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

Understanding the dataset for face emotion recognition


To develop an FER application, we are considering the FER2013 dataset. You can download this dataset from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data. We need to know the basic details about this dataset. The dataset credit goes to Pierre-Luc Carrier and Aaron Courville as part of an ongoing research project.

This dataset consists of 48x48 pixel grayscale images of faces. The task is to categorize each of the faces based on the emotion that has been shown in the image in the form of facial expressions. The seven categories are as follows, and for each of them there is a numeric label that expresses the category of the emotion:

  • 0 = Anger

  • 1 = Disgust

  • 2 = Fear

  • 3 = Happiness

  • 4 = Sadness

  • 5 = Surprise

  • 6 = Neutral

This dataset has the fer2013.csv file. This csv file will be used as our training dataset. Now let's look at the attributes of the file. There are three columns in the...