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Launching into Machine Learning auf Deutsch

Nach einem ersten Überblick über die Geschichte von ML erfahren Sie in diesem Kurs, weshalb heute mithilfe neuronaler Netzwerke viele Probleme so erfolgreich gelöst werden können. Wir erklären anschließend, wie Sie überwachtes Lernen zur Problemlösung einrichten und mithilfe des Gradientenverfahrens gute Ergebnisse erzielen. Dazu sind Datasets erforderlich, mit denen die Generalisierung möglich ist. In diesem Kurs zeigen wir Ihnen, wie Sie Datasets auf wiederholbare Weise erstellen, um Experimente zu ermöglichen. Kursziele: Erkennen, warum Deep Learning derzeit beliebt ist Modelle anhand von Verlustfunktionen und...

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Starts with a basic overview of basic ML concepts to build a foundation before delving deeper into neural networks

Addresses common machine learning pitfalls and how to minimize them

Covers the nuances of neural networks, which have been successful in solving numerous problems

Demonstrates how to set up supervised learning for problem-solving and achieve good results using gradient descent

Highlights the importance of optimizing and evaluating models based on loss functions and performance metrics

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Career center

Learners who complete Launching into Machine Learning auf Deutsch will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models. This course will teach you about the history of machine learning, deep learning, and the gradient descent method. You'll also learn how to create repeatable and scalable datasets to train, evaluate, and test your models. This course provides a strong foundation for a career in Machine Learning Engineering.

See salaries and explore the career path for Machine Learning Engineer

Research Scientist

Research Scientists conduct research in a variety of fields, including machine learning. This course will provide you with a solid foundation in machine learning concepts and techniques. You'll learn about the history of machine learning, neural networks, and deep learning. You'll also learn how to create and evaluate machine learning models. This knowledge will be invaluable if you are interested in a career as a Research Scientist.

See salaries and explore the career path for Research Scientist

Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, test, and deploy AI systems. This course provides a good overview of machine learning, neural networks, and deep learning, which are essential topics for anyone working in the field of Artificial Intelligence Engineering. By taking this course, you'll gain the knowledge and skills needed to succeed in this rapidly growing field.

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Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. This course will teach you about machine learning techniques that can be used to extract insights from data. You'll also learn how to create and evaluate machine learning models, which are essential skills for Data Analysts.

See salaries and explore the career path for Data Analyst

Data Engineer

Data Engineers build and maintain the infrastructure that is used to store and process data. Machine learning models require large amounts of data to train. This course will help you understand the basics of machine learning and how it can be used to train models. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as a Data Engineer.

See salaries and explore the career path for Data Engineer

Software Developer

Software Developers design, develop, and test software applications. Machine learning is increasingly being used to develop new software applications. This course will help you understand the basics of machine learning and how it can be used to develop software applications. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as a Software Developer.

See salaries and explore the career path for Software Developer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course will help you build a foundation in machine learning, which is increasingly being used in the financial industry. You'll learn about how to use machine learning to identify trading opportunities, manage risk, and make investment decisions. This knowledge may be helpful if you are interested in a career as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data Scientists analyze large volumes of data to extract valuable insights. By completing this course, you will gain the skills needed to leverage machine learning algorithms to construct predictive models. This course may be helpful in your transition to becoming a Data Scientist as it covers the foundational concepts of machine learning, including how to set up supervised learning to solve problems and optimize models using loss functions and performance metrics.

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Health Data Analyst

Health Data Analysts use data to improve the quality and efficiency of healthcare. Machine learning is increasingly being used to analyze health data and improve healthcare outcomes. This course will help you understand the basics of machine learning and how it can be used to analyze health data. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as a Health Data Analyst.

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Business Analyst

Business Analysts identify and solve business problems using data and analysis. Machine learning is increasingly being used to solve business problems. This course will help you understand the basics of machine learning and how it can be used to solve business problems. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as a Business Analyst.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to improve the efficiency of organizations. Machine learning is increasingly being used to solve operational problems. This course will help you understand the basics of machine learning and how it can be used to solve operational problems. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as an Operations Research Analyst.

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Financial Analyst

Financial Analysts use data to make investment decisions. Machine learning is increasingly being used to analyze financial data and make investment decisions. This course will help you understand the basics of machine learning and how it can be used to analyze financial data. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts use data to understand customer behavior and develop marketing campaigns. Machine learning is increasingly being used to analyze customer data and create targeted marketing campaigns. This course will help you understand the basics of machine learning and how it can be used to analyze customer data. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as a Marketing Analyst.

See salaries and explore the career path for Marketing Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. Machine learning is increasingly being used to create new products and features. This course will help you understand the basics of machine learning and how it can be used to create successful products. You'll learn about the different types of machine learning algorithms, how to train and evaluate models, and how to deploy models into production. This knowledge may be helpful if you are interested in a career as a Product Manager.

See salaries and explore the career path for Product Manager

Software Engineer

Software Engineers apply engineering principles to the design, development, and maintenance of software systems. This course will help you build a foundation in machine learning, which is becoming increasingly important in the software industry. You'll learn about how to use machine learning to solve problems, optimize models, and create datasets. This knowledge may be helpful if you are interested in working as a Software Engineer.

See salaries and explore the career path for Software Engineer

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Time

8 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 (viewing)
Feature Engineering auf Deutsch
Art and Science of Machine Learning auf Deutsch
How Google does Machine Learning auf Deutsch
Intro to TensorFlow auf Deutsch

Good to know

Starts with a basic overview of basic ML concepts to build a foundation before delving deeper into neural networks

Addresses common machine learning pitfalls and how to minimize them

Covers the nuances of neural networks, which have been successful in solving numerous problems

Demonstrates how to set up supervised learning for problem-solving and achieve good results using gradient descent

Highlights the importance of optimizing and evaluating models based on loss functions and performance metrics

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