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 understand the meaning of data attributes, which will help us understand what kind of dataset we are going to deal with and what kind of preprocessing is needed for the dataset. We understand our dataset in two sections, and those sections are given as follows:

  • Understanding the DJIA dataset

  • Understanding the NYTimes news article dataset

Understanding the DJIA dataset

In the DJIA dataset, we have seven data attributes. They are quite easy to understand, so let's look at each of them one by one:

  • Date: The first column indicates the date in the YYYY-MM-DD format when you see data in the .csv file.

  • Open: This indicates the price at which the market opens, so it is the opening value for the DJIA index for that particular trading day.

  • High: This is the highest price for the DJIA index for a particular trading day.

  • Low: This is the lowest price for DJIA index for a particular trading day.

  • Close: The price of DJIA index at the close of the trading...