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Explore and Evaluate Models using Model Garden

Google Cloud Training

This is a self-paced lab that takes place in the Google Cloud console.

In this lab, you explore and evaluate AI models using Model Garden.

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What's inside

Syllabus

Explore and Evaluate Models using Model Garden

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides learners with the skills to evaluate AI models efficiently utilizing Model Garden
Suitable for learners interested in Machine Learning and model evaluation techniques
Taught by Google Cloud Training, recognized experts in the field
Can be completed at the learner's desired pace, allowing for flexibility in learning
May require prior knowledge in Machine Learning or AI, learners new to the field may need additional resources
Involvement of Google Cloud Training ensures the course materials are up-to-date with industry best practices

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Activities

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Career center

Learners who complete Explore and Evaluate Models using Model Garden will develop knowledge and skills that may be useful to these careers:
AI Engineer
AI Engineers apply machine learning and artificial intelligence techniques to solve business problems. **Explore and Evaluate Models using Model Garden** provides an introduction to model evaluation, a key aspect of AI development, helping aspiring AI Engineers build a foundation in this field.
Statistician
Statisticians collect, analyze, and interpret data to provide insights for decision-making. **Explore and Evaluate Models using Model Garden** offers an opportunity to gain practical experience with model evaluation, a valuable skill for Statisticians.
Data Science Consultant
Data Science Consultants provide expertise in data science and analytics to help businesses solve problems and make informed decisions. **Explore and Evaluate Models using Model Garden** can provide valuable hands-on experience with model evaluation, a key skill for Data Science Consultants.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. The **Explore and Evaluate Models using Model Garden** course offers an opportunity to gain hands-on experience with model evaluation and selection, a critical skill for Machine Learning Engineers.
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations and provide financial advice. **Explore and Evaluate Models using Model Garden** may provide helpful insights into model evaluation and selection, which are relevant skills for Financial Analysts.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve business operations. **Explore and Evaluate Models using Model Garden** may provide helpful insights into model evaluation and selection, which are relevant skills for Operations Research Analysts.
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. **Explore and Evaluate Models using Model Garden** can provide a valuable introduction to the techniques and tools used in data science, helping aspiring Data Scientists build a foundation in this field.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. **Explore and Evaluate Models using Model Garden** may provide helpful insights into model evaluation and selection, which are relevant skills for Quantitative Analysts.
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the data analysis and statistical skills gained in this course can be helpful for aspiring Actuaries.
Data Engineer
Data Engineers design and maintain data systems and infrastructure. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the data management skills gained in this course can be helpful for aspiring Data Engineers.
Data Analyst
Data Analysts clean, analyze, and visualize data to extract insights and trends. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the skills gained in this course, such as data exploration and analysis, can be applied to the broader field of data analysis.
Market Researcher
Market Researchers collect and analyze data to understand market trends and consumer behavior. While **Explore and Evaluate Models using Model Garden** focuses on model evaluation, the data analysis and interpretation skills gained in this course can be applied to market research roles.
Business Analyst
Business Analysts identify and solve business problems by analyzing data and providing recommendations. While **Explore and Evaluate Models using Model Garden** focuses on model evaluation, the data analysis and problem-solving skills gained in this course can be applied to business analysis roles.
Product Manager
Product Managers lead the development and launch of new products and services. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the understanding of data and analytics gained in this course can be valuable for Product Managers.
Software Engineer
Software Engineers design, develop, and maintain software applications. While **Explore and Evaluate Models using Model Garden** focuses on model evaluation, the programming and problem-solving skills gained in this course can be applied to software engineering roles.

Reading list

We've selected eight 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 Explore and Evaluate Models using Model Garden.
Provides a comprehensive introduction to machine learning with TensorFlow, covering topics such as data preprocessing, model training, and evaluation. It great resource for learners who want to gain a solid foundation in machine learning and TensorFlow.
Covers a wide range of machine learning topics, including supervised and unsupervised learning, model selection, and feature engineering. It valuable resource for learners who want to gain a practical understanding of machine learning algorithms and their implementation in Python.
Provides a comprehensive introduction to deep learning with Python, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for learners who want to gain a deeper understanding of deep learning and its applications.
Provides a comprehensive introduction to natural language processing with Python, covering topics such as text preprocessing, part-of-speech tagging, and named entity recognition. It valuable resource for learners who want to gain a practical understanding of how to use NLP techniques for various tasks.
Provides a comprehensive overview of computer vision algorithms and applications, covering topics such as image processing, object detection, and image recognition. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of computer vision.
Provides a comprehensive overview of generative adversarial networks (GANs), covering topics such as GAN architectures, training GANs, and applications of GANs. It valuable resource for learners who want to gain a deeper understanding of GANs and their applications.
Provides a gentle introduction to machine learning for absolute beginners, covering topics such as the basics of machine learning, different types of machine learning algorithms, and how to use machine learning for various tasks. It valuable resource for learners who are new to machine learning and want to gain a basic understanding of the field.
Provides a practical introduction to deep learning with R, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for learners who want to gain a practical understanding of how to use deep learning for various tasks in R.

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