Types of machine learning

Hello everyone, welcome to this tutorial by Blueguard. In this tutorial, you will learn about an interesting machine learning topic that is supervised vs unsupervised vs reinforcement learning. Let's discuss each of them in detail and understand when to use these algorithms, along with their applications.

There are a number of algorithms used in the field of machine learning to solve complex problems. Each of these algorithms can be classified into a certain category. The different types of machine learning algorithms are supervised learning, unsupervised learning and reinforcement learning.

Supervised Learning 

Now let's look at the definition of each of these learning techniques. Supervised learning uses labeled data to train machine learning models. Labeled data means that the output is already known to you. The model just needs to map the inputs to the outputs. 

An example of supervised learning can be to train a machine that identifies the image of an animal. Below you can see, we have a trained model that identifies the picture of a cat. 

Unsupervised Learning 

Unsupervised learning uses unlabeled data to train machines. Unlabeled data means there is no fixed output variable. The model learns from the data, discovers patterns and features in the data and returns the output. 

Here is an example of an unsupervised learning technique, that the model uses images of vehicles to classify cars, lorries and trucks. So the model learns by identifying parts of a vehicle, such as, length, width, front and rear cover, roof hoods, the type and number of wheels and Etc. Based on these features, the model classifies if the vehicle is a bus or a truck or a car. 

Reinforcement Learning 

Reinforcement learning trains a machine to take suitable actions and maximize reward in a particular situation. It uses an agent and an environment to produce actions rewards. The agent has a start and an end state, but there might be different paths for reaching the end state like a maze. 

In this learning technique, there is no predefined target variable. An example of reinforcement learning is to train a machine that can identify the shape of an object given a list of different objects such as square, triangle, rectangle or a circle. In the example shown in the picture above, the model tries to predict the shape of the object which is a square here. 

Machine Learning Algorithms 

Now let's look the different machine learning algorithms that come under these learning techniques. Some of the commonly used supervised learning algorithms are: 

  • Linear regression
  • Logistic regression
  • Support vector machines
  • K-nearest neighbors
  • Decision tree
  • Random forest and Naive Bayes. 

Examples of unsupervised learning algorithms are: 

  • K-Means clustering
  • Hierarchical clustering
  • DB scan
  • Principal component analysis 
  • And others.

Choosing the right algorithm depends on the type of problem you are trying to solve. Some of the important reinforcement learning algorithms are:

  • Q-Learning
  • Monte Carlo
  • SARSA and 
  • Deep Q-Network. 

Now, let's look at the approach in which these machine learning techniques work. Supervised learning takes labeled inputs and maps it to known outputs, which means you already know the target variable. 

Unsupervised learning finds patterns and understands the trends in the data to discover the output. So the model tries to label the data based on the features of the input data. 

While reinforcement learning follows trial an error method to get the desired solution. After accomplishing a task, the agent receives an award. An example could be to train a dog to catch the ball. If the dog learns to catch a ball, you give it a reward such as a biscuit. 

Training Approach to Different Types of Machine Learning 

Now, let's discuss the training process for each of these learning methods. Supervised learning methods need external supervision to train machine learning models and hence the name supervised. They need guidance and additional information to return the result. 

Unsupervised learning techniques do not need any supervision to train models. They learn on their own and predict the output. 

Similarly, reinforcement learning methods do not need any supervision to train machine learning models. And with that let's focus on the types of problems that can be solved using these three types of machine learning techniques. 

Problems that can be solved using different types of machine learning

So supervised learning is generally used for classification and regression problems. We'll see the examples shortly. And unsupervised learning is used for and association problems. While reinforcement learning is reward-based, so for every task or for every step completed, there will be a reward received by the agent. And if the task is not achieved correctly, there will be some penalty used. 

Applications of Different Types of Machine Learning 

Now, let's look at a few applications of supervised, unsupervised, and reinforcement learning. As we saw earlier, supervised learning are used to solve classification and regression problems. For example, you can predict the weather for a particular day based on humidity, precipitation, wind speed, and pressure values. You can use supervised learning algorithms to forecast sales for the next month or the next quarter for different products.

Similarly, you can use it for stock price analysis or identifying if a cancer cell is malignant or benign. 

Now talking about the applications of unsupervised learning, we have customer segmentation. So based on customer behavior, likes, dislikes and interests, you can segment and cluster similar customers into a group. Another example where unsupervised learning algorithms are used is customer churn analysis. 

 For  reinforcement learning. Reinforcement learning algorithms are widely used in the gaming industries to build games.

It is also used to train robots to perform human tasks. 

With this, we have come to the end of this post on supervised vs. unsupervised vs. reinforcement learning. I hope you liked this post. If you enjoyed reading this post, then please hit the follow button below to never miss an update. Thank you for reading and keep learning.

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