Book Image

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
4 (2)
Book Image

TensorFlow Developer Certificate Guide

4 (2)
By: Oluwole Fagbohun

Overview of this book

The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional.
Table of Contents (20 chapters)
1
Part 1 – Introduction to TensorFlow
6
Part 2 – Image Classification with TensorFlow
12
Part 3 – Natural Language Processing with TensorFlow
15
Part 4 – Time Series with TensorFlow

Classification with TensorFlow

In Chapter 1, Introduction to Machine Learning, we talked about supervised learning and briefly talked about classification modeling. Classification modeling involves predicting classes in our target variable. When the classes we try to predict are binary (for example, trying to predict whether a pet is either a dog or a cat, whether an email is spam or not, or whether a patient has cancer or not), this type of classification scenario is referred to as binary classification.

Then again, we may be faced with a problem where we want to build an ML model to predict the different breeds of dogs. In this case, we have more than two classes, so this type of classification is called multi-class classification. Just like binary classification problems, in multi-class classification, our target variable can only belong to one class out of multiple classes – our model will select either a bulldog, a German shepherd, or a pit bull. Here, the classes are...