Machine Learning: Introduction

Imagine you are planning a budget trip to Siem Reap during Khmer New Year. You know that this period attracts many travelers, and in 2025, the Angkor Sangkranta celebration will further increase demand. From past experience, the cost of an overnight stay ranges between $15 to $30, but given the increased demand, will the minimum price remain at $15?

The likely answer is no. This type of reasoning—analyzing past trends, recognizing influencing factors, and making informed predictions—is at the core of Machine Learning. By training models on historical data, we can make intelligent forecasts, much like how businesses predict market trends or customer behavior.

Machine Learning vs. Data Science

Machine Learning (ML) is often confused with Data Science, but they serve different purposes. Data Science is a broad discipline that involves data collection, cleaning, visualization, and statistical analysis to derive insights. In contrast, Machine Learning is a specialized subset of Data Science that focuses on building algorithms capable of learning from data and making predictions. While a data scientist might analyze customer purchasing trends, an ML engineer would develop a recommendation system that personalizes product suggestions based on past behavior. Both fields complement each other—Data Science ensures high-quality data, while Machine Learning leverages that data for automation and predictive analytics.

Types of Machine Learning Models

Machine Learning can be categorized into two main approaches: Traditional (Statistical) Models and Neural Network Models.

1. Traditional (Statistical) Models: These include algorithms like Linear Regression, Decision Trees, and Support Vector Machines (SVMs). They work well with structured data and are widely used in areas such as fraud detection, healthcare, and financial forecasting. These models are interpretable, efficient, and require less computational power compared to deep learning.
2. Neural Network Models: Inspired by the human brain, these models use multiple layers of artificial neurons to process complex data. Deep Learning techniques such as Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for sequential data like speech and text have advanced AI applications significantly. However, they require large datasets and substantial computing resources for training.

Conclusion

Machine Learning is a powerful tool that enables computers to learn from data and make predictions. While Data Science focuses on understanding and interpreting data, ML automates decision-making through algorithms. Traditional statistical models are ideal for structured data with clear patterns, whereas Neural Networks excel in complex tasks like image and speech recognition. Whether predicting hotel prices during peak seasons or building AI-powered applications, Machine Learning continues to shape industries and drive innovation in our data-driven world.