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Was ist maschinelles Lernen und welche Probleme lassen sich damit lösen? Für Google geht es beim maschinellen Lernen (ML) mehr um Logik als nur um Daten. In diesem Kurs erfahren Sie, warum dieser Ansatz beim Erstellen einer Pipeline aus ML-Modellen nützlich...
Read more
Was ist maschinelles Lernen und welche Probleme lassen sich damit lösen? Für Google geht es beim maschinellen Lernen (ML) mehr um Logik als nur um Daten. In diesem Kurs erfahren Sie, warum dieser Ansatz beim Erstellen einer Pipeline aus ML-Modellen nützlich...
Read more
Was ist maschinelles Lernen und welche Probleme lassen sich damit lösen? Für Google geht es beim maschinellen Lernen (ML) mehr um Logik als nur um Daten. In diesem Kurs erfahren Sie, warum dieser Ansatz beim Erstellen einer Pipeline aus ML-Modellen nützlich ist. Außerdem erläutern wir die fünf Phasen zur Umsetzung eines für ML geeigneten Anwendungsfalls und warum keine dieser Phasen übersprungen werden darf. Zum Abschluss besprechen wir die Verzerrung, die durch ML entstehen kann, und erklären, wie man sie erkennt.
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Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the practical application of machine learning (ML) in industry
Covers a comprehensive five-step process for successful ML implementation
Explores the potential bias in ML and provides strategies to mitigate it
May require prior knowledge or experience with machine learning concepts
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Save How Google does Machine Learning auf Deutsch to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in How Google does Machine Learning auf Deutsch with these
activities:
Review data analysis techniques
Show steps
Review the fundamentals of data analysis, including data collection, cleaning, and analysis techniques, to prepare for this course's emphasis on machine learning models.
Browse courses on
Data Analysis
Show steps
-
Review data collection methods and tools
-
Practice data cleaning and preprocessing techniques
-
Explore data analysis techniques, such as descriptive statistics and visualization
Review machine learning concepts
Show steps
Refresh your understanding of machine learning concepts, including supervised and unsupervised learning, model evaluation, and bias mitigation, to enhance your comprehension of the course material.
Browse courses on
Machine Learning
Show steps
-
Recall the different types of machine learning algorithms
-
Review model evaluation metrics and techniques
-
Discuss the importance of bias mitigation in machine learning
Join a study group or online forum for machine learning
Show steps
Connect with other learners and experts in the field by joining a study group or online forum, fostering collaboration, knowledge sharing, and support.
Browse courses on
Machine Learning
Show steps
-
Search for study groups or online forums dedicated to machine learning
-
Join the group or forum and introduce yourself
-
Actively participate in discussions and ask questions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve machine learning practice problems
Show steps
Engage in hands-on practice by solving machine learning problems, reinforcing your understanding of model building, training, and evaluation.
Browse courses on
Machine Learning
Show steps
-
Work through practice problems involving supervised learning algorithms
-
Attempt practice problems focused on unsupervised learning algorithms
-
Analyze and interpret the results of your machine learning models
Follow tutorials on specific machine learning algorithms
Show steps
Enhance your understanding of specific machine learning algorithms by following guided tutorials, deepening your knowledge of their implementation and applications.
Browse courses on
Machine Learning
Show steps
-
Search for tutorials on specific machine learning algorithms
-
Follow tutorials and implement the algorithms
-
Apply the algorithms to real-world datasets
Build a machine learning model for a specific problem
Show steps
Apply your machine learning skills by building a model to solve a specific problem, demonstrating your ability to apply machine learning techniques to real-world scenarios.
Browse courses on
Machine Learning
Show steps
-
Define a specific problem that can be solved using machine learning
-
Gather and prepare a dataset relevant to the problem
-
Select and train a machine learning algorithm
-
Evaluate the performance of the model
-
Deploy the model and monitor its performance
Contribute to open-source machine learning projects
Show steps
Engage with the machine learning community by contributing to open-source projects, enhancing your understanding of machine learning practices and expanding your network.
Browse courses on
Machine Learning
Show steps
-
Identify open-source machine learning projects
-
Review the documentation and codebase
-
Contribute to the project by fixing bugs or adding new features
Mentor junior learners in machine learning
Show steps
Reinforce your understanding of machine learning concepts by mentoring junior learners, sharing your knowledge and expertise while fostering their growth in the field.
Browse courses on
Machine Learning
Show steps
-
Identify opportunities to mentor junior learners, such as through online platforms or local organizations
-
Share your knowledge and experience with the learners
-
Provide guidance and support to the learners as they develop their skills
Review data analysis techniques
Show steps
Review the fundamentals of data analysis, including data collection, cleaning, and analysis techniques, to prepare for this course's emphasis on machine learning models.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Review data collection methods and tools
- Practice data cleaning and preprocessing techniques
- Explore data analysis techniques, such as descriptive statistics and visualization
Review machine learning concepts
Show steps
Refresh your understanding of machine learning concepts, including supervised and unsupervised learning, model evaluation, and bias mitigation, to enhance your comprehension of the course material.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Recall the different types of machine learning algorithms
- Review model evaluation metrics and techniques
- Discuss the importance of bias mitigation in machine learning
Join a study group or online forum for machine learning
Show steps
Connect with other learners and experts in the field by joining a study group or online forum, fostering collaboration, knowledge sharing, and support.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for study groups or online forums dedicated to machine learning
- Join the group or forum and introduce yourself
- Actively participate in discussions and ask questions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve machine learning practice problems
Show steps
Engage in hands-on practice by solving machine learning problems, reinforcing your understanding of model building, training, and evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Work through practice problems involving supervised learning algorithms
- Attempt practice problems focused on unsupervised learning algorithms
- Analyze and interpret the results of your machine learning models
Follow tutorials on specific machine learning algorithms
Show steps
Enhance your understanding of specific machine learning algorithms by following guided tutorials, deepening your knowledge of their implementation and applications.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for tutorials on specific machine learning algorithms
- Follow tutorials and implement the algorithms
- Apply the algorithms to real-world datasets
Build a machine learning model for a specific problem
Show steps
Apply your machine learning skills by building a model to solve a specific problem, demonstrating your ability to apply machine learning techniques to real-world scenarios.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define a specific problem that can be solved using machine learning
- Gather and prepare a dataset relevant to the problem
- Select and train a machine learning algorithm
- Evaluate the performance of the model
- Deploy the model and monitor its performance
Contribute to open-source machine learning projects
Show steps
Engage with the machine learning community by contributing to open-source projects, enhancing your understanding of machine learning practices and expanding your network.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open-source machine learning projects
- Review the documentation and codebase
- Contribute to the project by fixing bugs or adding new features
Mentor junior learners in machine learning
Show steps
Reinforce your understanding of machine learning concepts by mentoring junior learners, sharing your knowledge and expertise while fostering their growth in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners, such as through online platforms or local organizations
- Share your knowledge and experience with the learners
- Provide guidance and support to the learners as they develop their skills
Career center
Learners who complete How Google does Machine Learning auf Deutsch will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work closely with data scientists to develop and implement algorithms that can solve complex problems. This course can help you develop the skills you need to become a Machine Learning Engineer by providing you with a foundation in machine learning concepts and techniques.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills you need to become a Data Scientist by providing you with a foundation in machine learning concepts and techniques.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They work closely with business stakeholders to develop insights that can help drive decision-making. This course can help you develop the skills you need to become a Data Analyst by providing you with a foundation in machine learning concepts and techniques.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work closely with other engineers to create software that meets the needs of users. This course can help you develop the skills you need to become a Software Engineer by providing you with a foundation in machine learning concepts and techniques.
Product Manager
Product Manager
Product Managers are responsible for developing and launching new products. They work closely with engineers, designers, and marketers to bring new products to market. This course can help you develop the skills you need to become a Product Manager by providing you with a foundation in machine learning concepts and techniques.
Business Analyst
Business Analyst
Business Analysts help organizations improve their business processes. They work closely with stakeholders to identify areas for improvement and develop solutions. This course can help you develop the skills you need to become a Business Analyst by providing you with a foundation in machine learning concepts and techniques.
Marketing Analyst
Marketing Analyst
Marketing Analysts help organizations improve their marketing campaigns. They work closely with marketers to identify areas for improvement and develop solutions. This course can help you develop the skills you need to become a Marketing Analyst by providing you with a foundation in machine learning concepts and techniques.
