Apprentice Level
6 Hands-on labs

TensorFlow Developer Certificate Exam Prep

By Adam Vincent

TensorFlow Developer Certificate Exam Prep

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13 hours
  • 59 Lessons
  • 6 Hands-On Labs

About the course

Do you want to learn more about machine learning? Do you want to code your own machine learning projects? Do you want to get certified in one of the most popular machine learning frameworks? This course is for you!

TensorFlow is a fantastic tool for both beginners and experts in machine learning. In this course, we will build up our understanding of TensorFlow and neural networks from first principles. What is a tensor? How does it flow? How can you use these tools to teach machines?

Once we’re comfortable with the basic building blocks, we’ll create models to explore the three main areas covered by the TensorFlow Developer Exam: Computer Vision, Sequence Forecasting, and Natural Language Processing. After you’ve built models along with me, you’ll have the opportunity to practice your skills on similar problems in our labs!

Come jump start your machine learning understanding and your career!

Lab Highlights

Generating Text Using TensorFlow Classifying Text Using TensorFlow Predicting Sequences Using TensorFlow Classify Images Using TensorFlow Loading Data from TensorFlow Datasets Load a TensorFlow Dataset with Web Data
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  • Chapter 1 4 Lessons Introduction 12:22

    What You Need to Know before Starting This Course

    3:50

    Course Overview

    4:45

    Labs and Practice Exam

    1:43

    About the Architect

    2:04
  • Chapter 2 2 Lessons Environment Setup 15:47

    Install PyCharm

    7:39

    Set Up PyCharm

    8:08
  • Chapter 3 9 Lessons TensorFlow Basics 38:50

    Introducing the Basics of TensorFlow

    0:38

    What Is a Tensor?

    3:19

    Understanding Rank 0 Tensors

    2:28

    Understanding Rank 1 Tensors

    4:32

    Understanding Rank 2 Tensors

    4:38

    Understanding Rank 3 Tensors and Beyond

    5:55

    How Does a Tensor Flow?

    8:59

    Keras

    7:13

    Reviewing the TensorFlow Basics

    1:08
  • Chapter 4 10 Lessons Neural Nets in TensorFlow 1:05:22

    Introducing Neural Nets in TensorFlow

    2:40

    Deep Neural Network (DNN)

    8:03

    Convolutions

    10:22

    Pooling

    4:13

    Convolutional Neural Network (CNN)

    3:55

    Recurrent Neural Network (RNN)

    13:10

    Loss Functions

    10:49

    Optimizers

    5:53

    Activation Functions

    4:31

    Neural Network Review

    1:46
  • Chapter 5 9 Lessons Data In 2:20:40

    Data In Overview

    3:13

    Reading CSV

    10:01

    Reading JSON

    9:21

    Reading Images

    13:38

    Reading Plain Text

    10:37

    Using the Dataset API

    11:32

    Reading TFRecord

    7:18

    Load Data from TensorFlow 2 Datasets

    45:00 Hands-On Lab

    Load a TensorFlow Dataset with Web Data

    30:00 Hands-On Lab
  • Chapter 6 5 Lessons Image Classification 2:19:39

    Image Data Wrangling

    8:36

    Building an Image Classification Model

    13:00

    Evaluating the Image Classification Model

    12:30

    Improving the Image Classification Model

    15:33

    Build an Image Classification Model Using TensorFlow 2

    1:30:00 Hands-On Lab
  • Chapter 7 6 Lessons Series and Sequence Modeling 2:21:55

    Series and Sequence Overview

    0:49

    Series and Sequence Data Wrangling

    15:17

    Building a Series and Sequence Model

    14:27

    Evaluating the Series and Sequence Model

    8:55

    Improving Series and Sequence Models

    12:27

    Build a Series and Sequence Model Using TensorFlow 2

    1:30:00 Hands-On Lab
  • Chapter 8 9 Lessons Natural Language Processing 3:36:35

    Introducing Natural Language Processing

    4:05

    Natural Language Data Wrangling

    10:39

    Data Wrangling in a Natural Language Processing Model

    7:57

    Building a Natural Language Processing Model

    12:32

    Evaluating the NLP Model

    12:52

    Improving the NLP Model

    15:15

    NLP Review

    3:15

    Build a Natural Language Processing Model Using TensorFlow 2

    1:30:00 Hands-On Lab

    Build a Natural Language Processing Model to Generate Text Using TensorFlow 2

    1:00:00 Hands-On Lab
  • Chapter 9 3 Lessons About the Exam 17:47

    Available Resources

    7:20

    Taking the Exam

    5:44

    Exam Tips

    4:43
  • Chapter 10 2 Lessons Course Wrap Up 10:02

    Course Summary

    5:27

    What's Next?

    4:35

What you will need

  • Strong Python coding skills

  • Some exposure to machine learning

  • Some mathematics (no PhD required!)

What you can expect

  • Have the knowledge and skills to pass the TensorFlow Developer Certificate Exam

  • Understand tensors and the properties used to define them.

  • Understand Keras layers and how they are used to create neural networks

  • Determine the type of machine learning problem being solved

  • Understand model hyperparameters, such as optimization functions, loss, and activation functions

  • Know how to load and parse many common data formats, including CSV, JSON, images, text, and TensorFlow's TFRecord.

  • Be able to build and train models for computer vision

  • Be able to build and train models for sequence forecasting

  • Be able to build and train models for natural language processing

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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