Scroll down your social feed update list, and out of nowhere, there it is: an ad that seems freakishly relevant. Or you’re talking to some customer support agent, and they are responding with uncanny finesse to all your questions. Ever wonder how these systems just know what you need? That magic will be done through machine learning: the subset of Artificial Intelligence changing the way we interact with our technology today.
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In this article, we will cover the most important algorithms and basic techniques pertaining to machine learning. We will also discuss the need for data within UI and the ways it is used to train or build models. We will finally get a look at real-world examples of active machine learning, from image recognition to natural language processing.
In this article, we will cover the most important algorithms and basic techniques pertaining to machine learning. We will also discuss the need for data within UI and the ways it is used to train or build models. We will finally get a look at real-world examples of active machine learning, from image recognition to natural language processing.
The Supervised Learning Approach
The Supervised Learning Approach
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Imagine you need to teach a child all the kinds of animals. You show them a picture of a turtle and tell them, “This is a turtle.” Then you show them a picture of a dog and say, “This is a dog.” The child starts connecting the images with their labels. This idea is roughly what’s happening under supervised learning, a type of machine learning where the algorithm gets trained on labeled data.
In supervised learning, input-output pairs are fed into the algorithm that learns to map the inputs onto the proper outputs. This approach is applied to make predictions on new unseen data, given the patterns acquired from the training sets, and in solving various other problems such as image classification, speech recognition, and sentiment analysis.
Imagine you need to teach a child all the kinds of animals. You show them a picture of a turtle and tell them, “This is a turtle.” Then you show them a picture of a dog and say, “This is a dog.” The child starts connecting the images with their labels. This idea is roughly what’s happening under supervised learning, a type of machine learning where the algorithm gets trained on labeled data.
In supervised learning, input-output pairs are fed into the algorithm that learns to map the inputs onto the proper outputs. This approach is applied to make predictions on new unseen data, given the patterns acquired from the training sets, and in solving various other problems such as image classification, speech recognition, and sentiment analysis.
The Unsupervised Learning Approach
The Unsupervised Learning Approach
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Now, suppose you are given a set of images, but without any labels. Your task is to group similar images together. This belongs to the regime of unsupervised learning, where an algorithm learns patterns and relationships from data without knowledge of the labels.
This is useful when we don’t have labeled data or rather want to find the hidden structures of it. Some common techniques used in unsupervised learning include clustering, dimensionality reduction, and anomaly detection.
Now, suppose you are given a set of images, but without any labels. Your task is to group similar images together. This belongs to the regime of unsupervised learning, where an algorithm learns patterns and relationships from data without knowledge of the labels.
This is useful when we don’t have labeled data or rather want to find the hidden structures of it. Some common techniques used in unsupervised learning include clustering, dimensionality reduction, and anomaly detection.
The Reinforcement Learning Approach
The Reinforcement Learning Approach
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Imagine playing chess: You move a pawn, and the opponent answers. Your aim is to win the game by making the right moves. Similarly, in reinforcement learning, the algorithm learns to take actions in an environment so as to maximize a reward.
This is how an algorithm in reinforcement learning would work: it would interact with the environment and get feedback in the form of reward and penalties to adjust its actions until it could realize the desired result. Applications for this include game playing, robotics, and autonomous vehicles.
Imagine playing chess: You move a pawn, and the opponent answers. Your aim is to win the game by making the right moves. Similarly, in reinforcement learning, the algorithm learns to take actions in an environment so as to maximize a reward.
This is how an algorithm in reinforcement learning would work: it would interact with the environment and get feedback in the form of reward and penalties to adjust its actions until it could realize the desired result. Applications for this include game playing, robotics, and autonomous vehicles.
Data-Driven Approach
Data-Driven Approach
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Machine learning is all about the data. The performance of the model becomes a function of the quality and quantity of data used. On the other hand, UI data is used to train/build a model that can make predictions, classify, or generate text for objects.
It may be in the form of images, texts, audio, or sensor readings. This data is preprocessed, transformed, and then fed into the machine learning algorithm. This learns to identify patterns and relationships in the data and predict or act upon that learning.
Machine learning is all about the data. The performance of the model becomes a function of the quality and quantity of data used. On the other hand, UI data is used to train/build a model that can make predictions, classify, or generate text for objects.
It may be in the form of images, texts, audio, or sensor readings. This data is preprocessed, transformed, and then fed into the machine learning algorithm. This learns to identify patterns and relationships in the data and predict or act upon that learning.
Real-World Examples of Machine Learning
Real-World Examples of Machine Learning
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- Image Recognition: With an uncannily high accuracy, Google’s image recognition system identifies entities, people, and places from pictures. Applications of this technology range from Google Photos to self-driving cars and security systems.
- Natural Language Processing: This is how virtual assistants like Siri, Alexa, and Google Assistant know and understand your voice commands. They will also generate human-like text and summarize long documents.
- Recommendation Systems: With machine learning, Amazon and Netflix are able to recommend products and movies based on your browsing and purchase history.
The powerful technology of machine learning is very zealously developing means for better relations between humans and technology. It is only then that the sense of unlocking full potential in machine learning, to make such transformative solutions that might improve our lives, stems from understanding these core algorithms and techniques.
Stay tuned for more articles in this series where we dive deeper into the world of Machine Learning and explore its applications in UI.
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