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中級

完成時間大約為9 小時

建議:12 hours/week...

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100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為9 小時

建議:12 hours/week...

英語(English)

字幕:英語(English)

學習Course的學生是

  • Data Scientists
  • Machine Learning Engineers
  • Chief Technology Officers (CTOs)
  • Data Engineers
  • Vice Presidents

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 3 小時

Introduction to Machine Learning Applications

12 個視頻 (總計 44 分鐘), 6 個閱讀材料, 2 個測驗
12 個視頻
Instructor Introduction1分鐘
Introduction to Course 12分鐘
What is Artificial Intelligence and Machine Learning?5分鐘
What about Data Science?3分鐘
The Machine Learning Process4分鐘
The Three Kinds of Machine Learning3分鐘
Classification: What is it and how does it work?3分鐘
Regression: Fitting lines and predicting numbers3分鐘
Unsupervised Learning4分鐘
Reinforcement Learning6分鐘
Weekly Summary1分鐘
6 個閱讀材料
What about Deep Learning? (supplemental)10分鐘
Fooling Neural Networks (supplemental)10分鐘
How to Curate A Ground Truth For Your Business Dataset (Required)10分鐘
Learning From Multiple Annotators: A Survey (supplemental)10分鐘
Inferring the Ground Truth Through Crowdsourcing (supplemental)10分鐘
Semi Supervised Learning (required)10分鐘
2 個練習
Concepts and Definitions20分鐘
Identifying Machine Learning Techniques10分鐘
2
完成時間為 1 小時

Machine Learning in the Real World

8 個視頻 (總計 34 分鐘), 4 個閱讀材料, 1 個測驗
8 個視頻
Features and transformations of raw data6分鐘
Farmer Betty and Her Precision Agriculture Plans3分鐘
What to consider when using your QuAM2分鐘
Broad Examples Narrowed Down4分鐘
Identify Business Evaluation4分鐘
Everything is a Proxy4分鐘
Weekly Summary2分鐘
4 個閱讀材料
A Brief Introduction into Precision Agriculture10分鐘
Farmer Betty Tried Unsupervised Learning (required)10分鐘
Data is Central to Your ML Problem (required)10分鐘
Martin Zinkevich's Rules for ML (supplemental)10分鐘
1 個練習
Machine Learning in the Real World Review
3
完成時間為 1 小時

Learning Data

9 個視頻 (總計 34 分鐘), 2 個閱讀材料, 1 個測驗
9 個視頻
How Much Data Do I Need?4分鐘
Ethical Issues4分鐘
Bias in Data Sources3分鐘
Noise and Sources of Randomness5分鐘
Image Classification Example3分鐘
Data Cleaning: Everybody's favourite task4分鐘
Why you need to set up a Data Pipeline4分鐘
Weekly Summary1分鐘
2 個閱讀材料
Data Protection Laws (required)10分鐘
Government readings on data privacy (supplemental)10分鐘
1 個練習
Understanding Data for ML
4
完成時間為 1 小時

Machine Learning Projects

7 個視頻 (總計 35 分鐘), 2 個閱讀材料, 1 個測驗
7 個視頻
MLPL as experienced by Farmer Betty3分鐘
Exploring the process of problem definition7分鐘
Assessing your QuAM for use in your Business6分鐘
Technically Assessing the Strength of your QuAM6分鐘
Different Kinds of Wrong4分鐘
Weekly Summary2分鐘
2 個閱讀材料
Machine Learning Process Lifecycle Explained10分鐘
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10分鐘
1 個練習
Understanding Machine Learning Projects
4.7
27 個審閱Chevron Right

來自Introduction to Applied Machine Learning的熱門評論

創建者 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.

創建者 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.

講師

Avatar

Anna Koop

Senior Scientific Advisor
Alberta Machine Intelligence Institute, University of Alberta

關於 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....

關於 Machine Learning: Algorithms in the Real World 專項課程

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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