Machine learning (ML) is a subset of artificial intelligence (AI). It allows computers and systems to learn from experience and use this experience to execute independent tasks. They can work with different types of inputs, including digital data sets and verbal human instructions. While the learning process differs from human learning, the goal is the same.
Supervised Learning
Supervised learning involves training the machine with labeled data (data that already includes the correct answers). The system learns the relationship between inputs and outputs, then uses this understanding to make predictions.
Common supervised ML algorithms are:
Linear regression
Logistic regression
Decision trees
Support vector machines (SVM)
k-Nearest Neighbors (k-NN)
Neural network
Example: The model receives a dataset that includes thousands of email messages marked as "spam" or "not spam." The machine analyzes the content of each message and learns to identify patterns (e.g., certain keywords, sender addresses, formatting styles). After training, it can flag new incoming messages as spam or not spam.
Unsupervised Learning
Unsupervised learning uses unlabeled data. The machine explores the structure of the data to find patterns without assistance. This method helps identify hidden relationships that humans might not see immediately.
Common unsupervised ML algorithms are:
k-Means clustering
Hierarchical clustering
Principal Component Analysis (PCA)
Autoencoders
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Example: A marketing team might use unsupervised learning to analyze customer behavior. Without knowing anything about buyers, the system might cluster them into groups based on purchase history or location. These clusters help businesses create personalized offers for different target audience segments.
Despite many impressive capabilities, machine learning comes with several challenges.
Data Quality: ML models depend heavily on accurate datasets. If the data contains errors, the model will learn those mistakes and produce flawed results.
Hallucinations: Machine learning models often struggle when they see something outside the scope of the data they received in the past. This could lead to "made-up" answers.
Resources: These models require significant computing power, which can be costly and energy-intensive.
Ethical concerns: Concerns around data privacy and transparency continue to grow. Meanwhile, humans are not ready for machines to make important decisions like whether or not to have surgery.