Financial Analyst
Financial Analyst
Financial Analysts help organizations make investment decisions. They work closely with investors to identify areas for investment and develop strategies. This course can help you develop the skills you need to become a Financial Analyst by providing you with a foundation in machine learning concepts and techniques.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts help organizations improve their operations. They work closely with managers to identify areas for improvement and develop solutions. This course can help you develop the skills you need to become an Operations Research Analyst by providing you with a foundation in machine learning concepts and techniques.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data. They work closely with investors to identify areas for investment and develop strategies. This course can help you develop the skills you need to become a Quantitative Analyst by providing you with a foundation in machine learning concepts and techniques.
Risk Analyst
Risk Analyst
Risk Analysts help organizations identify and manage risks. They work closely with managers to develop strategies to mitigate risks. This course can help you develop the skills you need to become a Risk Analyst by providing you with a foundation in machine learning concepts and techniques.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They work closely with insurance companies to develop products and strategies. This course can help you develop the skills you need to become an Actuary by providing you with a foundation in machine learning concepts and techniques.
Statistician
Statistician
Statisticians use mathematical and statistical models to analyze data. They work closely with researchers to design studies and analyze results. This course can help you develop the skills you need to become a Statistician by providing you with a foundation in machine learning concepts and techniques.
Economist
Economist
Economists use mathematical and statistical models to analyze economic data. They work closely with policymakers to develop economic policies. This course can help you develop the skills you need to become an Economist by providing you with a foundation in machine learning concepts and techniques.
Consultant
Consultant
Consultants help organizations improve their performance. They work closely with clients to identify areas for improvement and develop solutions. This course can help you develop the skills you need to become a Consultant by providing you with a foundation in machine learning concepts and techniques.
For more career information including salaries, visit:
OpenCourser.com/course/qulzby/how
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
How Google does Machine Learning auf Deutsch.
Dieses Buch bietet einen umfassenden Überblick über künstliche Intelligenz und deckt die wichtigsten Konzepte, Algorithmen und Anwendungen ab. Es ist eine wertvolle Ressource für alle, die mehr über künstliche Intelligenz erfahren möchten, unabhängig von ihrem Erfahrungsstand.
Provides a comprehensive overview of statistical learning, covering the key concepts, algorithms, and applications. It valuable resource for anyone who wants to learn more about statistical learning, regardless of their level of experience.
Provides a comprehensive overview of pattern recognition and machine learning, covering the key concepts, algorithms, and applications. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning, regardless of their level of experience.
Save
Dieses Buch bietet einen umfassenden Überblick über maschinelles Lernen und deckt die wichtigsten Konzepte, Algorithmen und Anwendungen ab. Es ist eine wertvolle Ressource für alle, die mehr über maschinelles Lernen erfahren möchten, unabhängig von ihrem Erfahrungsstand.
Save
Comprehensive guide to deep learning, covering the latest research and techniques. It valuable resource for anyone who wants to learn more about deep learning, regardless of their level of experience.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to get started with machine learning.
Provides a practical introduction to machine learning using R. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to get started with machine learning using R.
Provides a practical introduction to machine learning for people with a non-technical background. It covers a wide range of topics, from data collection to model deployment. It valuable resource for anyone who wants to learn more about machine learning without getting bogged down in the technical details.
Provides a practical introduction to machine learning for people with a non-technical background. It covers a wide range of topics, from data collection to model deployment. It valuable resource for anyone who wants to learn more about machine learning without getting bogged down in the technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qulzby/how
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Time
19 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
This course belongs to a
collection
called
Machine Learning with TensorFlow on Google Cloud Platform auf Deutsch. It's comprised of five courses:
- Launching into Machine Learning auf Deutsch
- Feature Engineering auf Deutsch
- Art and Science of Machine Learning auf Deutsch
- How Google does Machine Learning auf Deutsch (viewing)
- Intro to TensorFlow auf Deutsch
Good to know
Teaches the practical application of machine learning (ML) in industry
Covers a comprehensive five-step process for successful ML implementation
Explores the potential bias in ML and provides strategies to mitigate it
May require prior knowledge or experience with machine learning concepts
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