Book Image

Real-World Machine Learning Projects Using TensorFlow [Video]

By : Mohamed Elsayed Mohamed Elhaj Abdou
Book Image

Real-World Machine Learning Projects Using TensorFlow [Video]

By: Mohamed Elsayed Mohamed Elhaj Abdou

Overview of this book

<p>Machine learning algorithms and research are mushrooming due to their accuracy at solving problems. This course walks you through developing real-world projects using TensorFlow in your ML projects.</p> <p>The initial project will deal with assessing the viability of expanding your Restaurant business using a single variable linear regression. You will use Linear Regression with multiple variables with an example involving buying and selling a property at the best prices and use a dataset containing 11 features to deal with it. Next, you will create an algorithm to detect anomalous behavior in server computers using Gaussian methods. Finally, you'll design and build a convolutional Neural Networks model on a Traffic Signal Classifier from scratch.</p> <p>By the end of this course you will be using TensorFlow in real-world scenarios, and you'll be confident enough to use ML Algorithms to build your own projects.</p> <p>The code bundle for this video course is available at -&nbsp;<a href="https://github.com/PacktPublishing/Real-world-Machine-Learning-Projects-using-TensorFlow" target="_blank">https://github.com/PacktPublishing/Real-world-Machine-Learning-Projects-using-TensorFlow</a></p> <h1>Style and Approach</h1> <p>The course first defines a problem and then it gives you its solution along with the steps to solve it practically by using Python with TensorFlow. You will be working and building examples from scratch, starting with simple problems and progressing to complicated ones.</p>
Table of Contents (5 chapters)
Chapter 4
Anomaly Detection Algorithm
Content Locked
Section 2
Server Computer's Behavior
In this video, we’ll code the example that we covered in the last video. - Monitor and import the required libraries - Get the mean and variance for the input feature X - Plot and detect the anomalies