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Google Cloud Training
Unternehmen, die maschinelles Lernen (ML) für Datenpipelines einsetzen, gewinnen leichter Informationen aus ihren Daten. Im Kurs lernen Sie, wie maschinelles Lernen mit verschiedenen Anpassungsstufen in Datenpipelines auf der Google Cloud Platform integriert...
Read more
Unternehmen, die maschinelles Lernen (ML) für Datenpipelines einsetzen, gewinnen leichter Informationen aus ihren Daten. Im Kurs lernen Sie, wie maschinelles Lernen mit verschiedenen Anpassungsstufen in Datenpipelines auf der Google Cloud Platform integriert...
Read more
Unternehmen, die maschinelles Lernen (ML) für Datenpipelines einsetzen, gewinnen leichter Informationen aus ihren Daten. Im Kurs lernen Sie, wie maschinelles Lernen mit verschiedenen Anpassungsstufen in Datenpipelines auf der Google Cloud Platform integriert werden kann. Für Lösungen mit wenig oder ohne Anpassung wird AutoML vorgestellt. Detailliertere ML-Funktionen lernen Sie in den Diensten AI Platform Notebooks und BigQuery ML kennen. Außerdem machen Sie sich mit der Anwendung von ML-Lösungen mit Kubeflow vertraut. Wie sich ML-Modelle in der Google Cloud Platform errichten lassen, üben Sie in den praxisorientierten Labs in Qwiklabs.
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possible dealbreakers
Teaches a combination of theory and practice for data engineers to use during ML integration
Introduces BigQuery ML and AI Platform Notebooks for more advanced ML capabilities
Integrates Kubeflow for ML solutions application
Offers opportunities to practice in labs
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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 Smart Analytics, Machine Learning, and AI on GCP auf Deutsch with these
activities:
Organize your notes and resources
Show steps
Organizing your notes and resources will help you stay on track during the course, making it easier to locate and review important information when needed.
Show steps
-
Create a system for organizing your lecture notes, assignments, and other course materials.
-
Use a digital or physical notebook to keep track of your notes.
-
Regularly review and summarize your notes to reinforce your understanding.
Review reading comprehension basics
Show steps
Reviewing reading comprehension basics can help you strengthen your foundational skills in understanding written texts, which can enhance your learning in this course.
Browse courses on
Reading Comprehension
Show steps
-
Revisit reading strategies such as previewing, annotating, and summarizing.
-
Practice active reading by engaging with the text and highlighting or underlining important points.
-
Take practice quizzes or exercises to assess your comprehension and identify areas for improvement.
Review basic statistics and probability concepts
Show steps
Refreshing your basic statistics and probability concepts will provide a solid foundation for understanding data analysis and modeling techniques covered in this course.
Browse courses on
Statistics
Show steps
-
Review key statistical concepts such as mean, median, mode, and standard deviation.
-
Brush up on probability theory, including concepts like conditional probability and Bayes' theorem.
-
Practice solving basic statistics and probability problems.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve data science coding challenges
Show steps
Regularly solving data science coding challenges will enhance your problem-solving skills, improve your coding efficiency, and deepen your understanding of data science algorithms.
Browse courses on
Coding Challenges
Show steps
-
Find online platforms or resources that provide data science coding challenges.
-
Select challenges that are aligned with your skill level and gradually increase the difficulty.
-
Break down the problem into smaller steps and design a logical solution.
-
Implement your solution in a preferred programming language and test its accuracy.
-
Review the solutions of others and learn from different approaches.
Develop a data science project proposal
Show steps
Creating a data science project proposal will allow you to apply your learning from this course to a practical problem, deepening your understanding and solidifying your skills.
Browse courses on
Data Science Project
Show steps
-
Identify a problem or challenge that you can address using data science techniques.
-
Define the scope and objectives of your project.
-
Outline the data you will need, where you will obtain it, and how you will prepare it.
-
Describe the methods and algorithms you will use to analyze the data.
-
Plan how you will evaluate the results of your project.
Contribute to an open-source data science project
Show steps
Contributing to an open-source data science project will provide you with hands-on experience in a collaborative environment, further enhancing your skills and knowledge.
Browse courses on
Data Science Project
Show steps
-
Identify open-source data science projects that align with your interests and skills.
-
Review the project documentation and familiarize yourself with the codebase.
-
Identify a specific area where you can contribute, such as data cleaning, feature engineering, or model development.
-
Submit a pull request with your contributions and provide clear documentation.
-
Engage with the project community and seek feedback on your work.
Organize your notes and resources
Show steps
Organizing your notes and resources will help you stay on track during the course, making it easier to locate and review important information when needed.
Show steps
Show steps
Show steps
- Create a system for organizing your lecture notes, assignments, and other course materials.
- Use a digital or physical notebook to keep track of your notes.
