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
Introduction to Applied Machine Learning
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À propos de ce cours
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Cours 1 sur 4 dans le
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Niveau intermédiaire
Approx. 6 heures pour terminer
Anglais
Sous-titres : Anglais
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 du 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
12 vidéos
Instructor Introduction1 min
Introduction to Course 12 min
What is Artificial Intelligence and Machine Learning?5 min
What about Data Science?3 min
The Machine Learning Process4 min
The Three Kinds of Machine Learning3 min
Classification: What is it and how does it work?3 min
Regression: Fitting lines and predicting numbers3 min
Unsupervised Learning4 min
Reinforcement Learning6 min
Weekly Summary1 min
6 lectures
What about Deep Learning? (supplemental)10 min
Fooling Neural Networks (supplemental)10 min
How to Curate A Ground Truth For Your Business Dataset (Required)10 min
Learning From Multiple Annotators: A Survey (supplemental)10 min
Inferring the Ground Truth Through Crowdsourcing (supplemental)10 min
Semi Supervised Learning (required)10 min
2 exercices pour s'entraîner
Concepts and Definitions20 min
Identifying Machine Learning Techniques10 min
1 heure 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!
1 heure pour terminer
8 vidéos (Total 34 min), 4 lectures, 1 quiz
8 vidéos
Features and transformations of raw data6 min
Farmer Betty and Her Precision Agriculture Plans3 min
What to consider when using your QuAM2 min
Broad Examples Narrowed Down4 min
Identify Business Evaluation4 min
Everything is a Proxy4 min
Weekly Summary2 min
4 lectures
A Brief Introduction into Precision Agriculture10 min
Farmer Betty Tried Unsupervised Learning (required)10 min
Data is Central to Your ML Problem (required)10 min
Martin Zinkevich's Rules for ML (supplemental)10 min
1 exercice pour s'entraîner
Machine Learning in the Real World Review
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
9 vidéos
How Much Data Do I Need?4 min
Ethical Issues4 min
Bias in Data Sources3 min
Noise and Sources of Randomness5 min
Image Classification Example3 min
Data Cleaning: Everybody's favourite task4 min
Why you need to set up a Data Pipeline4 min
Weekly Summary1 min
2 lectures
Data Protection Laws (required)10 min
Government readings on data privacy (supplemental)10 min
1 exercice pour s'entraîner
Understanding Data for ML
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
7 vidéos
MLPL Overview5 min
MLPL as experienced by Farmer Betty3 min
Exploring the process of problem definition7 min
Assessing your QuAM for use in your Business6 min
Technically Assessing the Strength of your QuAM6 min
Different Kinds of Wrong4 min
Weekly Summary2 min
2 lectures
Machine Learning Process Lifecycle Explained10 min
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10 min
1 exercice pour s'entraîner
Understanding Machine Learning Projects
Avis
4.6
Meilleurs avis pour INTRODUCTION TO APPLIED MACHINE LEARNING
par MMOct 28, 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 AASep 11, 2019
very comprehensive course on applied machine learning. the most interesting information in this course is business needs for ML and what it's requirement to have a good QuAM.
par KSOct 13, 2019
This course will give the actual understanding especially focusing on different types of Machine learning with real examples. I'm so excited to learn more about it.
par VESep 21, 2019
I have taken a great deal of knowledge in this course and have applied it to what I have seen in the ML and AI world. This was a great introduction!
par PRSep 27, 2019
I enjoyed this course. It was a good refresher but I believe that it is also good for people that want to understand this Machine Learning hype...
par BTJan 5, 2020
Course gives a broader understanding of any Machine Learning project, how to approach and what are the important things to keep in mind.
par OHOct 24, 2019
This course was very helpful and fruitful. It is a good entry for machine learning and I really recommend it. Thanks, Anna, Thanks, amii
par FMNov 11, 2019
Great Introductory course, it was delivered very good. I picked up a lot of different definitions which were simplified. Very Good!
par UJan 24, 2020
The course was a really good one introducing you to the machine learning and how you should think and approach an ML problem.
par MTAug 30, 2019
Anna Koop has a very engaging onscreen personality that makes it easy to follow her explanations of machine learning.
par AHMar 29, 2020
It is a good start. For sure, this course is recommended for everyone who is not in the computer science world.
par ECSep 21, 2019
Very informative, highly recommended to anyone with an interest in starting any ML project in the future.
par PLSep 7, 2019
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par EGJan 8, 2020
The whole specialization is extremely useful for people starting in ML. Highly recommended!
par KAJan 26, 2020
Very good course for ethics and introduction to the basic concept of Machine Learning.
par JRSep 1, 2019
Solid introduction to the Machine Learning Lifecycle and other intro topics in ML
par SZMar 26, 2020
t refreshed my previous concepts of machine learning with better understanding
par GASep 7, 2019
Very good. Interesting format and method to introduce the basic concepts.
par GGSep 6, 2019
Great intro to ML. Anna Koop has great knack for teaching ...
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.
After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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