Book Image

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
3 (1)
Book Image

TensorFlow Developer Certificate Guide

3 (1)
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 the last chapter, we covered linear regression with TensorFlow, where we looked at both simple and multiple linear regression; we also explored various metrics for evaluating regression models. We concluded the chapter with a real-world use case, where we built a salary prediction model, and we used this to predict the salaries of new employees based on a set of features. In this chapter, we will continue with modeling in TensorFlow – this time, by exploring classification problems with TensorFlow.

We will start by looking at the concept of classification modeling, after which we will examine the various evaluation metrics for classification modeling and how we can apply them to various use cases. We will look at binary, multi-class, and multi-label classification modeling. Finally, we will walk through a case study, putting all we have learned into practice by building a binary classification model to predict whether a student will drop...