Types of Machine Learning, You Should Know

Do you wanna know What are the Types of Machine Learning? If yes, then this blog is just for you. Here I will discuss the various types of Machine Learning in detail. So, give your few minutes to this article in order to get all the details regarding the Types of Machine Learning.

Hello, & Welcome!

In this blog, I am gonna tell you-

1. What is Machine Learning?
2. Types of Machine Learning.

Firstly, I would like to start with-

What is Machine Learning?

As the name suggests, Machine Learning allows machines to learn and make decisions smartly. In Machine Learning, machines can learn from the data provided or their own experience. It depends upon the type of machine learning.

Machine Learning is growing day by day. As the data is increasing, the usage of machine learning tools is also increasing. Nowadays every industry is using machine learning, no matter it is the Medical industry or business industry. Medical Industry is using machine learning to predict different symptoms of the disease. Whereas the business industry is using machine learning to increase their sale.

Types of Machine Learning-

There are four types of Machine Learning-

1. Supervised Machine Learning.
2. Unsupervised Machine Learning.
3. Semisupervised Learning.
4. Reinforcement Learning.

1. Supervised Machine Learning.

As the name suggests, supervised learning means it works under supervision. In supervised learning, learning is performed under the supervision of the teacher. Here the teacher means the data you provided at the time of training.

Therefore, in supervised learning data is provided at the training phase. Based on this data, the model predicts the outcome.

Supervised learning is the same as teaching the child to walk, where parents give instructions to the child how to walk. Based on the instructions, the child learns walking. Similarly, supervised learning performs based on the data or instruction given at the training phase.

Supervised learning is also known as Classification, which classifies the data into separate classes.

For Example-

Suppose you have to classify between dog and cat. So for training the model, different types of images of dogs and cats are given. And instructions with a label that this image is of dog and this image is of a cat. The model will learn from those images. So when we will give a new image of the dog to the model, so the model has to classify whether its dog or cat.

So, in supervised learning, training data is mandatory in order to predict or classify the result.

The supervised learning problem is of two types-

1. Classification- The classification problem is when there is a category like “dog” or “cat”. So here, supervised learning has to classify the data in between these categories.
2. Regression- In regression problem model has to predict the output in real value, like the salary of an employee. This prediction is based on the training data.

Supervised Learning Algorithms-

Supervised learning or classification has following algorithms including linear as well as nonlinear models-

1. Logistic Regression.
2. K-Nearest Neighbors(K-NN)
3. Support Vector Machine(SVM)
4. Kernel SVM.
5. Naive Bayes
6. Decision Tree Classification.
7. Random Forest Classification

2. Unsupervised Machine Learning–

As the name suggests, unsupervised learning means it doesn’t have a need for any supervision. That means, in unsupervised learning, there is no teacher to train the model. The model has to learn by itself.

Unlabeled data will be provided in unsupervised learning, which means there is no labeling on the data in unsupervised learning.

Unsupervised learning is also known as Clustering. It works on unlabeled data and finds itself different patterns that help them to predict the outcome.

For example,

Suppose, I am taking the same example of supervised learning. Here in unsupervised learning, you provide various images of dogs and cats to the model, but without any label. Here, you don’t tell the model that this image belongs to the cat or dog. So in unsupervised learning, algorithms try to separate images of dogs from cats based on different parameters without any supervision.

Unsupervised Learning Algorithms-

Unsupervised learning has the following algorithms-

1. K-Means Clustering
2. Hierarchical Clustering.
3. Probabilistic Clustering

3. Semisupervised Learning.

Semisupervised learning is a mixture of supervised learning and unsupervised learning. That means, in semisupervised learning, some of the data is labeled and most of the data is not labeled.

You can use semisupervised learning to label data. Suppose you have some labeled data and the rest of the data is not labeled. So you can use semisupervised learning to label the rest of the data with the help of labeled data.

4. Reinforcement Learning.

Reinforcement Learning is a kind of hit and trial learning. The model learns from its own mistakes and success. If the model predicts correctly, so it will be rewarded and if it predicts the wrong answer, it will be penalized. So on that penalties and rewards, the model learns and predicts the next outcome.

Reinforcement learning is mostly based on behavior. That means it totally depends upon the feedback received. In the beginning, reinforcement learning makes a lot of mistakes, and after receiving negative feedback, it improves performance.

You can take an example of Video games, where reinforcement learning is used to find a suitable path. In the video, games player has to find the best path, so first, it starts and based on the reward it received, improves its performance. When it takes a correct step, it gets a reward and when it takes a wrong step, the reward is subtracted. Another example of reinforcement learning is Self-driven cars.

Reinforcement Learning Algorithms-

Reinforcement learning has the following algorithms-

1. Model-Free Reinforcement Learning.
   1. Policy Optimization.
   2. Q-Learning
2. Model-Based Reinforcement Learning.
   1. Learn the Model
   2. Given the Model.

I hope, now you have a clear idea about different types of machine learning.

Enjoy Machine Learning

All the Best!

Are you ML Beginner and confused, from where to start ML, then read my BLOG – How do I learn Machine Learning?

If you are looking for Machine Learning Algorithms, then read my Blog – Top 5 Machine Learning Algorithm.

If you are wondering about Machine Learning, read this Blog- What is Machine Learning?

Wanna Learn Basics of ML?. Learn Here.

Thank YOU!

Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford