Introduction
In this article, you will learn about the basics of machine learning. Generally, the goal of machine learning is to understand the structure of data and fit that data into models that can be understood and utilized by people.
What is Machine Learning
A subset of artificial intelligence (AI), is the area of computational science that focuses on analyzing and structures in data learning, decision making, clustering of human interaction. Machine learning is an extensively algorithm-driven study which makes computer/devices capable of learning based on their own previous experiences and improves the performance of tasks.
Why Machine Learning?
- The system provided by ML has the ability to automatically learn and improve from past experiences.
- It focuses on the development of computer programs that can access and use data.
Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning
Supervised learning is the process of teaching a model by fetching input data as well as correct output data. Think about a teacher who knows the correct answer and takes marks from a student based on the correctness of her response to a question. Supervised learning is used to create machine learning models of problem solvation.
- Regression
- Classification
Linear Regression
It’s a technique used for predicting, forecasting, actual finding between accurate data. The technique can be applied to examine the relationship between a share market business rating and budget. You could also use it to determine if there is a linear relationship between a particular ration.
Classification
It’s a technique used to predict a model's task of approximating the mapping function from the input values to discrete output values. The classifier, in this case, needs training data to understand how the given input values are related to the class. When the classifier is trained accurately, it can be used to detect particular data.
Supervised Learning Algorithms
- Support Vector Machines
- Linear Regression
- Logistic Regression
- Naive Bayes
- Decision Trees
- K-nearest neighbor algorithm
- Neural Networks
Conclusion
In this post, we have seen an introduction to Machine Learning (ML). I hope this article was useful for you... Thank You!