This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
Ce cours fait partie de la Spécialisation Machine Learning: Algorithms in the Real World
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Spécialisation Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence InstituteÀ propos de ce cours
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Cours 1 sur 4 dans le
Niveau intermédiaire
Approx. 7 heures pour terminer
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Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Niveau intermédiaire
Approx. 7 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Offert par
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Programme de cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
Introduction to Machine Learning Applications
This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.
3 heures pour terminer
12 vidéos (Total 44 min), 6 lectures, 2 quiz
2 heures pour terminer
Machine Learning in the Real World
This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!
2 heures pour terminer
8 vidéos (Total 34 min), 4 lectures, 1 quiz
1 heure pour terminer
Learning Data
This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.
1 heure pour terminer
9 vidéos (Total 34 min), 2 lectures, 1 quiz
1 heure pour terminer
Machine Learning Projects
This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.
1 heure pour terminer
7 vidéos (Total 35 min), 2 lectures, 1 quiz
Avis
Meilleurs avis pour INTRODUCTION TO APPLIED MACHINE LEARNING
par SR8 oct. 2020
I have been teaching Machine Learning for some time now. This course is really good and comprehensively presents the working of machine learning process in a nutshell. Very good introductory course.
par MM28 oct. 2019
I have really got benefit from this course as a beginner to ML, it gives me the best understanding of ML. I m looking forward to getting into it more efficiently with more practices.
par BW12 nov. 2020
An excellent introduction to the mechanics of ML. Not so deep that coding is required, but simultaneously not so high-level as to be abstract. A very nice intro - thanks for this!
par HL21 juin 2020
An excellent introduction to the fascinating world of machine learning and its endless applications. Loved the emphasis on the evaluation of the business prospect of ML as well.
À propos du Spécialisation Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application.
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