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

Exploring problems with the existing approach


In this section, we will be discussing the problems of the existing approach. There are mainly three errors we could have possibly committed, which are listed as follows:

  • Alignment

  • Smoothing

  • Trying a different ML algorithm

Let's discuss each of the points one by one.

Alignment

As we have seen in the graph, our actual price and predicted prices are not aligned with each other. This becomes a problem. We need to perform alignment on the price of the stocks. We need to consider the average value of our dataset, and based on that, we will generate the alignment. You can understand more about alignment in upcoming section called Alignment-based approach.

Smoothing

The second problem I feel we have with our first model is that we haven't applied any smoothing techniques. So for our model, we need to apply smoothing techniques as well. We will be using the Exponentially Weighted Moving Average (EWMA) technique for smoothing. This technique is used to adjust...