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Jerry Kurata

This course shows you how to install and use TensorFlow, a leading machine learning library from Google. You'll see how TensorFlow can create a range of machine learning models, from simple linear regression to complex deep neural networks.

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This course shows you how to install and use TensorFlow, a leading machine learning library from Google. You'll see how TensorFlow can create a range of machine learning models, from simple linear regression to complex deep neural networks.

Developing sophisticated machine learning solutions is a difficult task. There are many processing steps that must be performed, and how this processing is performed is a function of not only the code you write, but also the data you use. In this course, TensorFlow 1: Getting Started, you'll see how TensorFlow easily addresses these concerns by learning TensorFlow from the bottom up. First, you'll be introduced to the installation process, building simple and advanced models, and utilizing additional libraries that make development even easier. Along the way, you'll learn how the unique architecture in TensorFlow lets you perform your computing on systems as small as a Raspberry Pi, and as large as a data farm. Finally, you'll explore using TensorFlow with neural networks in general, and specifically with powerful deep neural networks. By the end of this course, you'll have a solid foundation on using TensorFlow, and have the knowledge to apply TensorFlow to create your own machine learning solutions.

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What's inside

Syllabus

Course Overview
Introduction
Introducing TensorFlow
Creating Neural Networks in TensorFlow
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Debugging and Monitoring
Transfer Learning with TensorFlow
Extending TensorFlow with Add-ons
Summary

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Creates neural networks and models that are a staple of industry machine learning
Builds a foundation in TensorFlow for students with varying levels of background
Develops models with varying degrees of complexity from simple linear regression to complicated neural networks
Incorporates add-ons that simplify model development
Explores TensorFlow at a granular level, including how to install it and utilize its architecture
Suitable for students with varying levels of knowledge, from beginners to more experienced learners

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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 TensorFlow 1: Getting Started with these activities:
Review Python basics
This course is taught in Python. Review the fundamentals to strengthen your programming foundation.
Browse courses on Programming
Show steps
  • Review data types, variables, and functions
  • Complete a Python coding practice problem
Compile TensorFlow resources
Consolidate valuable TensorFlow resources such as tutorials, articles, and tools for easy reference.
Show steps
  • Identify relevant TensorFlow resources.
  • Organize and categorize the resources.
  • Create a central repository for easy access.
Join a TensorFlow study group
Collaborate with peers to reinforce your understanding, share knowledge, and motivate each other.
Show steps
  • Find or create a study group for TensorFlow.
  • Meet regularly with your study group.
  • Discuss course material, work on projects, and provide support to each other.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow coding practice
Complete practice drills to apply your TensorFlow knowledge and build coding confidence.
Show steps
  • Solve a TensorFlow coding problem.
  • Debug your code.
  • Review your solution.
Explore TensorFlow tutorials
Supplement your learning by following online tutorials to further refine your TensorFlow skills.
Show steps
  • Identify a specific TensorFlow topic you want to learn more about.
  • Find a tutorial on that topic.
  • Follow the tutorial instructions.
Connect with TensorFlow experts
Seek guidance and support from experienced TensorFlow professionals to enhance your learning.
Show steps
  • Attend TensorFlow meetups or online communities.
  • Reach out to TensorFlow experts on social media or through email.
  • Ask questions and engage in discussions.
Develop a TensorFlow project
Create a practical project that utilizes TensorFlow to solidify your understanding and showcase your skills.
Show steps
  • Choose a project idea that interests you.
  • Gather the necessary resources.
  • Design and implement your project.
  • Test and refine your project.
  • Share your project with others.
Build a TensorFlow portfolio
Showcase your TensorFlow skills and build your portfolio by creating a collection of projects.
Show steps
  • Identify projects that align with your interests and career goals.
  • Plan and develop your projects.
  • Document your projects and share them with others.

