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中级

完成时间大约为9 小时

建议:12 hours/week...

英语(English)

字幕:英语(English)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为9 小时

建议:12 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 3 小时

Introduction to Machine Learning Applications

12 个视频 (总计 44 分钟), 6 个阅读材料, 2 个测验
12 个视频
Instructor Introduction1分钟
Introduction to Course 12分钟
What is Artificial Intelligence and Machine Learning?5分钟
What about Data Science?3分钟
The Machine Learning Process4分钟
The Three Kinds of Machine Learning3分钟
Classification: What is it and how does it work?3分钟
Regression: Fitting lines and predicting numbers3分钟
Unsupervised Learning4分钟
Reinforcement Learning6分钟
Weekly Summary1分钟
6 个阅读材料
What about Deep Learning? (supplemental)10分钟
Fooling Neural Networks (supplemental)10分钟
How to Curate A Ground Truth For Your Business Dataset (Required)10分钟
Learning From Multiple Annotators: A Survey (supplemental)10分钟
Inferring the Ground Truth Through Crowdsourcing (supplemental)10分钟
Semi Supervised Learning (required)10分钟
2 个练习
Concepts and Definitions20分钟
Identifying Machine Learning Techniques10分钟
2
完成时间为 1 小时

Machine Learning in the Real World

8 个视频 (总计 34 分钟), 4 个阅读材料, 1 个测验
8 个视频
Features and transformations of raw data6分钟
Farmer Betty and Her Precision Agriculture Plans3分钟
What to consider when using your QuAM2分钟
Broad Examples Narrowed Down4分钟
Identify Business Evaluation4分钟
Everything is a Proxy4分钟
Weekly Summary2分钟
4 个阅读材料
A Brief Introduction into Precision Agriculture10分钟
Farmer Betty Tried Unsupervised Learning (required)10分钟
Data is Central to Your ML Problem (required)10分钟
Martin Zinkevich's Rules for ML (supplemental)10分钟
1 个练习
Machine Learning in the Real World Review
3
完成时间为 1 小时

Learning Data

9 个视频 (总计 34 分钟), 2 个阅读材料, 1 个测验
9 个视频
How Much Data Do I Need?4分钟
Ethical Issues4分钟
Bias in Data Sources3分钟
Noise and Sources of Randomness5分钟
Image Classification Example3分钟
Data Cleaning: Everybody's favourite task4分钟
Why you need to set up a Data Pipeline4分钟
Weekly Summary1分钟
2 个阅读材料
Data Protection Laws (required)10分钟
Government readings on data privacy (supplemental)10分钟
1 个练习
Understanding Data for ML
4
完成时间为 1 小时

Machine Learning Projects

7 个视频 (总计 35 分钟), 2 个阅读材料, 1 个测验
7 个视频
MLPL as experienced by Farmer Betty3分钟
Exploring the process of problem definition7分钟
Assessing your QuAM for use in your Business6分钟
Technically Assessing the Strength of your QuAM6分钟
Different Kinds of Wrong4分钟
Weekly Summary2分钟
2 个阅读材料
Machine Learning Process Lifecycle Explained10分钟
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10分钟
1 个练习
Understanding Machine Learning Projects
4.7
30 条评论Chevron Right

来自Introduction to Applied Machine Learning的热门评论

创建者 MMOct 29th 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.

创建者 KSOct 14th 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.

讲师

Avatar

Anna Koop

Senior Scientific Advisor
Alberta Machine Intelligence Institute, University of Alberta

关于 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 research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

关于 Machine Learning: Algorithms in the Real World 专项课程

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....
Machine Learning: Algorithms in the Real World

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