Introduction to TensorFlow from Pluralsight

This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns. We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.

What's inside

Syllabus

Introduction to course

Introduction to TensorFlow

Design and Build a TensorFlow Input Data Pipeline

Training neural networks with Tensorflow 2 and the Keras Sequential API

Training neural networks with Tensorflow 2 and Keras Functional API

Summary

Course Resource

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Useful for understanding contemporary machine learning uses and methods

Excellent resource for developing a strong foundation of knowledge of the TensorFlow 2.x API and Keras

Hands-on labs provide ample opportunity to apply concepts

Taught by instructors from Google Cloud, who are recognized for their work in machine learning

Covers a wide range of topics, from data preprocessing to model deployment

Requires familiarity with Python programming and machine learning concepts

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 Introduction to TensorFlow with these activities:

Volunteer with a TensorFlow user group

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Give back to the TensorFlow community and connect with other learners by volunteering with a TensorFlow user group.

Browse courses on TensorFlow

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Find a TensorFlow user group in your area.
Attend a meeting and introduce yourself.
Volunteer to help with organizing events or mentoring other members.

Review the Keras API

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Familiarize yourself with the Keras API before starting the course to improve your understanding of deep learning models.

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Read the Keras documentation and tutorials.
Complete the Keras getting started tutorial.

Read 'Hands-On Machine Learning with TensorFlow 2.0'

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Deepen your knowledge of TensorFlow and machine learning by reading a comprehensive book that provides practical examples and guidance.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read Chapter 1: Introduction to TensorFlow 2.0
Complete the exercises in Chapter 2: Building and Training Neural Networks

Five other activities

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Follow TensorFlow tutorials

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Supplement your learning by following TensorFlow tutorials to gain practical experience building and training machine learning models.

Browse courses on TensorFlow

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Complete the TensorFlow basics tutorial.
Build a simple image classification model using TensorFlow.

Practice building TensorFlow models

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Solidify your understanding of TensorFlow by practicing building and training models for various tasks.

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Create a TensorFlow model for a binary classification task.
Train and evaluate a TensorFlow model for a regression task.

Create a blog post on TensorFlow

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Demonstrate your understanding of TensorFlow and share your knowledge with others by creating a blog post that covers a specific aspect of the framework.

Browse courses on TensorFlow

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Choose a topic related to TensorFlow that you are familiar with.
Write a draft of the blog post, including an introduction, body, and conclusion.
Proofread and edit your blog post.

Attend a TensorFlow workshop

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Enhance your practical skills by attending a TensorFlow workshop where you can learn from experts and collaborate with other learners.

Browse courses on TensorFlow

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Find and register for a TensorFlow workshop.
Attend the workshop and participate actively.

Contribute to an open-source TensorFlow project

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Gain hands-on experience and contribute to the TensorFlow community by working on an open-source project that aligns with your interests.

Browse courses on TensorFlow

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Identify an open-source TensorFlow project to contribute to.
Fork the project and create a branch for your changes.
Make your changes and submit a pull request.

Career center

Learners who complete Introduction to TensorFlow will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists leverage their knowledge of mathematics, computer science, and business to understand and solve complex problems. Using TensorFlow, you can build, train, and deploy machine learning models that can help you identify patterns, make predictions, and gain valuable insights from large and diverse datasets. This course introduces you to the fundamentals of TensorFlow, including concepts such as feature columns and input data pipelines, which are essential for building robust and scalable machine learning models. By taking this course, you can enhance your skills in data modeling and analysis, making you well-equipped for the role of a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and maintaining machine learning systems. TensorFlow is an essential tool for Machine Learning Engineers, as it allows them to build and train machine learning models efficiently. This course provides you with a solid foundation in TensorFlow's capabilities and best practices, enabling you to build and deploy robust machine learning models for various applications. Whether you are working with structured or unstructured data, this course will help you develop the skills necessary to succeed as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Deep Learning Engineer

Deep Learning Engineers specialize in designing, developing, and deploying deep learning models. TensorFlow is a powerful tool for building deep learning models, particularly with its Keras API. This course introduces you to the core concepts of deep learning and provides hands-on experience in building and training deep learning models using TensorFlow. By taking this course, you can develop the skills to create innovative and effective deep learning solutions.