Career center

Learners who complete TensorFlow 1: Getting Started will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain the machine learning models used in a wide range of industries, including healthcare, finance, and transportation. Taking this course may help you build a foundation in TensorFlow, a popular machine learning library, which can be used to create and deploy machine learning models. The syllabus covers topics such as creating neural networks, debugging and monitoring models, and extending TensorFlow with add-ons, all of which are essential skills for a Machine Learning Engineer.
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems that can see and interpret images and videos. This course may help you develop the skills needed to use TensorFlow to create computer vision models. The syllabus includes topics such as creating neural networks, which are used in many computer vision applications.
Data Scientist
Data Scientists use data to solve business problems. This course may help you build a foundation in TensorFlow, which is a popular library for data science tasks such as machine learning and deep learning. The syllabus covers topics such as creating neural networks, debugging and monitoring models, and extending TensorFlow with add-ons, all of which are essential skills for a Data Scientist.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. This course may help you build a foundation in TensorFlow, a popular library for artificial intelligence tasks such as machine learning and deep learning. The syllabus covers topics such as creating neural networks, debugging and monitoring models, and extending TensorFlow with add-ons, all of which are essential skills for an Artificial Intelligence Engineer.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may help you build a foundation in TensorFlow, a popular library for developing machine learning and deep learning applications. The syllabus covers topics such as creating neural networks, debugging and monitoring models, and extending TensorFlow with add-ons, all of which are essential skills for a Software Engineer.
Deep Learning Engineer
Deep Learning Engineers design, develop, and maintain deep learning models. This course may help you build a foundation in TensorFlow, a popular library for developing deep learning models. The syllabus covers topics such as creating neural networks, debugging and monitoring models, and extending TensorFlow with add-ons, all of which are essential skills for a Deep Learning Engineer.
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course may help you build a foundation in TensorFlow, a popular library for developing machine learning algorithms. The syllabus covers topics such as creating neural networks, debugging and monitoring models, and extending TensorFlow with add-ons, all of which are essential skills for a Machine Learning Researcher.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course may be useful for Quantitative Analysts who want to learn how to use TensorFlow to develop machine learning models for financial analysis.
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course may be useful for Data Analysts who want to learn how to use TensorFlow to analyze data and build machine learning models.
Business Analyst
Business Analysts use data to help businesses make informed decisions. This course may be useful for Business Analysts who want to learn how to use TensorFlow to analyze data and build machine learning models.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course may be useful for Operations Research Analysts who want to learn how to use TensorFlow to develop machine learning models for solving business problems.
Statistician
Statisticians collect, analyze, and interpret data. This course may be useful for Statisticians who want to learn how to use TensorFlow to analyze data and build machine learning models.
Project Manager
Project Managers plan, execute, and close projects. This course may be useful for Project Managers who want to learn how to use TensorFlow to develop machine learning models for project management.
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course may be useful for Financial Analysts who want to learn how to use TensorFlow to develop machine learning models for financial analysis.
Product Manager
Product Managers develop and manage the lifecycle of products. This course may be useful for Product Managers who want to learn how to use TensorFlow to develop machine learning features for their products.

Reading list

We've selected six 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 TensorFlow 1: Getting Started.
Provides a thorough introduction to deep learning using Python and the Keras API. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks, and provides practical examples and exercises.
Provides a comprehensive introduction to machine learning and deep learning using Scikit-Learn, Keras, and TensorFlow. It covers the fundamentals of machine learning, including data preprocessing, model selection, and evaluation, and provides practical examples and exercises.
Covers the basics of machine learning and deep learning using TensorFlow. Provides a good overview of the field and useful resource for beginners.
Provides a collection of recipes for solving common problems in deep learning using TensorFlow 1.x. It covers a wide range of topics, including data preprocessing, model training, and deployment. This is more of a reference book than a course supplement.
Provides a visual introduction to deep learning, using simple and intuitive explanations and illustrations. It valuable resource for those who want to understand the concepts of deep learning without getting bogged down in the technical details.
Provides a concise guide to TensorFlow 2, covering the basics of the framework and how to use it to build and train machine learning models. While it may not cover the same topics as the course, it can be a useful reference for those looking to get started with TensorFlow 2 quickly.

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