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

Forecasting Apple stock price data

We have now covered everything we need to know about time series for the TensorFlow Developer Certificate exam. Let us round off this chapter and the book with a real-world use case on time series. For this exercise, we will be working with a real-world dataset (Apple closing day stock price). Let’s see how we can do this next. The Jupyter notebook for this exercise can be found here: https://github.com/PacktPublishing/TensorFlow-Developer-Certificate-Guide. Let’s begin:

  1. We start by importing the required libraries:
    import numpy as np
    import matplotlib.pyplot as plt
    import tensorflow as tf
    from tensorflow import keras
    import yfinance as yf

    Here, we are using a new library called yfinance. This lets us access the Apple stock data for our case study.

Note

You may want to run pip install yfinance to get it working if the import fails.

  1. Create a DataFrame:
    df_apple = yf.Ticker(tickerSymbol)
    df_apple = df_apple.history(period...