Book Image

Python Machine Learning Workbook for Beginners

By : AI Sciences
Book Image

Python Machine Learning Workbook for Beginners

By: AI Sciences

Overview of this book

<p>Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to better understand potential risks. The ever-expanding data, cost-effective data storage, and competitively priced powerful processing continue to drive the growth of ML. </p><p> </p><p>This is the best time you could enter the exciting machine learning universe. Industries are reinventing themselves constantly by developing more advanced data analysis models. These models analyze larger and more complex data than ever while delivering instantaneous and more accurate results on enormous scales. </p><p>In this backdrop, it is evident that hands-on practice is everything in machine learning. Tons of theory will amount to nothing if you don’t have enough hands-on practice. Textbooks and online classes mislead you into a false sense of mastery. The easy availability of learning resources tricks you and you become overconfident. But when you try to apply the theoretical concepts you have learned, you realize it’s not that simple. </p><p> </p><p>This is where projects play a crucial role in your learning journey. Projects are doubtless the best investment of your time. You’ll not only enjoy learning but you’ll also make quick progress. And unlike studying boring theoretical concepts, you’ll find that working on projects is easier to stay motivated. </p><p> </p><p>The projects in this book cover ten different interesting topics. Each project will help you refine your ML skills and apply them in the real world. These projects also present you with an opportunity to enrich your portfolio, making it simpler to find a great job, explore interesting career paths, and even negotiate a higher pay package. Overall, this learning-by-doing book will help you accomplish your machine learning career goals faster. </p><p> </p><p>The code bundle for this course is available at https://www.aispublishing.net/ai-sciences-book</p>
Table of Contents (15 chapters)
1
About the Author

2.7. Training the Model

The data is now ready for training a machine learning model. But first, we need to divide our data into the training and test sets. Using the training data, the naive Bayes algorithm will learn the relationship between the email text and the email label (spam or not) since both email text and corresponding labels are given in the training dataset.

Once the naive Bayes model is trained on the training set, the test set containing only email texts is passed as inputs to the model. The model then predicts which of the emails in the test set are spam. Predicted outputs for the test set are then compared with the actual label in the test data in order to determine the performance of the spam email detector naive Bayes model.

The following script divides the data into training and test sets.

Script 12:

1. from sklearn.model_selection import train_test_split

2. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

To train the machine...