See salaries and explore the career path for Deep Learning Engineer

Data Analyst

Data Analysts leverage their skills in data analysis and interpretation to make data-driven decisions. TensorFlow can help Data Analysts build predictive models and perform advanced data analysis. This course introduces you to the basics of TensorFlow and provides hands-on experience in building machine learning models. By completing this course, you can enhance your data analysis skills and become proficient in using TensorFlow to uncover valuable insights from data.

See salaries and explore the career path for Data Analyst

AI Engineer

AI Engineers develop, deploy, and maintain artificial intelligence systems. TensorFlow is a fundamental tool for AI Engineers, as it provides a comprehensive platform for building and training machine learning models. This course introduces you to the key concepts of TensorFlow and will give you hands-on experience in building, training, and evaluating machine learning models. By taking this course, you can build a strong foundation for a career as an AI Engineer.

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Software Engineer (Machine Learning)

Software Engineers specialized in Machine Learning focus on developing and maintaining software systems that incorporate machine learning algorithms. TensorFlow is widely used in industry for building and deploying machine learning solutions. This course will provide you with a solid foundation in TensorFlow and teach you how to apply it to solve real-world problems. By completing this course, you will enhance your software engineering skills and become proficient in using TensorFlow.

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Research Scientist

Research Scientists in the field of machine learning and deep learning often use TensorFlow in their research. This course may be useful if you are interested in understanding the latest advancements in TensorFlow and how to apply them in your own research. By taking this course, you will gain a deeper understanding of TensorFlow's capabilities and how to use it effectively for research purposes.

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Business Analyst

Business Analysts use data to help businesses make better decisions. TensorFlow can be used to analyze large and complex datasets, identify patterns, and make predictions. This course may be useful if you are interested in using TensorFlow to enhance your data analysis skills and gain a competitive edge in your role as a Business Analyst.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers build and maintain the infrastructure that supports data analysis and machine learning. TensorFlow is a commonly used tool for building and deploying machine learning models. This course may be useful if you are interested in learning how to use TensorFlow to build data pipelines and prepare data for machine learning models.

See salaries and explore the career path for Data Engineer

Data Architect

Data Architects design and oversee the construction of data systems. TensorFlow is a powerful tool for building and deploying machine learning models. This course may be useful if you are interested in learning how to use TensorFlow to create data-driven solutions and improve the efficiency of your data architecture.

See salaries and explore the career path for Data Architect

Statistician

Statisticians use data to understand the world around us. TensorFlow can be used to analyze large and complex datasets, identify patterns, and make predictions. This course may be useful if you are interested in using TensorFlow to enhance your statistical analysis skills and gain a deeper understanding of data.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to assess risk and make investment decisions. TensorFlow can be used to build and deploy machine learning models for predictive analytics and risk management. This course may be useful if you are interested in using TensorFlow to enhance your quantitative analysis skills and gain a competitive edge in the financial industry.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. TensorFlow can be used to analyze large and complex datasets, identify patterns, and make predictions. This course may be useful if you are interested in using TensorFlow to enhance your financial analysis skills and gain a deeper understanding of financial markets.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematics and statistics to assess risk and make financial decisions. TensorFlow can be used to build and deploy machine learning models for predictive analytics and risk management. This course may be useful if you are interested in using TensorFlow to enhance your actuarial skills and gain a competitive edge in the insurance industry.

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Software Tester

Software Testers ensure that software systems meet the requirements and perform as expected. TensorFlow is used in developing and testing machine learning systems. This course may be useful if you are interested in learning how to use TensorFlow to test machine learning models and gain a deeper understanding of software testing.

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Introduction to TensorFlow

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