Machine learning is a branch or subset of Artificial Intelligence and computer science which focuses on the use of data and algorithms to imitate the way that humans learn gradually improving its accuracy. Machine learning is a branch of computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering, and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions
Another way to say, AI is the ability of a computer or a robot to do tasks or make decisions like humans. With machine learning, we can make a system that can make decisions, and then we can say that the system has artificial intelligence. So how can we make a learner machine or learner system?
let’s think of a story:
Suppose a few talented students of a university have creat a robot. The specialty of the robot is that he can play good football. They call it Robo. If Robo got a football on her feet, he can pull the ball from one end of the field to the other and score a goal. Not only this, but if Robo plays two or three times with other players, he can remember their style of play. Let’s think now, FIFA host a friendly football match series between Robo and Ronaldo. And the rules of the game are the same as always. The deadline will be fixed. At that time, the one who can score the most goals will win. In this way, both will play a total of five matches. After playing five games, the player that wins the most matches will be the winner of the game.
The results of the five games are given below.
From this result table, The score of the match shows that Robo lost with huge intervals in the first match. However, they drew the next two games. But Robo won the last two games. So based on these results, we can easily claim that Robbie has been consistently performing well against Ronaldo. In other words, his play in each match has been of better quality than the previous match, only based on the experience of the earlier rounds. Now, based on this fact, we can say that Robo has intelligence, and this is what we call artificial intelligence. Because of this, we can say machine learning is a method of data analysis that automates analytical model building.
What Can We Do With Machine Learning?
Actually, by using machine learning we can do lots of powerful things such as:
- Fraud detection.
- Web search results.
- Real-time ads on web pages
- Credit scoring and next-best offers.
- Prediction of equipment failures.
- New pricing models.
- Network intrusion detection.
- Recommendation Engines
- Customer Segmentation
- Text Sentiment Analysis
- Predicting Customer Churn
- Pattern and image recognition.
- Email spam filtering.
- Financial Modeling and many more.
Types of machine learning algorithms:
Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data. Also, machine learning allows computers to find hidden insights without explicitly programming where to look.
Generally, there are three types of machine learning algorithms:
- Supervised Learning
- Unsupervised Learning.
- Reinforcement Learning.
Supervised learning algorithms train the machine learning models using labeled examples, such as an input where the desired output is known. So the learning algorithm receives a set of input along with the corresponding correct results, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. We mainly use supervised learning for applications where historical data predicts future events. Such as, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim. Through methods like classification, regression, prediction, and gradient boosting, supervised learning uses patterns to predict the label’s values on additional unlabeled data.
Unsupervised learning is used against data that has no historical labels. So the system doesn’t know the “right answer.” The algorithm must figure out what is being shown. So the goal is to explore the data and find some structure within it. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering, and singular value decomposition. Also, these algorithms are used to segment text topics, recommend items, and identify data outliers.
We mainly use Reinforcement learning for robotics, gaming, and navigation. The algorithm discovers which actions yield the most significant rewards through trial and error with reinforcement learning. This type of learning has three primary components: the agent (the learner or decision-maker), the environment (everything the agent interacts with), also actions (what the agent can do). So the objective is for the agent to choose steps that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal of reinforcement learning is to learn the best policy.