Über diesen Kurs

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Zertifikat zur Vorlage
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100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
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Stufe „Mittel“
Ca. 6 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 6 Stunden zum Abschließen
Englisch
Untertitel: Englisch

von

Alberta Machine Intelligence Institute-Logo

Alberta Machine Intelligence Institute

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1

Woche 1

3 Stunden zum Abschließen

Introduction to Machine Learning Applications

3 Stunden zum Abschließen
12 Videos (Gesamt 44 min), 6 Lektüren, 2 Quiz
12 Videos
Instructor Introduction1m
Introduction to Course 12m
What is Artificial Intelligence and Machine Learning?5m
What about Data Science?3m
The Machine Learning Process4m
The Three Kinds of Machine Learning3m
Classification: What is it and how does it work?3m
Regression: Fitting lines and predicting numbers3m
Unsupervised Learning4m
Reinforcement Learning6m
Weekly Summary1m
6 Lektüren
What about Deep Learning? (supplemental)10m
Fooling Neural Networks (supplemental)10m
How to Curate A Ground Truth For Your Business Dataset (Required)10m
Learning From Multiple Annotators: A Survey (supplemental)10m
Inferring the Ground Truth Through Crowdsourcing (supplemental)10m
Semi Supervised Learning (required)10m
2 praktische Übungen
Concepts and Definitions20m
Identifying Machine Learning Techniques10m
Woche
2

Woche 2

1 Stunde zum Abschließen

Machine Learning in the Real World

1 Stunde zum Abschließen
8 Videos (Gesamt 34 min), 4 Lektüren, 1 Quiz
8 Videos
Features and transformations of raw data6m
Farmer Betty and Her Precision Agriculture Plans3m
What to consider when using your QuAM2m
Broad Examples Narrowed Down4m
Identify Business Evaluation4m
Everything is a Proxy4m
Weekly Summary2m
4 Lektüren
A Brief Introduction into Precision Agriculture10m
Farmer Betty Tried Unsupervised Learning (required)10m
Data is Central to Your ML Problem (required)10m
Martin Zinkevich's Rules for ML (supplemental)10m
1 praktische Übung
Machine Learning in the Real World Review
Woche
3

Woche 3

1 Stunde zum Abschließen

Learning Data

1 Stunde zum Abschließen
9 Videos (Gesamt 34 min), 2 Lektüren, 1 Quiz
9 Videos
How Much Data Do I Need?4m
Ethical Issues4m
Bias in Data Sources3m
Noise and Sources of Randomness5m
Image Classification Example3m
Data Cleaning: Everybody's favourite task4m
Why you need to set up a Data Pipeline4m
Weekly Summary1m
2 Lektüren
Data Protection Laws (required)10m
Government readings on data privacy (supplemental)10m
1 praktische Übung
Understanding Data for ML
Woche
4

Woche 4

1 Stunde zum Abschließen

Machine Learning Projects

1 Stunde zum Abschließen
7 Videos (Gesamt 35 min), 2 Lektüren, 1 Quiz
7 Videos
MLPL as experienced by Farmer Betty3m
Exploring the process of problem definition7m
Assessing your QuAM for use in your Business6m
Technically Assessing the Strength of your QuAM6m
Different Kinds of Wrong4m
Weekly Summary2m
2 Lektüren
Machine Learning Process Lifecycle Explained10m
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10m
1 praktische Übung
Understanding Machine Learning Projects

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Über den Spezialisierung 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....
Machine Learning: Algorithms in the Real World

Häufig gestellte Fragen

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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