Achieve TensorFlow certification with this comprehensive guide covering all exam topics using a hands-on, step-by-step approach—perfect for aspiring TensorFlow developers

Key Features

• Build real-world computer vision, natural language, and time series applications
• Learn how to overcome issues such as overfitting with techniques such as data augmentation
• Master transfer learning—what it is and how to build applications with pre-trained models

Book Description

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.

What you will learn

• Prepare for success in the TensorFlow Developer Certification exam
• Master regression and classification modelling with TensorFlow 2.x
• Build, train, evaluate, and fine-tune deep learning models
• Combat overfitting using techniques such as dropout and data augmentation
• Classify images, encompassing preprocessing and image data augmentation
• Apply TensorFlow for NLP tasks like text classification and generation
• Predict time series data, such as stock prices
• Explore real-world case studies and engage in hands-on exercises

Who this book is for

This book is for machine learning and data science enthusiasts, as well as data professionals aiming to demonstrate their expertise in building deep learning applications with TensorFlow. Through a comprehensive hands-on approach, this book covers all the essential exam prerequisites to equip you with the skills needed to excel as a TensorFlow developer and advance your career in machine learning. A fundamental grasp of Python programming is the only prerequisite.

Table of Contents

1. Introduction to Machine Learning
2. Introduction to Tensorflow
3. Linear Regression with Tensorflow
4. Classification with Tensorflow
5. Image Classification with Neural Networks
6. Improving the model
7. Image Classification with Convolution Neural Networks
8. Handling Overfitting
9. Transfer Learning
10. Introduction to Natural Language Processing
11. Natural Language Processing with Tensorflow
12. Introduction to Time Series, Sequence and Prediction
13. Time Series, Sequence and Prediction with Tensorflow

About the Author

Oluwole Fagbohun, a certified TensorFlow developer and machine learning engineer, currently he is serving Vice President of Engineering and Environmental Data at ChangeBlock, where he is spearheading the fight against climate change with machine learning. Beyond his role at ChangeBlock, Oluwole is the founder of Readrly an EdTech startup employing AI to enhance children's reading skills, offering engaging and gamified learning experiences.