- Regularly review and summarize your notes to reinforce your understanding.
Review reading comprehension basics
Show steps
Reviewing reading comprehension basics can help you strengthen your foundational skills in understanding written texts, which can enhance your learning in this course.
Browse courses on
Reading Comprehension
Show steps
Show steps
Show steps
- Revisit reading strategies such as previewing, annotating, and summarizing.
- Practice active reading by engaging with the text and highlighting or underlining important points.
- Take practice quizzes or exercises to assess your comprehension and identify areas for improvement.
Review basic statistics and probability concepts
Show steps
Refreshing your basic statistics and probability concepts will provide a solid foundation for understanding data analysis and modeling techniques covered in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review key statistical concepts such as mean, median, mode, and standard deviation.
- Brush up on probability theory, including concepts like conditional probability and Bayes' theorem.
- Practice solving basic statistics and probability problems.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve data science coding challenges
Show steps
Regularly solving data science coding challenges will enhance your problem-solving skills, improve your coding efficiency, and deepen your understanding of data science algorithms.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Find online platforms or resources that provide data science coding challenges.
- Select challenges that are aligned with your skill level and gradually increase the difficulty.
- Break down the problem into smaller steps and design a logical solution.
- Implement your solution in a preferred programming language and test its accuracy.
- Review the solutions of others and learn from different approaches.
Develop a data science project proposal
Show steps
Creating a data science project proposal will allow you to apply your learning from this course to a practical problem, deepening your understanding and solidifying your skills.
Browse courses on
Data Science Project
Show steps
Show steps
Show steps
- Identify a problem or challenge that you can address using data science techniques.
- Define the scope and objectives of your project.
- Outline the data you will need, where you will obtain it, and how you will prepare it.
- Describe the methods and algorithms you will use to analyze the data.
- Plan how you will evaluate the results of your project.
Contribute to an open-source data science project
Show steps
Contributing to an open-source data science project will provide you with hands-on experience in a collaborative environment, further enhancing your skills and knowledge.
Browse courses on
Data Science Project
Show steps
Show steps
Show steps
- Identify open-source data science projects that align with your interests and skills.
- Review the project documentation and familiarize yourself with the codebase.
- Identify a specific area where you can contribute, such as data cleaning, feature engineering, or model development.
- Submit a pull request with your contributions and provide clear documentation.
- Engage with the project community and seek feedback on your work.
Career center
Learners who complete Smart Analytics, Machine Learning, and AI on GCP auf Deutsch will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning and AI to extract insights from data. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of machine learning and AI. The course covers a range of topics, including data preprocessing, model training, and model evaluation. This knowledge is essential for Data Scientists who want to succeed in their field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work closely with Data Scientists to ensure that models are accurate and meet the needs of the business. The course Smart Analytics, Machine Learning, and AI on GCP provides a comprehensive overview of the machine learning lifecycle. The course covers a range of topics, including data preprocessing, model training, and model evaluation. This knowledge is essential for Machine Learning Engineers who want to succeed in their field.
AI Engineer
AI Engineer
AI Engineers design, develop, and deploy AI systems. They work on a variety of projects, such as self-driving cars, natural language processing, and computer vision. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of AI. The course covers a range of topics, including machine learning, deep learning, and natural language processing. This knowledge is essential for AI Engineers who want to succeed in their field.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of data analysis. The course covers a range of topics, including data preprocessing, data visualization, and statistical analysis. This knowledge is essential for Data Analysts who want to succeed in their field.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to improve business decision-making. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of business intelligence. The course covers a range of topics, including data mining, data visualization, and statistical analysis. This knowledge is essential for Business Intelligence Analysts who want to succeed in their field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of software engineering. The course covers a range of topics, including software design, software development, and software testing.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of quantitative analysis. The course covers a range of topics, including financial modeling, statistical analysis, and machine learning.
Data Architect
Data Architect
Data Architects design and manage data systems. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of data architecture. The course covers a range of topics, including data modeling, data storage, and data security.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of database administration. The course covers a range of topics, including database design, database management, and database security.
Systems Analyst
Systems Analyst
Systems Analysts design and implement computer systems. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of systems analysis. The course covers a range of topics, including systems design, systems development, and systems testing.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of operations research. The course covers a range of topics, including linear programming, integer programming, and optimization.
Decision Scientist
Decision Scientist
Decision Scientists use data and analytics to make better decisions. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of decision science. The course covers a range of topics, including decision making, data analysis, and statistical modeling.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of risk analysis. The course covers a range of topics, including risk identification, risk assessment, and risk management.
Compliance Analyst
Compliance Analyst
Compliance Analysts ensure that organizations comply with laws and regulations. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP provides a solid foundation in the theory and practice of compliance analysis. The course covers a range of topics, including compliance risk, compliance auditing, and compliance reporting.
