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

Introducing the problem statement


In this chapter, we will be building an object detection application. We won't just be detecting objects, but we will be building the application that detects the objects in real time. This application can be used in self-driving cars, for segregation tasks in the agricultural field, or even in the robotics field. Let's understand our goal and what we are actually building.

We want to build an application in which we will provide the live webcam video stream or the live video stream as the input. Our application will use pre-trained Machine Learning models, which will help us predict the objects that appear in the video. This means that, if there is a person in the video, then our application can identify the person as a person. If the video contains a chair or a cup or a cell phone, then our application should identify all these objects in the correct manner. So, our main goal in this chapter is to build an application that can detect the objects in images...