Machine Learning Tutorial 2021. Beginners Guide To Machine Learning
What is supervised learning?
In the computer world, Machine Learning is a fancy term. Every newcomer wants to go through it. But due to a lack of proper resources, planning, and easy-to-understand tutorial, they lost interest after a while. But don’t worry, we got your back. We are going to elaborate as much as possible and we will also try to cover each topic of machine learning. So, let’s start.
What is Machine Learning?
Machine Learning is the science or art of programming computers so that the computers can learn from data.
A more engineering-oriented definition is given by Tom Mitchell in 1997.
“ A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
Knowingly or unknowingly we all are surrounded by machine learning applications. For example, Gmail. We all are using Gmail or any other email applications. They are using machine learning to classify the email that whether the email is spam or not. Based on the classification task, Gmail classify a particular email that belongs to the spam category or not.
The example that the system uses to learn is called the training set. Each training example is called a training instance. In the above example, task T is to flag spam or new email, experience E is the training data, and the performance measures P needs to be defined. The particular performance measure is called accuracy and is often used in classification tasks.
Machine Learning Examples
1. Classify and analyze image product
2. Detect tumors in brain scan
3. Automatically classify new articles
4. Automatically flag offensive comments on the discussion forum
5. Summarizing long documents automatically
6. Create a personal assistant or chatbot
7. Voice command
8. Detecting credit or debit card fraud
9. Forecasting your company’s revenue next year, based on performance metrics
10. Market analysts often use it to gain maximum profit
11. Representing a complex, high-dimensional dataset in a clear and insightful diagram
12. Product Recommendation
13. Building an intelligent bot for game
And this list will go on and on as we will witness more intense and dynamic machine learning applications in the upcoming future.
What are the types of Machine Learning?
Supervised Learning
Unsupervised Learning
Semisupervised Learning
Reinforcement Learning
Batch Learning
Online Learning
Instance-based Learning
Model-based Learning
What is Supervised Learning?
In Supervised Learning, we train or train the machine using data that is well labeled. Some data is already present with the correct responses. When we provide the machine with new labeled data the machine analyzes the data and produces the correct outcome of the labeled data based on the training set.
A typical supervised learning task is classification. The spam filter is a good example of this. It is trained with a lot of labeled data of emails. All these training informations helps the machine classify the new email instance to classify it as spam.
Another most used example is the regression problem. Here the main goal is to predict a target numeric value, such as the price of the house, cars, etc. The price of the house is predicted based on locations, the number of bedrooms, area, etc. This sort of task is called a regression task. You need to give as many as possible examples of houses with prices based on different locations, areas, etc. So that the machine will predict the price of the new instance house.
Some most important supervised machine learning algorithms :
K — Nearest Neighbors (KNN)
Linear Regression
Logistic Regression
Support vectors machine (SVMs)
Decision Tree and Random Forests
Neural networks2
We will elaborate on each term and algorithm in the further part. Check out more about machine learning below.
What is Unsupervised Learning?
What is Semi-supervised Learning?
What is Reinforcement Learning?
Thank You.
