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Building Classification Models with TensorFlow

Janani Ravi

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This course is no longer available. Find something similar by browsing:

TensorFlow Classification Models Machine Learning Convolutional Neural Networks Recurrent Neural Networks Logistic Regression K-Nearest Neighbors

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Emphasizes techniques such as Logistic Regression and k-Nearest Neighbor, providing a strong foundation in traditional machine learning

Leverages the capabilities of TensorFlow, a widely adopted platform in the industry

Explores advanced deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks, for building powerful classification models

Guides learners in evaluating classification models using metrics like accuracy, precision, and recall, ensuring a thorough understanding of model performance

Suitable for individuals with a basic understanding of machine learning concepts

Requires familiarity with TensorFlow and its core concepts

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Career center

Learners who complete Building Classification Models with TensorFlow will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers develop and deploy machine learning models for a variety of applications. This course can help you succeed in this role by giving you a solid understanding of the principles of classification modeling. TensorFlow is a leading framework for building deep learning models and as such, mastery of it is required for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

Quantitative Analyst use mathematical and statistical models to analyze markets and investments. This course can help you succeed in this career by giving you a strong foundation in machine learning and AI models, which are used extensively to improve investment and trading outcomes.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data Scientists use machine learning and other techniques to extract knowledge from data. Convolutional Neural Networks and Recurrent Neural Networks are important in roles such as image recognition and text generation. This course can help build a foundation for success in this role by teaching you the key principles and techniques used in building classification models with TensorFlow.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use data to solve problems and improve decision making. This course can help you in this career by teaching you techniques, mathematical principles, and algorithms for building classification models.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts help companies improve their performance using data. This course can help you succeed in this role by giving you a strong foundation in building classification models, a key skill for this role.

See salaries and explore the career path for Business Analyst

Research Scientist

Research Scientists investigate and develop new technologies and products for various organizations. This course can help you in this role by providing you with the foundational knowledge and techniques required to build machine learning models.

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Software Engineer

Software Engineers design, develop, and maintain software for companies and organizations. This course will help you succeed as a Software Engineer by teaching you the underlying mathematical principles, algorithms, and techniques used in machine learning models.

See salaries and explore the career path for Software Engineer

Deep Learning Engineer

Deep Learning Engineers research, develop, and implement deep learning models. This course may be helpful for you as a Deep Learning Engineer as it covers TensorFlow, a leading framework for developing deep learning models.

See salaries and explore the career path for Deep Learning Engineer

Data Analyst

Data Analysts use data to help companies improve their products and services. This course can help you in this role by giving you a working knowledge of machine learning techniques and algorithms important to this role.

See salaries and explore the career path for Data Analyst

Cloud Architect

Cloud Architects design, build, and maintain cloud computing systems. It may be useful to take this course to get a strong foundation in deep learning models, which are needed for the development of some of the most advanced cloud computing services.

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Artificial Intelligence Engineer

AI Engineers build, implement, and maintain AI systems. This course may be helpful for this career as it teaches some of the foundational principles and techniques for building deep learning models used by AI.

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Front-End Engineer

Front-End Engineers design and develop the user interface for websites and other applications. A course in TensorFlow may not be the highest priority for your career, but it can help you to better understand the machine learning and AI programming models that your user interface interacts with.

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Database Administrator

Database Administrators maintain and manage computer databases. This course may not be the best for your career. However, it can help build a foundation in TensorFlow, which is a leading framework for building deep learning models.

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Blockchain Developer

Blockchain Developers design and develop blockchain systems. This course may not be the best fit for your career path, but it can give you an introduction to building models in TensorFlow, which is a framework commonly used by data scientists.

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Web Developer

Web Developers design and develop websites and other web applications. This course is unlikely to be the best use of your time as your career path does not heavily rely on building machine learning models with TensorFlow.

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Reading list

We've selected four 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 Building Classification Models with TensorFlow.

Cover image

Cover image

Hands-On Machine Learning with Scikit-Learn, Keras,...

Save

Provides a comprehensive introduction to machine learning with TensorFlow, making it a good choice as background or prerequisite knowledge.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn, Keras,...

Cover image

Cover image

Deep Learning with Python, Second Edition

Save

Provides a broad introduction to deep learning with Python, making it useful for background and prerequisite knowledge, as well as additional reading.

Deep Learning with Python, Second Edition

Deep Learning with Python, Second Edition

Cover image

Cover image

TensorFlow 2.0 Quick Start Guide

Save

Provides a quick introduction to TensorFlow 2.0, which may be useful as background or prerequisite knowledge.

TensorFlow 2.0 Quick Start Guide

TensorFlow 2.0 Quick Start Guide: Get up to speed...

Cover image

Cover image

Deep Learning with TensorFlow

Save

Provides a beginner-friendly introduction to TensorFlow, making it useful as background knowledge.

Deep Learning with TensorFlow - Second Edition:...

Deep Learning with TensorFlow: Explore neural...

Predictive Analytics with TensorFlow: Implement...

Deep Learning with TensorFlow: Explore neural...

Deep Learning with TensorFlow: Explore neural...

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Time

3 hours

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Pluralsight

Instructor

Janani Ravi

This course belongs to a collection called Building Machine Learning Solutions with Tensorflow. It's comprised of 11 courses:

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Emphasizes techniques such as Logistic Regression and k-Nearest Neighbor, providing a strong foundation in traditional machine learning

Leverages the capabilities of TensorFlow, a widely adopted platform in the industry

Explores advanced deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks, for building powerful classification models

Guides learners in evaluating classification models using metrics like accuracy, precision, and recall, ensuring a thorough understanding of model performance

Suitable for individuals with a basic understanding of machine learning concepts

Requires familiarity with TensorFlow and its core concepts

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