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

Problems with the revised approach


In this section, we will be discussing what the problems with the revised approach are. Is there any way in which we can optimize the revised approach? So first of all, let's discuss the area of improvement so that during the upcoming approach, we can focus on that particular point.

Some of the points that I want to highlight are as follows:

  • In the previous version of our chatbot there was a lack of reasoning, which means the chatbot couldn't answer the question by applying basic reasoning to it. This is what we need to improve.

  • Let me give you an example. Suppose I tell chatbot a story: John is in the kitchen. Daniel is in the bathroom. After that, say, I ask the chatbot this question: Where is John? The chatbot that we have built so far will not be able to answer this simple question. We as humans answer these kinds of questions well.

  • We try to implement this kind of functionality in our next approach so that we can enable some features of AI in the chatbot...