Understanding Supervised and Unsupervised Learning: A Beginners guide.
Tags: machinelearning, datascience, tutorial, programming
Published: 2024-06-15 06:49:22

Every day, we interact with [machine learning](https://www.coursera.org/articles/what-is-machine-learning) through smart assistants like Siri and Alexa, streaming services like Netflix and Spotify, search engines like Google, and our favorite social media platforms like Tiktok and Instagram. These technologies bring us closer, making our world smarter and more connected.

In this article, you will learn the fundamentals of machine learning--Supervised and unsupervised learning. We’ll discuss their types, real-world applications, advantages and disadvantages, and how they differ.

Machine Learning is a branch of Artificial Intelligence that enables computers to learn from and make predictions or decisions based on a given data without being programmed to do so.
In simpler terms, it is like teaching computers to learn and get better from experience, just like humans, but using lots of data and powerful [algorithms](https://www.simplilearn.com/tutorials/data-structure-tutorial/what-is-an-algorithm). 

Machine learning is widely categorized into two main types: 

1. 
Supervised Learning 
2. 
Unsupervised Learning

Each one uses different methods to train models depending on the kind of data.


##Supervised Learning
In supervised learning, the [model](https://www.coursera.org/articles/machine-learning-models) learns from a dataset that is labeled. This simply means that the model is taught using examples that have the correct answers. For instance, if you have a set of fruit images with their names labeled on them, the model learns to recognize the fruits from the labeled images. Later, when given new images, it can predict the fruit names based on what it has learned.


###Types of Supervised Learning

1.**Regression:** This is a type of supervised learning algorithm used to predict continuous values. 
Examples:

•House price predictions: Predicting the sales price of a house based on features like size, location, and number of bedrooms in the house.
•Forecasting Temperature: By forecasting future weather temperatures based on past weather records, a regression model can forecast the temperature for the next day or week.
•Predicting stock price: by analyzing past stock prices, trading volume, and other financial indicators, a regression model can attempt to predict the future price movements of a stock.


2.**Classification:** This is a type of supervised learning algorithm used to categorize data. It is like sorting objects into different groups based on their characteristics. For instance, you have a basket of fruits and you want to sort them into groups like apples, bananas, and oranges. The model learns that apples are red and round, while bananas are yellow and elongated, and then proceeds to group them accordingly. Similarly, in email spam detection, the model learns patterns in emails to know whether they are spam, based on the sender and other features of the mail.

###Applications of Supervised Learning
• Email Spam Filtering: The supervised learning algorithm is trained on a dataset of emails to identify and classify emails that are spam or non-spam by learning to recognize patterns and features that distinguish the two.

• Speech Recognition: The model is trained on audio recordings to convert spoken language into text. The recordings have their spoken words written down with them. This helps the model learn how people speak and change what they say into written text.

• Customer Churn Predictions: The model can predict which customers are likely to stop using a service by analyzing their past behavior.

• Predictive Maintenance: The models learn from past machines' data to spot signs that the equipment might need fixing soon.

###Supervised Learning Algorithms
Supervised learning algorithms teach computers to make predictions or decisions by learning from examples given to them. Here are some common examples:
•Linear Regression
•Logistic Regression
•Decision Trees
•Random Forests
•Support Vector Machines (SVM)
•k-Nearest Neighbors (k-NN)

###Advantages of Supervised Learning 
•It makes accurate predictions.
•The models use past data to predict what might happen in the future.
•The algorithms are easy to understand and interpret.
•You can easily spot when the model makes mistakes and correct them during the training process.
•The more labeled data you have, the better the model can learn and improve its accuracy.
•The algorithms can learn from large datasets, making them powerful tools for big data analysis.

###Disadvantages of Supervised Learning
•Supervised learning requires labeled data.
•Training a supervised learning model can be time-consuming.
•The model can only predict the specific tasks they were trained on.
•If there are errors in the labeled data, the model will learn the errors, causing the model to make inaccurate predictions.
•Some algorithms are complex and difficult to interpret.


##Unsupervised Learning
In Unsupervised learning, the model works with data that doesn't have any labels or correct answers. It figures out patterns and groups on its own.

For example, if you give the model a bunch of fruit pictures without telling it which fruit is which, the model will find similarities and differences among the pictures and group the fruits accordingly. It doesn’t know the names, but it can still organize them based on their characteristics. 

###Types of Unsupervised Learning
• Clustering: Clustering is a type of unsupervised learning that groups data points based on their similarities.
Examples:
-K-Means Clustering
-Hierarchical Clustering
-Independent Component Analysis
-Density-Based Spatial Clustering of Applications with Noise(DBSCAN)

• Dimensionality Reduction: This technique simplifies complex data while keeping important informations. Examples:
-Principal Component Analysis
-Autoencoders

• Association Rule Learning: This type of unsupervised learning finds patterns and relationships between items in data. Examples: 
-Apriori Algorithm
-Eclat Algorithm


###Applications of Unsupervised Learning
•Customer Segmentation: The algorithm looks at customer data e.g. purchase history, website activity, etc., and groups customers into different categories based on their behaviors and preferences.

•Imagine Compression: The algorithm will identify the most important parts of an image and compress it while retaining important information.

•Recommendation Systems: Unsupervised learning can suggest products, movies, or music based on user behavior.

•Market Basket Analysis: The algorithm analyzes shopping data to find products that are frequently bought together.

###Advantages of Unsupervised Learning
•It does not require labeled data.
•It can identify hidden patterns in data.
•It is useful in fraud detection.
•It is useful in exploratory data analysis.


###Disadvantages of Unsupervised Learning
•It is hard to determine the accuracy of the model without labels.
•The interpretations are difficult to understand.
•It requires the knowledge of experts to choose the right algorithm and interpret results.

###Differences between Supervised and Unsupervised Learning 

| Aspect                  | Supervised Learning                                    | Unsupervised Learning                              |
|-------------------------|--------------------------------------------------------|----------------------------------------------------|
| Definition              | Involves training a model with labeled data            | Involves training a model with unlabeled data      |
| Objective               | Makes accurate predictions                             | Finds hidden patterns or structures                |
| Examples of Algorithms  | Algorithms like Linear Regression, Decision Trees, SVM | Algorithms like k-Means Clustering, Hierarchical Clustering |
| Applications            | Used for tasks like spam detection, fraud detection    | Used for tasks like customer segmentation, image compression |

##Conclusion
Supervised and unsupervised learning are important techniques in machine learning, each with its own strengths and weaknesses. While supervised learning needs a lot of labeled data and can sometimes make mistakes, it is very accurate. On the other hand, Unsupervised learning does not need labeled data but its results can be hard to understand.

Knowing when to use each, method helps in solving different types of problems effectively, making the most out of machine learning.