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

How to optimize the existing approach


There are certain techniques that can help us improve this application. The key techniques that can help us improvise the baseline approach are as follows:

  • We can use word embedding-based techniques such as Word2Vec, glove, and so on

  • We should also implement Convolution Neural Networks (CNN ) to get an idea about how a deep learning algorithm can help us

So in the revised approach, we will be focusing on word embedding techniques and the Deep Learning algorithm. We will be using Keras with the TensorFlow backend. Before implementation, let's understand the revised approach in detail.

Understanding key concepts for optimizing the approach

In this section, we will understand the revised approach in detail, so we know what steps we should implement. We are using Keras, a Deep Learning library that provides us with high-level APIs so we can implement CNN easily. The following steps are involved:

  • Importing the dependencies: In this step, we will be importing...