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.
Этот курс входит в специализацию ''Специализация Machine Learning: Algorithms in the Real World'
Introduction to Applied Machine Learning
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Промежуточный уровень
Прибл. 9 часа на выполнение
Предполагаемая нагрузка: 13 hours/week
Английский
Субтитры: Английский
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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
Программа курса: что вы изучите
3 ч. на завершение
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 ч. на завершение
12 видео ((всего 44 мин.)), 6 материалов для самостоятельного изучения, 2 тестов
12 видео
Instructor Introduction1мин
Introduction to Course 12мин
What is Artificial Intelligence and Machine Learning?5мин
What about Data Science?3мин
The Machine Learning Process4мин
The Three Kinds of Machine Learning3мин
Classification: What is it and how does it work?3мин
Regression: Fitting lines and predicting numbers3мин
Unsupervised Learning4мин
Reinforcement Learning6мин
Weekly Summary1мин
6 материала для самостоятельного изучения
What about Deep Learning? (supplemental)10мин
Fooling Neural Networks (supplemental)10мин
How to Curate A Ground Truth For Your Business Dataset (Required)10мин
Learning From Multiple Annotators: A Survey (supplemental)10мин
Inferring the Ground Truth Through Crowdsourcing (supplemental)10мин
Semi Supervised Learning (required)10мин
2 практического упражнения
Concepts and Definitions20мин
Identifying Machine Learning Techniques10мин
1 ч. на завершение
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 ч. на завершение
8 видео ((всего 34 мин.)), 4 материалов для самостоятельного изучения, 1 тест
8 видео
Features and transformations of raw data6мин
Farmer Betty and Her Precision Agriculture Plans3мин
What to consider when using your QuAM2мин
Broad Examples Narrowed Down4мин
Identify Business Evaluation4мин
Everything is a Proxy4мин
Weekly Summary2мин
4 материала для самостоятельного изучения
A Brief Introduction into Precision Agriculture10мин
Farmer Betty Tried Unsupervised Learning (required)10мин
Data is Central to Your ML Problem (required)10мин
Martin Zinkevich's Rules for ML (supplemental)10мин
1 практического упражнения
Machine Learning in the Real World Review
1 ч. на завершение
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 ч. на завершение
9 видео ((всего 34 мин.)), 2 материалов для самостоятельного изучения, 1 тест
9 видео
How Much Data Do I Need?4мин
Ethical Issues4мин
Bias in Data Sources3мин
Noise and Sources of Randomness5мин
Image Classification Example3мин
Data Cleaning: Everybody's favourite task4мин
Why you need to set up a Data Pipeline4мин
Weekly Summary1мин
2 материала для самостоятельного изучения
Data Protection Laws (required)10мин
Government readings on data privacy (supplemental)10мин
1 практического упражнения
Understanding Data for ML
1 ч. на завершение
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 ч. на завершение
7 видео ((всего 35 мин.)), 2 материалов для самостоятельного изучения, 1 тест
7 видео
MLPL Overview5мин
MLPL as experienced by Farmer Betty3мин
Exploring the process of problem definition7мин
Assessing your QuAM for use in your Business6мин
Technically Assessing the Strength of your QuAM6мин
Different Kinds of Wrong4мин
Weekly Summary2мин
2 материала для самостоятельного изучения
Machine Learning Process Lifecycle Explained10мин
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10мин
1 практического упражнения
Understanding Machine Learning Projects
Рецензии
4.6
Лучшие отзывы о курсе INTRODUCTION TO APPLIED MACHINE LEARNING
от партнера 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.
от партнера 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.
от партнера 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.
от партнера 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!
от партнера 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...
от партнера 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.
от партнера 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
от партнера FMNov 11, 2019
Great Introductory course, it was delivered very good. I picked up a lot of different definitions which were simplified. Very Good!
от партнера 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.
от партнера MTAug 30, 2019
Anna Koop has a very engaging onscreen personality that makes it easy to follow her explanations of machine learning.
от партнера AHMar 29, 2020
It is a good start. For sure, this course is recommended for everyone who is not in the computer science world.
от партнера ECSep 21, 2019
Very informative, highly recommended to anyone with an interest in starting any ML project in the future.
от партнера PLSep 7, 2019
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от партнера EGJan 8, 2020
The whole specialization is extremely useful for people starting in ML. Highly recommended!
от партнера KAJan 26, 2020
Very good course for ethics and introduction to the basic concept of Machine Learning.
от партнера JRSep 1, 2019
Solid introduction to the Machine Learning Lifecycle and other intro topics in ML
от партнера SZMar 26, 2020
t refreshed my previous concepts of machine learning with better understanding
от партнера GASep 7, 2019
Very good. Interesting format and method to introduce the basic concepts.
от партнера GGSep 6, 2019
Great intro to ML. Anna Koop has great knack for teaching ...
Специализация Machine Learning: Algorithms in the Real World: общие сведения
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.
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