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

Selecting the machine learning algorithm


Sentiment analysis is a classification problem. There are some algorithms that can be really helpful for us. In movie reviews, you may discover that there are some phrases that appear quite frequently. If these frequently used phrases indicate some kind of sentiment, most likely, they are phrases that indicate a positive sentiment or a negative sentiment. We need to find phrases that indicate a sentiment. Once we find phrases that indicate sentiment, we just need to classify the sentiment either in a positive sentiment class or a negative sentiment class. In order to find out the actual sentiment class, we need to identify the probability of the most likely positive phrases and most likely negative phrases so that based on a higher probability value, we can identify that the given movie review belongs to a positive or a negative sentiment. The probabilities we will be taking into account are the prior and posterior probability values. This is the...