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.
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IBMÀ propos de ce cours
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Niveau intermédiaire
Approx. 8 heures pour terminer
Anglais
Sous-titres : Anglais
Compétences que vous acquerrez
Artificial Intelligence (AI)Machine LearningFeature EngineeringStatistical Hypothesis TestingExploratory Data Analysis
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 8 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
1 heure pour terminer
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.
1 heure pour terminer
8 vidéos (Total 47 min), 2 lectures, 3 quiz
8 vidéos
Introduction to Artificial Intelligence and Machine Learning5 min
Machine Learning and Deep Learning10 min
History of AI7 min
History of Machine Learning and Deep Learning5 min
Modern AI6 min
Applications3 min
Machine Learning Workflow6 min
2 lectures
Course Prerequisites4 min
Summary/Review3 min
3 exercices pour s'entraîner
Check for Understanding5 min
Check for Understanding5 min
Module 1 Quiz5 min
4 heures pour terminer
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.
4 heures pour terminer
16 vidéos (Total 148 min), 5 lectures, 5 quiz
16 vidéos
Retrieving Data13 min
Demo: Reading Data Demo Jupyter Notebook7 min
Lab Solution: Reading in Database Files5 min
Data Cleaning6 min
Handling Missing Values and Outliers10 min
EDA - Part 15 min
EDA - Part 212 min
Solution: EDA Notebook - Part 15 min
Solution: EDA Notebook - Part 210 min
Solution: EDA Notebook - Part 36 min
Solution: EDA Notebook - Part 48 min
Feature Engineering and Variable Transformation - Part 18 min
Feature Engineering and Variable Transformation - Part 211 min
Solution: Feature Engineering Lab - Part 111 min
Solution: Feature Engineering Lab - Part 212 min
Solution: Feature Engineering Lab-Part 39 min
5 lectures
Demo: Reading in Database Files (Activity)10 min
Lab: Reading in Database Files (Activity)10 min
Exploratory Data Analysis Lab (Activity)10 min
Feature Engineering Demo (Activity)10 min
Summary/Review3 min
5 exercices pour s'entraîner
Check for Understanding5 min
Check for Understanding5 min
Check for Understanding5 min
Check for Understanding5 min
Module 2 Quiz5 min
3 heures pour terminer
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.
3 heures pour terminer
10 vidéos (Total 83 min), 2 lectures, 4 quiz
10 vidéos
Estimation and Inference - Part 210 min
Estimation and Inference - Part 35 min
Hypothesis Testing8 min
Type 1 vs Type 2 Error9 min
Significance Level and P-Values - Part 17 min
Significance Level and P-Values - Part 25 min
Hypothesis Testing Demo - Part 15 min
Hypothesis Testing Demo - Part 210 min
Correlation vs Causation9 min
2 lectures
Hypothesis Testing Demo (Activity)10 min
Summary/Review3 min
3 exercices pour s'entraîner
Check for Understanding5 min
Check for Understanding5 min
Module 3 Quiz5 min
Avis
Meilleurs avis pour EXPLORATORY DATA ANALYSIS FOR MACHINE LEARNING
par AC30 sept. 2020
It is a really insightful and interactive learning experience. Furthermore, the trainers and coaches were very knowledgable.
par AF6 nov. 2020
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
par MA5 nov. 2020
In week 2, the lessons were a bit in rush and it would be better to have a bit more detailed discussion.
par AA11 oct. 2020
Great....Very nice explanations of all things I rate 5/5 ...Amazing course from IBM
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