Introduction to Machine Learning: exploring types, features and labels
Welcome to Nilesh’s Blog! Here, we’re diving into the basics of machine learning. We’ll start by understanding what features and labels are, and then we’ll explore the main types of machine learning. First things first, let’s talk about what machine learning is. Ready? Let’s get started!
In the Real world, humans can learn from their experience and improve their performance likewise computers can also learn from their experience or past data and improve performance without a piece of extra code with the help of machine learning.
What is the Feature and label of machine learning data?
As you see data is the main thing in machine learning. There can be many types of data on which our machine learning model can be trained. It can be an Image, CSV file, video etc. Most of the time we will be dealing with CSV files. we can divide data into columns and rows. we can have two types of columns first is input columns and there should be an output column. Columns by which, we are going to predict output is called feature and columns that specify Output is called Label.
What are the types of Machine learning?
Supervised Learning
In Supervised learning, we give examples with the correct answers to the computer. computer learns from that data and can make predictions on new data.
Think of Supervised Learning as teaching a computer with examples. It’s like showing it pictures and telling it what’s in each picture. The computer learns from these examples and can guess things when given new pictures.
Categories of Supervised Machine Learning
Classification
Classification algorithms are used to solve classification problems in which the output variable is categorical, such as “ Yes” or “No”, “Male” or “Female”, 0 or 1, etc.
some examples of Classification Algorithms,
- Support Vector Machine Algorithm
- Decision tree algorithm
- Logistic Regression Algorithm
Regression
Regression algorithms are used to solve problems in which the output variable or label is numerical or any contiguous value, such as 23, 32.22, 83 etc.
some examples of Regression Algorithms,
- Decision Tree Algorithm
- Simple Linear Regression Algorithm
- Multivariate Regression Algorithm
Unsupervised Learning
In Unsupervised Learning, we let the computer explore on its own without giving it labelled examples. It’s like letting the computer figure out patterns and groups in the data without a guide. Unlike supervised learning, there are no correct answers provided; the computer discovers the structure by itself.
Think of Unsupervised Learning as if you’re sorting a mixed-up collection of toys without anyone telling you what each toy is. The computer works similarly, finding patterns and relationships in the data without being explicitly told what to look for.
Categories of Unsupervised Machine Learning
Clustering
Clustering algorithms are like sorting things into groups based on their similarities. Imagine you have a bunch of different fruits, and the computer figures out which ones are similar and groups them. Some examples of Clustering Algorithms include:
- K-Means Algorithm
- Hierarchical Clustering Algorithm
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Algorithm
Association
Association algorithms find relationships between variables in large datasets. It’s like discovering that people who buy ice cream often buy sunglasses too. The computer identifies these associations without being explicitly told what to look for. Some examples of Association Algorithms include:
- Apriori Algorithm
- Eclat Algorithm
Reinforcement Learning
Reinforcement Learning is all about learning through interaction and feedback. Instead of providing examples, we let the computer play around and figure things out on its own by receiving rewards or penalties based on its actions.
Think of Reinforcement Learning as teaching a dog new tricks. The dog tries different things, and when it gets a treat, it learns to do more of those things. Similarly, the computer learns by getting rewards for good actions and learning what not to do when it gets penalties.
Categories of Reinforcement Learning
Positive Reinforcement Learning
Positive reinforcement learning specifies increasing the tendency for the required behavior would occur again by adding something. It enhances the strength of the behavior of the agent and positively impacts it.
Negative Reinforcement Learning
Negative reinforcement learning works exactly the opposite of positive RL. It increases the tendency for the specific behavior would occur again by avoiding the negative condition.
Conclusion
In conclusion, we have covered what are the features and labels and what are the types of machine learning with its categories and algorithms.
I hope this tutorial helped you to learn the basics of Machine learning.
I look forward to staying in touch and sharing more insights into machine learning. Happy coding!
Thanks for reading 🤩 and follow me on Twitter ❤️.
