A Brief Introduction to Fundamentals of Machine Learning

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Data adventure, which started with data mining concept, has been in a continuous development with introducing different algorithms. There are many applicable algorithms in AI. Besides, AI is actively used in marketing, health, agriculture, space, and autonomous vehicle production for now. Data mining is divided into different models according to fields in which it is used. These models can be grouped under four main headings as a value estimation model, database clustering model, link analysis, and difference deviations. It appears in many functions, such as fraud detection of customer or estimating how much profit can be made from customer. In this post, I will short introduction to machine learning models. I hope it’s helpful, have a nice reading!

We can classify machine learning systems according to amount and type of supervision they get during training. It’s issue that determines how data will learn so that it can make predictions. There are four major categories:

Yes, let’s take a brief look at the learning models mentioned in definition. You may encounter concepts such as supervised, unsupervised and semi-supervised, and reinforcement learning. These concepts are the learning model that decides what and how model will learn from the data.

Supervised learning algorithm learns from labeled training data, helping you predict outcomes for unpredictable data. Successfully building right learning model requires a team of highly skilled data scientists, time, and technical expertise. Researcher must reconstruct models to ensure that result stays true even when given inputs change. Classificationand Regressionbelong to this learning group.

Unsupervised learningallows machines to learn by themselves. This type of machine learning gives AI applications ability to learn and find hidden patterns in large datasets without human supervision. Unsupervised learning is recognized as one of key techniques to achieve artificial general intelligence. Also, it refers to use of artificial intelligence (AI) algorithms to identify patterns in datasets containing unclassified or unlabeled data points.

In supervised learning, there are predetermined classes. However, this is not case with unsupervised learning. It separates results into different clusters by looking for similarities and relationships among the attributes in existing data. Clustering is unsupervised learning structure.

One of the best ways to tell difference between supervised and unsupervised learning is to look at how to learn to play chess. Yes, playing chess! In this direction, first option is to learn the game from a chess master. A tutorial can teach you how to play game of chess by explaining basic rules, what each piece does, and more. Once you know rules of the game and each piece, you can practice by playing against the trainer. When you hear chess example, I can hear you say that it would make more sense to learn from someone who knows!

In short, in supervised learning, instructor helps you learn by giving you instructions, it has been determined that it will help you in advance. However, in unsupervised learning, you only learn by watching. Sometimes you can understand chess moves, and sometimes you don’t. This is one of shortcomings of unsupervised learning.

Many computational techniques and algorithms are used in supervised learning process. The following factors are considered when choosing a supervised machine learning algorithm:

While supervised learning can offer business benefits, such as deep data insights and advanced automation, you may encounter challenges when building sustainable supervised learning models. The following are some of these challenges:

Semi-supervised learning is a class of supervised learning tasks and techniques that also enable the use of unlabeled data for training. Generally, number of unlabeled data is greater than that of labeled data. Semi-supervised learning is between unsupervised learning (with no labeled training data) and supervised learning (with fully labeled training data). Many machine-learning researchers have found that unlabeled data, combined with a small amount of labeled data, can significantly improve learning accuracy. In other words, it is a learning technique created by using a large amount of unlabeled data and a small amount of labeled data.

For example, you have a large amount of data; you chose unsupervised as the learning method, but you want to get better results by training data with a small portion. You realize that small portion you allocate with supervised learning. This is called semi-supervised learning in short! A common example of a semi-supervised learning application is a text document classifier. This is the type of situation where semi-supervised learning is ideal, as it will be nearly impossible to find large volumes of labeled text documents.

It’s a learning technique in which player or “Agent” intervenes when necessary. Reinforcement Learning is all about gamifying the learning process. In this type of (unsupervised) machine learning, a reward-punishment method is used to teach an AI system. Machine is rewarded if it makes right move, and penalized if it makes a mistake. The goal here is to maximize total reward. That is, person who wrote this algorithm assigns positive values to desired actions and negative values to undesired actions to encourage machine. Thus, machine is programmed to maximize reward in a long-term way to reach an optimum solution. In this way, machine learns to do right thing by learning from its own mistakes, requiring no human supervision. It’s that simple! Currently, it is frequently used in areas such as digital game’s enemy moves, board game rival player, web user interfaces, autonomous vehicles, personalized product recommendation system, chatbots, robotics, etc.

Reinforcement learning is actually based on trial-and-error, logic of reward-punishment. Agent interacts with environment and decides or choices in final. There are 3 different reinforcement learning algorithms. These types will be covered in next posts.

We have talked about basic concepts of machine learning!

See you in next post!