Machine Learning can be divided into four main types: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning.
In this blog, you will learn about Supervised Learning and Unsupervised Learning in the simplest and most concise way possible, along with examples and applications of each.
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What is Supervised learning ?
Supervised learning is a machine learning technique where the algorithm is trained on a labeled dataset.
Here, labeled means that the input data is paired with a corresponding correct output, similar to a key-value pair format.
Example of Supervised Learning
One common example is predicting house prices based on house sizes. The dataset includes house sizes (input) paired with their prices (output). The model learns how the size of the house affects its price and then predicts prices for unseen data.
Applications of Supervised Learning
Sentiment Analysis: Analyzing text data (e.g., reviews, tweets) and classifying them as negative, positive, or neutral.
Stock Price Prediction: Predicting stock prices using historical data.
Recommendation Systems: Generating movie or product recommendations based on user preferences.
Medical Analysis: Using medical images to classify conditions such as tumors, fractures, or infections. Blood test data can also be analyzed to predict diseases.
What is Unsupervised learning ?
Unsupervised learning is a machine learning technique where the model is trained on unlabeled data. The primary goal is to discover hidden patterns or structures within the data without predefined labels.
Anomaly Detection: Identifying unusual patterns or outliers in data, such as fraudulent transactions, without labeling them as fraud beforehand.
Targeted Marketing: Clustering customers based on their purchasing behavior, demographics, or browsing habits to enable personalized marketing strategies.
Association Rule Learning: Finding frequent patterns in transactional data. For example, identifying that customers who buy bread are also likely to buy butter.
By understanding these two core techniques, you can start exploring how machine learning algorithms can simplify complex tasks and uncover valuable insights. Whether you're analyzing customer behavior or making medical predictions, s*upervised and unsupervised learning serve as the foundation for numerous real-world applications.*
The area of machine learning that interests the most to me is using ml model to predict stock prices and testing various trading systems, strategies and forecasting methods. If that's something that interest you too, stay tuned for future blogs.
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