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

Implementing the revised approach


In this section, we will cover each part of the implementation. You can find the code by using this GitHub link: https://github.com/jalajthanaki/Chatbot_tensorflow. Note that here, I'm using TensorFlow version 0.12.1. I perform training on a GeForce GTX 1060 6GB GPU for a few hours. In this implementation, we don't need to generate features because the seq2seq model generates its internal representation for sequences of words given in a sentence. Our implementation part has the following steps:

  • Data preparation

  • Implementing the seq2seq model

Let's begin our coding.

Data preparation

During this implementation, we will be using the Cornell movie-dialogs dataset. First of all, we need to prepare data in a format that we can use for training. There is a Python script that is used to perform data preparation. You can find the script at: https://github.com/jalajthanaki/Chatbot_tensorflow/blob/master/data/prepare_data_script/data.py.

Data preparation can be subdivided...