Auditor
Auditor
Auditors examine financial records and documents to ensure accuracy and compliance. They work in a variety of industries, such as finance, healthcare, and retail. The course Smart Analytics, Machine Learning, and AI on GCP may be useful for Auditors, as it provides a solid foundation in the theory and practice of data analysis and auditing.
For more career information including salaries, visit:
OpenCourser.com/course/f6romc/smart
Reading list
We've selected 21 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
Smart Analytics, Machine Learning, and AI on GCP auf Deutsch.
Provides a practical guide to machine learning. It covers a range of topics, including data preparation, feature engineering, model selection, and evaluation. It valuable resource for anyone looking to learn more about machine learning and how to use it in practice.
Provides a practical guide to machine learning with Python. It covers a range of topics, including data preparation, feature engineering, model selection, and evaluation. It valuable resource for anyone looking to learn more about machine learning and how to use it in practice.
Provides a comprehensive overview of machine learning, covering the basics of supervised and unsupervised learning, as well as more advanced topics such as deep learning and neural networks.
Provides a comprehensive introduction to machine learning with Python. It covers a range of topics, including data preparation, feature engineering, model selection, and evaluation. It valuable resource for anyone looking to learn more about machine learning and how to use it in practice.
Provides a practical guide to machine learning. It covers a range of topics, including data preparation, feature engineering, model selection, and evaluation. It valuable resource for anyone looking to learn more about machine learning and how to use it in practice.
Provides a comprehensive overview of deep learning. It covers a range of topics, including the different types of deep learning, how to use deep learning, and the benefits of using deep learning. It valuable resource for anyone looking to learn more about deep learning and how to use it in practice.
Provides a practical introduction to deep learning using the fastai library. It covers a wide range of deep learning topics, from convolutional neural networks to recurrent neural networks.
Provides a practical introduction to machine learning using R. It covers a wide range of machine learning topics, from data preprocessing to model evaluation.
Provides a practical introduction to deep learning using the Keras library. It covers a wide range of deep learning topics, from convolutional neural networks to recurrent neural networks.
Provides a practical introduction to machine learning using Python. It covers a wide range of machine learning topics, from data preprocessing to model evaluation, and how to use machine learning to solve real-world problems.
Provides a visual introduction to deep learning. It uses clear and concise illustrations to explain the concepts and algorithms of deep learning.
Provides a practical introduction to machine learning for programmers and hackers. It covers a wide range of machine learning topics, from data preprocessing to model evaluation, and how to use machine learning to solve real-world problems.
Provides a practical introduction to machine learning using R. It covers a wide range of machine learning topics, from data preprocessing to model evaluation.
Provides a comprehensive overview of data science, including machine learning, statistics, and data visualization. It good resource for those who want to learn about the broader field of data science.
Save
Provides a comprehensive overview of deep learning. It covers a range of topics, including neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone looking to learn more about deep learning and how to use it in practice.
Save
Provides a practical introduction to machine learning for business professionals. It covers a wide range of machine learning topics, from data preprocessing to model evaluation, and how to use machine learning to solve business problems.
Provides a comprehensive introduction to machine learning in German. It covers the basics of supervised and unsupervised learning, as well as more advanced topics such as deep learning and neural networks.
Provides a guide to Kubeflow. It covers a range of topics, including the different components of Kubeflow, how to use Kubeflow, and the benefits of using Kubeflow. It valuable resource for anyone looking to learn more about Kubeflow and how to use it in practice.
Provides an introduction to machine learning. It covers a range of topics, including the different types of machine learning, how to use machine learning, and the benefits of using machine learning. It valuable resource for anyone looking to learn more about machine learning and how to use it in practice.
Provides an introduction to data analytics. It covers a range of topics, including data collection, data preparation, data analysis, and data visualization. It valuable resource for anyone looking to learn more about data analytics and how to use it in practice.
Provides a comprehensive introduction to machine learning, covering the basics of supervised and unsupervised learning, as well as more advanced topics such as deep learning and neural networks. It good starting point for those who are new to machine learning or who want to brush up on the basics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f6romc/smart
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Time
12 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
This course belongs to a
collection
called
Data Engineer, Big Data and ML on Google Cloud auf Deutsch. It's comprised of five courses:
- Building Batch Data Pipelines on GCP auf Deutsch
- Modernizing Data Lakes and Data Warehouses with GCP auf Deutsch
- Building Resilient Streaming Analytics Systems on GCP auf Deutsch
- Smart Analytics, Machine Learning, and AI on GCP auf Deutsch (viewing)
- Google Cloud Platform Big Data and Machine Learning Fundamentals auf Deutsch
Good to know
Teaches a combination of theory and practice for data engineers to use during ML integration
Introduces BigQuery ML and AI Platform Notebooks for more advanced ML capabilities
Integrates Kubeflow for ML solutions application
Offers opportunities to practice in labs
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