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


In this section, we will choose the Machine Learning (ML) algorithm based on our intuition and then perform training using our training dataset. This is the first model for this particular chapter, so the trained model is our baseline model, which we will improve later on. So, let's decide which kind of ML algorithm suits this stock price prediction application.

The stock price prediction application is a time-series analysis problem, where we need to predict the next point in the time series. This prediction activity is similar to linear regression, so we can say that this application is a kind of regression problem and any algorithm from the regression family should work. Let's select the ensemble algorithm, which is RandomForestRegressor, in order to develop our baseline model. So let's train our baseline model, and, based on the result of that model, we will modify our approach.