Saturday 30 March, 2024
What is Machine Learning?
Machine Learning (ML) is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy and performance on a specific task without being explicitly programmed.
Example:
Your email provider spam filter is a machine learning program that, given examples of spam emails and examples of regular emails, can learn to flag spam. The examples that the system uses to learn are called training set. Each training example is called a training instance. The part of the machine learning system that learns to make predictions is called model. Neural networks and random forest are example of models.
Why use Machine Learning?
Imagine if you had to write a spam filter using traditional programming techniques:
First you would need to examine what spam would look like, for example you might notices the use of words and phrases such as: "4U", "Free Money", "Claim free cash", "I'm the prince of Nigeria". You might spot other patterns such as the use of uncommon domain extension and sender's name.
You would write a detection algorithm for each of the patterns you have noticed, and your program would flag emails as spam if a number of these patterns were detected.
You would test your program and repeat steps 1 and 2 until it was good enough to launch.
After doing this you will quickly realise this is a very difficult problem, your program will become a long list of complex rules (pretty hard to maintain)
In contrast, a spam filter based on a machine learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually frequent patterns of words in the spam examples compared to the non-spam examples. The program is much shorter, easier to maintain, and most likely more accurate.
Conclusion
In summary, Machine Learning is a powerful tool that enables computers to learn from data and improve their performance on specific tasks without being explicitly programmed. By using algorithms and statistical models to identify patterns in data, machine learning can help solve complex problems that would be difficult or impossible to tackle using traditional programming techniques.
As the amount of data in the world continues to grow exponentially, the importance of machine learning will only continue to increase. From spam filters to self-driving cars, machine learning is already being used in a wide range of applications, and its potential is virtually limitless.
As a result, understanding the basics of machine learning is becoming increasingly important for professionals in many fields, not just those in computer science and AI. By learning about machine learning, you can position yourself to take advantage of the many opportunities this exciting field has to offer.
Acknowledgement: The ideas and concepts discussed in this blog post were inspired by the book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron, published by O'Reilly Media. This book provides a comprehensive guide to machine learning and has been an invaluable resource in understanding the different machine learning strategies and their applications. I highly recommend this book to anyone interested in diving deeper into the world of machine learning.