Exploring Machine Learning: Basics and Applications
Machine learning is a rapidly growing field that continues to evolve and impact various aspects of our lives. In this article, we will discuss the basics of machine learning and why it is essential in today’s data-driven world.
Tom Mitchell, a prominent machine learning researcher, defines the concept as:
A computer program is said to learn from experience E with respect to some class of tasks T, and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Mitchell, Tom, and Machine Learning McGraw-Hill. “Edition.” (1997).
Consider a scenario where you want your computer to play tic-tac-toe (the task) and have played 100 games with it so far (the experience). The performance is measured by the number of games the computer has won. As you play more games with the computer, its experience and performance improve. This type of learning is often referred to as “Reinforcement Learning,” which we will explore later.
However, let’s first understand the basics and the need for machine learning.
Why do we need Machine Learning?
In a nutshell: Data is everywhere.
Machine learning is a subfield of the broader domain known as “Artificial Intelligence.” It involves using computer algorithms to learn from data, enabling machines to make decisions and predictions.
Although machine learning algorithms have existed for decades, recent advancements in data collection and computational power have allowed us to harness their potential to extract valuable insights. Machine learning algorithms can be categorized into three major types based on the nature of the data:
1. Supervised Learning
In supervised learning, algorithms are provided with labeled data. For instance, imagine a basket filled with red and green balls. When you show a ball to a camera connected to a computer, the computer must identify the ball’s color. To achieve this, we train the computer algorithm by presenting it with labeled examples, allowing it to learn the correct classifications. Once trained, the computer can accurately identify new, unlabeled examples. Applications of supervised learning include image classification, stock-price predictions, and speech recognition.
2. Unsupervised Learning
Unsupervised learning algorithms do not rely on labeled data. Instead, they are designed to detect patterns within the data. A common example is news classification, where an algorithm groups news articles based on their content, such as sports or finance. Google News operates on this principle.
3. Reinforcement Learning
Reinforcement learning is a reward-based technique, where algorithms learn through trial and error. If they perform well, they receive rewards; if not, they are penalized. This approach is frequently used in computer gaming, as seen with chess or Go (e.g., DeepMind’s AlphaGo). Autonomous driving also employs reinforcement learning.
Machine Learning Pipeline
The machine learning pipeline comprises the following primary components:
- Pre-Processing: This step involves cleaning the data by removing outliers, imputing missing values, reducing dimensions, and performing feature learning and engineering. Pre-processing is a crucial and extensive research area.
- Model Training: Depending on the data and problem, this component trains the machine learning model using different algorithms tailored to specific applications and data types. Data from the pre-processing stage is utilized for training.
- Model Validation: Typically, data is split into training and testing sets. While the training data is used to train the model, the testing data evaluates its performance. This process is called model validation.
- Deployment: Once the machine learning model passes the testing stage, it is deployed in real-world applications.
In this series of tutorials, we will delve into each component, exploring various research directions and providing detailed explanations.