Hello folks, hope you all are doing great in this pandemic situation. Today I've came with an interesting topic to discuss about which is Machine Learning. In this we are going to see the following topic's.
Definition of Machine Learning.
Types of Machine Learning Algorithm's
Why Machine Learning matters ?
Applications of the Machine Learning
Machine Learning :
What is Machine Learning ?
Machine Learning is a field of Artificial Intelligence, which educates computers on how to perform the complex task's.
Machine Learning is one of the most exciting technologies. As it is evident from the name, it gives the computer that makes it more similar to humans i.e, The ability to learn.
Machine Learning mainly focuses on the development of the computer programs that can access the data & use that data to learn from it. The primary aim is to allow the computers to learn automatically without having a human intervention or assistance and adjust actions accordingly to it.
Types of Machine Learning Algorithm's
There are mainly 3 types of the algorithm's in the Machine Learning, those are :
Supervised Machine Learning Algorithm
Unsupervised Machine Learning Algorithm
Reinforcement Machine Learning Algorithm
Let's explore all these learning algorithm's step by step.
Supervised Machine Learning Algorithm :
The name itself indicates that there will be a presence of a supervisor or a teacher. In this type of learning, we train/teach the machine by using the dataset. In this the data we use is labelled i.e, the data is already tagged with the correct answers. By giving this data to the machine, the machine can utilize it to learn & then it can predict the correct future outcomes/events. In this, the learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
The following figure gives an insight about the Supervised Machine Learning Algorithm.
The supervised machine learning algorithm is further divided into two parts mainly as:
- Classification :
Classification refers to a predictive modeling problem where a class label is predicted for a given input data. In this, the output variable is a category or a group.
Ex. : Color is "red" or "yellow" or Mail is "spam" or "non-spam".
- Regression :
Regression analysis is most widely used statistical method. It is a form of predictive modeling technique & also used for the analyzing of the data.
Regression analysis consists of a set of machine learning methods that allow us to predict a continuous outcome variable (y) based on the value of one or multiple predictor variables (x).
In this problem the output variable is the real value.
Ex. : "Dollars" or "Weight".
Unsupervised Machine Learning Algorithm :
Unsupervised learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. As this statement clearly states that there is lack of a supervisor or a teacher which helps to classify the data. Here we provide the unlabeled data to the algorithm.
In this type of the learning, systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Unsupervised learning classified into two categories of algorithms:
- Clustering :
Clustering involves automatically discovering natural grouping in data. In this type, we discovers the group of data based on the similarities.
Ex. : "Customer's grouping is based on their buying habits."
- Association :
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. In this type of learning, we discovers rules to describe the large data.
Ex. : "If a person buys biscuits then he also tends to buy milk."
Reinforcement Machine Learning Algorithm :
Reinforcement Machine Learning Algorithm is a learning method that interacts with its environment by producing actions and discovers errors or rewards. It is all about making decisions sequentially.
Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.
This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal. In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions. The best example of this is the "WUMPUS" problem.
Why Machine Learning matters ?
Machine learning matters most because as models those are exposed to new data, they are able to adapt independently. Also as we seen in the above, the machine learns from previous computations to produce reliable, repeatable decisions and results. If any unseen data is given to the algorithm of the machine learning, it makes predictions using the models that built.
The power behind machine learning’s self-identification and analysis of new patterns, lies in the complex and powerful ‘pattern recognition’ algorithms that guide them in where to look for what. As most of the things in our daily use are somewhere based on the machine learning. Also we can see that machine learning is applied in very vast area's like Natural Language Processing(NLP), Spam filtering, etc. Thus, the demand for machine learning programmers who have extensive knowledge on working with complex mathematical calculations and applying them to big data and AI is growing year after year.
Applications of the Machine Learning :
Netflix, Amazon uses it for their recommendation system.
Used to determine the price & wait time of Uber/Ola.
Self-driving Car
Computer vision
Spam filters
Recommendation system
Stock Trading
Web search &
Many many more.....
Conclusion :
Here by we have seen all the basic aspects of the machine learning like the definition of machine learning, the types of the machine learning algorithms then why machine learning is most important & applications of the machine learning. Here by I conclude this topic of brief discussion on machine learning. Thank you.