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

Training the baseline model


In this section, we will perform actual training using the following ML algorithms. This step is time-consuming as it needs more computation power. We use 75% of the training dataset for actual training and 25% of the dataset for testing in order to measure the training accuracy.

You can find the code snippet in the following figure:

Figure 1.52: Code snippet for performing training

In the preceding code snippet, you can see that we performed the actual training operation using the fit() function from the scikit-learn library. This function uses the given parameter and trains the model by taking the input of the target data attribute and other feature columns.

Once you are done with this step, you'll see that our different ML algorithms generate different trained models. Now it's time to check how good our trained model is when it comes to prediction. There are certain techniques that we can use on 25% of the dataset. In the next section, we will understand these techniques.