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Understanding AI unsupervised learning: The science behind the magic

Introduction:

Artificial Intelligence (AI) has been fascinating us since its inception. It has progressed from its early stages of supervised and unsupervised learning to the most advanced form of AI, known as reinforcement learning. In this article, we will focus on the concept of unsupervised learning that is the root of Artificial Intelligence.

Understand Unsupervised Learning:

Supervised learning is a process where machines are trained on the basis of labelled data. For instance, when we teach children how to recognize objects, we show them pictures and instruct them that this particular object is called a “cat” or a “dog.” Similarly, we train machines by showing them with labelled data that this image is a “cat,” “dog,” “car,” or “ball.” The machine learns explicitly from what we teach and is then able to recognize new instances based on prior learning.

Unsupervised learning, on the other hand, does not use training with labelled data. As we can gather from its name, it works without external supervision. It is often called self-organized learning, where the machine learns the patterns, clusters, and relationships present in the data without any external guidance.

The process of unsupervised learning is helpful when we are unsure of what features we need to consider to analyze the data. It can help us to form clusters or groups of similar data instances. Unsupervised learning can also help us in discovering hidden patterns that are previously undiscovered.

Types of Unsupervised Learning:

Unsupervised learning can be classified into two main categories:

1. Clustering

2. Dimensionality Reduction

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Clustering:

Clustering looks for similarities between data instances and tries to form groups or categories that tend to have similar characteristics. In simple terms, clustering groups similar data instances based on their similarities and group them together.

K-means clustering is one of the most popularly used approaches. It is an iterative process where the algorithm assigns each data instance to a cluster based on the distance between the centroid of the cluster and the data instance. The algorithm continues until there is no further change in the clusters formed.

Dimensionality Reduction:

This approach uses techniques that aim to reduce the number of “features” or “variables” used in the computation of data instances. By reducing the number of features, we can represent the data instances in a more simplified manner. This simplified representation can help us in understanding the underlying patterns and relationships between instances.

Principal Component Analysis (PCA) is one of the commonly used techniques for dimensionality reduction. It includes mapping the high dimensional data instances to a lower dimensional space while retaining the maximum amount of variance in the data.

Real-life Applications of Unsupervised Learning:

Unsupervised learning has a wide range of applications in our daily lives. Let’s take a few examples below.

1. Customer Segmentation:

In commerce, it is essential to segment your customers into different groups based on their shopping history, preferences, and behaviours. The data scientists can use clustering algorithms to group customers based on their similar shopping habits.

For instance, a retailer might segment the data into groups of frequent, monthly and infrequent shoppers. This data can then be used to design targeted marketing campaigns and personalized recommendations to encourage the customers to shop more.

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2. Speech Recognition:

Speech recognition is a well-known application of unsupervised learning. Speech and audio processing technology use clustering algorithms to classify and categorize audio files or phoneme sounds. These algorithms then aid supervised models in recognizing different sounds in processing human speech.

3. Anomaly Detection:

Anomaly detection is commonly used in fraud detection and cyber-security. Unsupervised learning helps detect unusual patterns or behaviors in the vast data stores. These suspicious instances can be further verified through supervised learning models, ensuring the anomaly patterns are fraud cases and not simple outliers.

Conclusion:

Unsupervised learning has proven to be an impactful tool in the field of Artificial Intelligence. It has a wide range of applications in different domains, including finance, healthcare, commerce, speech recognition, and manufacturing. It can be used to identify hidden patterns and relationships in vast data stores.

Moreover, Unsupervised learning is a primary technique for big data processing with real-time processing capabilities as it doesn’t rely on labelled data and can make clusters for better analysis and processing. Although demonstrating a more complex nature, with the rise of deep learning models, unsupervised learning can provide similar power with some adjustments and techniques.

As AI technologies are becoming more common in day-to-day applications, we can expect to see increased usage of unsupervised learning in the future across several industries and verticals.

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