often overlooked at early stages of analyzing your data. They can give you quick insights about the quality of your data. They also help you confirm business intuition and help you prescribe what to analyze next using Machine Learning. This module looks at useful definitions and simple examples that will help you get started creating hypothesis around your business problem and how to test them.
완료하는 데 3시간 필요
10개 동영상 (총 83분), 2 개의 읽기 자료, 4 개의 테스트
10개의 동영상
Estimation and Inference - Part 210m
Estimation and Inference - Part 35m
Hypothesis Testing8m
Type 1 vs Type 2 Error9m
Significance Level and P-Values - Part 17m
Significance Level and P-Values - Part 25m
Hypothesis Testing Demo - Part 15m
Hypothesis Testing Demo - Part 210m
Correlation vs Causation9m
2개의 읽기 자료
Hypothesis Testing Demo (Activity)10m
Summary/Review3m
3개 연습문제
Check for Understanding5m
Check for Understanding5m
Module 3 Quiz5m
검토
EXPLORATORY DATA ANALYSIS FOR MACHINE LEARNING의 최상위 리뷰
DS 제공2020년 11월 30일
The only reason that I do not give it 5 stars is because the website of coursera is not good enough to handle the peer review assignments at the end of the course.
AM 제공2020년 12월 17일
Good introduction. The time estimates to complete assignments are off. Need a lot more material and direction for assignments to aid learning.
FF 제공2021년 1월 18일
if you really make the exercises and the final assignment the course is really contributes to your better understand of Data Analysis
AC 제공2020년 9월 30일
It is a really insightful and interactive learning experience. Furthermore, the trainers and coaches were very knowledgable.
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