In this chapter, our primary goal is to understand how to build a chatbot. Chatbots, or question-answering systems (QA systems), are really helpful. Let's consider a fun example. Suppose you are a student and you have five books to read. Reading and understanding five books may take time. What if you could feed the content of all these five books to the computer and ask only relevant questions? By doing this, students could learn the concepts and new information faster. As we all know, major internet product companies are arranging information so it is easy to access. Chatbots or QA systems will help us understand the meaning behind this information. This is the main reason why chatbots are the buzzword for the year 2017. Whatever application you can think of, you can make a chatbot for it. Many messaging platforms now host chatbots built by developers, including Facebook Messenger, Slack, WeChat, and so on. Chatbots are the new app because they already...
Machine Learning Solutions
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
Free Chapter
Credit Risk Modeling
Stock Market Price Prediction
Customer Analytics
Recommendation Systems for E-Commerce
Sentiment Analysis
Job Recommendation Engine
Text Summarization
Developing Chatbots
Building a Real-Time Object Recognition App
Face Recognition and Face Emotion Recognition
Building Gaming Bot
List of Cheat Sheets
Strategy for Wining Hackathons
Index
Customer Reviews