This fact has made many to wonder if a machine can learn from the experiences or data it has gathered from the past. So, what actually is Machine Learning?
Machine Learning is clearly a subsection of artificial intelligence that deals with the development of algorithms which enable a computer to learn on its own using data and experiences gathered from the past. The term Machine Learning was first used in 1959.
When a computer or machine is able to learn from its past experiences, it can help improve its functionality and performances. Even when it is not programmed to do so, it can easily predict things. To do this, it uses available historical data, which many in this field call training data. Based on this training data, machine learning algorithms create a mathematical model which enable the machine to make predictions and decisions on its own without being monitored or programmed.
At this point, it is comfortable to say that machine learning is a meeting point for both computer science and statistics with the goal of producing predictive models. Machine learning practically uses the algorithms it gets from historical or training data.
This means that if we want to enhance the performance of a computer or machine, we need to expose it to more information or more data so that it can have more experiences to learn from.
Understanding How Machine Learning Works
Whenever a Machine Learning system receives new information or data, it uses the historical data it has gathered to construct prediction modela so that it can predict the output for the new data. The level of accuracy of the decision made or output prediction is based largely on the quality and volume of data available to the machine. A huge amount of data will enable the machine to construct better decison-making models which will in turn help the machine to accurately predict the output.
For example, let say you have a difficult or complicated problem which will require you to perform some predictions. What do you do? Do you need to write a code for it? No! You do not need to.
All you need to do is train the computer system or machine by feeding it data pertaining to that problem to generate algorithms. These algorithms will help the machine to construct logical, mathematical models to analyze the problem so as to predict the output so as to give an accurate answer to our problem.
Do you understand the concept behind Machine Learning now? I believe you do. Lets take it easy and simple. As you progress, in the course of this tutorial, you will grasp everything in no time.
So far, you have seen how easy. Machine Learning has changed the way we approach solving problems.
The illustration below explains how Machine Learning algorithm works.
Characteristics of Machine Learning
- In Machine Learning, data is used to identify a variety of patterns in a given dataset.
- Machines are able to learn from past data and experiences which aids in improving efficiency, functionality and performance. All this is done automatically without the interference of any human.
- Machine Learning is a technology that is data-driven.
- Machine Learning depends on huge amount of data just like data mining.
Why Do We Need Machine Learning
In today's technology driven world, the need to use Machine Learning is glaring in each passing day. This is because we need Machine Learning to help with executing complicated and difficult tasks that can not be easily done by a human.
Are humans not capable of solving complex problems? As humans, we are limited in the way we can compute high volumes of data manually. As a result, we need machines or computer systems with the influence of Machine Learning to make executing and solving such complex problems very easy for us.
As humans, our role in Machine Learning Systems is to train the machine learning algorithms. We provide them the necessary volume of data so that they can explore and analyze the data. Such exploration of data will enable the machines to build mathematical or logical modes to predict output automatically when it comes across new data or variables. This process saves a lot of times and cost.
We are currently experiencing the benefits of Machine Learning In various areas where it is used. It is used in social media when you are given friend suggestions from Facebook. Some of us have experienced been driven by self-driving cars. Face recognition and fingerprint detection has become the order of the day. Many websites use machine learning to detect and analyze a user interests based on the user's past browsing experience in order to accurately recommend a product that might interest the user.
Based on these benefits we can summarize the reasons why we need Machine Learning:
The analysis and solving of complex problems which can not be solved simply by humans.
It aids in the quick prediction and decision making process in various fields.
It generate patterns in a given dataset, discovers hidden patterns which can be used to extract useful information.
Categories of Machine learning
Generally, machine learning processes can be categorized in to three groups.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised Learning
Supervised learning is that type of machine learning which uses labelled data provided by humans. The labelled data is used to train the machine or computer system. The system itself builds a mathematical model using the labelled data supplied to it, analyzing and understanding every part of the data. Once the training and processing are completely done, the system is tested with new data to predict the output. This is done by supplying a new sample data into the system to check if it is acurate in the prediction of output or not.
This method of machine learning aims at matching input data with the output data under strict monitoring or supervision. It is just like a chef teaching someone how to cook. The chef supplies all the ingredients to see if his student will replicate the resulting meal he taught the student.
Supervised Learning is further divided into two classes of algorithms.
Classification
Regression
Unsupervised Learning
This is simply machine learning without any method of supervision. The machine is trained using sets of data that are not labelled, grouped or classified. The algorithms acts on that data without supervision.
This method of Machine learning aims at restructuring the input data into new groups of data or objects with similar patterns. A fixed output or result is not expected from unsupervised learning. What the machine does is to look for relevant data from the massive volume of data supplied. It is also grouped into two classes of algorithms.
Clustering
Association
Reinforcement Learning
In this method of Machine Learning, the learning agent gets rewarded for every right action it takes or gets penalized for taking a wrong action. Thus, it is a feedback-dependent learning method.
The agent unconsciously learns from the feedbacks, positive or negatively, thereby increasing its level of performance. The role of the agent is to interact with the environment, exploring and making choices. This method of Machine learning depends on the agent to rewarded with more points, improving its performiance.
All these methods of learning will be discussed in later articles of this blog.
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