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


In this section, we will look into our dataset. We have considered an IMDb dataset, which you can download at: http://ai.stanford.edu/~amaas/data/sentiment/. After clicking on this link, you can see that there is a link provided on the page. This link is titled Large Movie Review Dataset v1.0; we need to click on it. This way, we can download the IMDb dataset. Once you have downloaded the dataset, you need to extract the .tar.gz file. Once you extract the .tar.gz file, you can see that there are two folders inside the extracted folder and some other files. Let's look at each of them in the following section.

Understanding the content of the dataset

After extracting the dataset file, we'll see that there are some folders and files inside it. We will be discussing all of the content's meaning and what we will be using for our training purposes. This dataset has two folders and three files:

  • train folder

  • test folder

  • imdb.vocab file

  • imdbEr.txt

  • README

Train folder

This folder...