Book Image

Machine Learning Solutions

Book Image

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Index

Building the face emotion recognition model


In this section, we will implement the application of FER using CNN. For coding purposes, we will be using the TensorFlow, TFLearn, OpenCV, and Numpy libraries. You can find the code by using this GitHub link: https://github.com/jalajthanaki/Facial_emotion_recognition_using_TensorFlow. These are the steps that we need to follow:

  1. Preparing the data

  2. Loading the data

  3. Training the model

Preparing the data

In this section, we will be preparing the dataset that can be used in our application. As you know, our dataset is in grayscale. We have two options. One is that we need to use only black and white images, and if we are using black and white images, then there will be two channels. The second option is that we can convert the grayscale pixel values into RGB (red, green, and blue) images and build the CNN with three channels. For our development purposes, we are using two channels as our images are in grayscale.

First of all, we are loading the dataset and...