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

Chapter 7. Text Summarization

In this chapter, we will be building the summarization application. We will specifically focus on the textual dataset. Our primary goal is to perform the summarization task on medical notes. Basically, the idea is to come up with a good solution to summarize medical transcription documents.

This kind of summarization application helps doctors a great manner. You ask how? Let's take an example. Suppose a patient has 10 years of history with a certain disease, and after 10 years, he consults a new doctor for better results. On the first day, the patient needs to hand over their last 10 years of medical prescriptions to this new doctor. After that, the doctor will need to study all these documents. The doctor also relies on the conversation he had with the patient. By using medical notes and conversations with the patient, the doctor can find out the patient's health status. This is quite a lengthy method.

However, what if we could generate a summary of the patient...