Hola Junta! Welcome back to our Deep Dive series. Today, we’re delving into the fascinating world of Supervised Learning Algorithms.
Think about your first experience riding a bicycle — the wobbles, spills, and adjustments for that perfect ride. Now, let’s explore the psychology of it: the brain, like a machine learning expert, receives signals (metaphorical injuries) and adjusts to ensure future rides are safe.
Picture a scenario where you effortlessly mastered cycling from the start — no bumps, no need for bandages. Whether it’s learning from challenges or having a smooth initial experience, the brain, like a computer on a grand coding adventure, learns from feedback. It’s a blend of trial, error, and a touch of magic, all guided by the captivating partner we know as “MATHEMATICS.” So, get ready, because today we’re unveiling the fascinating secrets of supervised learning, where algorithms dance with experience on a journey toward perfection! 🚀
Welcome to another edition of “Deep Dive: Exploring AI and the Metaverse” by KodeinKGP! In this week’s instalment, we embark on a journey into the realm of supervised learning, a fundamental concept in the fascinating field of artificial intelligence.
Types of Machine Learning
- Supervised Learning:
Definition: Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, learning patterns to make predictions or decisions on new, unseen data.
Example: Teaching a model to recognize spam emails based on labeled examples of spam and non-spam emails.
- Unsupervised Learning:
Definition: Unsupervised learning involves training algorithms on unlabeled data to discover inherent patterns, structures, or relationships within the data.
Example: Clustering customer data based on purchasing behavior without pre-defined categories.
- Reinforcement Learning:
Definition: Reinforcement learning focuses on training agents to make sequences of decisions by rewarding or penalizing their actions based on the outcomes.
Example: Teaching a computer program to play and improve at a game by receiving positive or negative feedback for its moves.
Introduction
Supervised learning is a machine learning technique that is widely used in various fields such as finance, healthcare, marketing, and more. It is a form of machine learning in which the algorithm is trained on labeled data to make predictions or decisions based on the data inputs. In supervised learning, the algorithm learns a mapping between the input and output data. This mapping is learned from a labeled dataset, which consists of pairs of input and output data. The algorithm tries to learn the relationship between the input and output data so that it can make accurate predictions on new, unseen data.
Supervised learning is classified into two categories of algorithms:
- Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” , “disease” or “no disease”.
2. Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
Examples of Supervised Learning
Here are some examples of how supervised learning is used:
- Recommendation Systems: Recommendation systems use supervised learning to analyze patterns and relationships between input and output variables in labeled data, the algorithm learns to make predictions.
- Image and Speech Recognition: Supervised machine learning is used to locate, categorize and isolate objects from images or videos. The primary goal of image or object recognition is to identify the image accurately
- Fraud Detection: Supervised learning can be used to detect fraudulent transactions by training a model on historical transaction data where fraudulent transactions are labeled.
Understanding Supervised Learning: A Deep Dive
Introduction to Supervised Learning
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset. Labeled data consists of input-output pairs, meaning the algorithm learns to map input data to corresponding output labels. The goal is for the algorithm to generalize from the training data and make accurate predictions on new, unseen data.
Examples of Supervised Learning
Let’s delve into some relatable examples to grasp the essence of supervised learning:
1. Handwritten Digit Recognition:
- Input: Images of handwritten digits.
- Output: Corresponding labels (0–9).
- Application: Used in postal services to automatically recognize zip codes.
2. Spam Email Classification:
- Input: Email content and metadata.
- Output: Classified as spam or not.
- Application: Enhances email filtering systems.
3. Predicting Housing Prices:
- Input: Features like size, location, number of bedrooms, etc.
- Output: Predicted house prices.
- Application: Real estate market predictions.
Real-world Implementations
1. Image Classification with Convolutional Neural Networks (CNNs):
CNNs are widely used for image classification tasks. They learn hierarchical features from images and have been successful in various applications, such as identifying objects in photos.
2. Predicting Stock Prices:
Financial analysts use supervised learning to predict stock prices based on historical market data. Algorithms can analyze trends and patterns to provide insights for investment decisions.
3. Medical Diagnosis:
In healthcare, supervised learning is employed for disease diagnosis. For instance, predicting whether a patient has a certain medical condition based on their symptoms and test results.
Conclusion
Supervised learning allows collecting data and produces data output from previous experiences. It helps to optimize performance criteria with the help of experience. Supervised machine learning helps to solve various types of real-world computation problems. It performs classification and regression tasks. It allows estimating or mapping the result to a new sample. We have complete control over choosing the number of classes we want in the training data.
However, classifying big data can be challenging. Training for supervised learning needs a lot of computation time. So, it requires a lot of time. Supervised learning cannot handle all complex tasks in Machine Learning. Computation time is vast for supervised learning. It requires a labelled data set. It requires a training process.
Despite these challenges, supervised learning models can accurately predict and classify new data. Supervised learning has a wide range of applications, including classification, regression, and even more complex problems like image recognition and natural language processing.
Written by:
Bhavesh Pabnani, Pritish Saha
AI & Metaverse Team, KodeinKGP