What is Machine Learning?
Expert System defines machine learning as a form that artificial intelligence (AI) that allows systems to learn and improve their knowledge as they experience more without having to be specifically programmed to do so.
The idea here is that the program can access information for its own use. If the system is utilizing machine learning, it will eventually be able to make adjustments. As it acquires new information without any help from human beings.
If you’ve ever wanted to know more about machine learning, what it is and how it works, then you’ve definitely come to the right place.
Machine Learning Algorithms
Obviously, programmers can’t just hand a bunch of information to a system without establishing some ground rules. The system has to know what it’s looking for as well as what its next course of action is after discovery.
To give the system the instructions it needs to process data and learn, machine learning engineers use algorithms.
What’s an Algorithm?
As Tech Target explains:
“An algorithm is a procedure or formula for solving a problem, based on conducting a sequence of specified actions.”
In other words, for the program to understand what it’s seeing and respond accordingly, it needs an algorithm, or a special set of instructions, to provide it with the necessary information.
Algorithm Types
Fortunately for newbies, machine learning algorithms are often grouped into categories depending on how they affect the program. This is by no means an exhaustive list, but here’s a quick list of 5 algorithm type examples that machine learning engineers often come across.
1. Instance-Based
This algorithm is sometimes also referred to as a memory-based method.
It doesn’t generalize data per se, but instead draws from the instances stored in its memory during training and compares it to the new problem instances it comes across.
2. Decision Tree
This is a predictive model or a model that seeks to predict future outcomes.
Decision tree algorithms make observations about data and then use the information gained to reach conclusions following the “branches” of the tree.
Depending on whether the variable comes from a continuous value set (it can retain value over a continuous range) or a discrete value set (restricted to certain values), decision trees can be further broken down into classification trees and regression trees.
3. Regularization
Regularization is used to correct scenarios where the model is easily handling data during the training stage but is having performance issues when introduced to new data and scenarios. In other words, it isn’t learning.
Following the rationale that these problems are likely the result of the algorithm being asked to do too much, regularization seeks to correct the issue by assigning penalties to more complex models in an effort to encourage simpler models.
4. Ensemble
This category uses identifiers to make predictions by classifying incoming data through weighted votes provided by multiple models.
If you want a prototypical example of an ensemble method, you’ll want to look into the Bayesian model.
5. Association Rule Learning
This is a learning method that relies on established rules to determine its course of action. These algorithms will comb through massive databases and find interesting relationships between the variables.
Machine Learning Methods
Just because these algorithms are available, that doesn’t mean that engineers merely have to enter them and step back while the magic happens.
These algorithms are used in service to a larger strategy called a machine learning method.
Generally speaking, machine learning methods are divided into two categories:
- Supervised
- Unsupervised
However there are some, like James Le in “The 10 Algorithms Machine Learning Engineers Need to Know“, who believe that reinforcement learning adds a third category.
Supervised Learning
With supervised learning, the program is given an end result and a set of training data. The program is asked to make predictions and receive correction when until the model’s accuracy is improved.
Unsupervised Learning
The end result is unknown and the input data isn’t labeled as such to the program. With unsupervised learning, the model examines the data and figures out what structures are present in it. This can be used when programs are being asked to find similarities in data.
Semi-Supervised Learning
With this learning method, some of the data is labeled and some of it is not. The goal with this approach is for the model to learn how to recognize the structures of the data for organization purposes while also being able to make predictions with it.
Reinforcement Learning
Reinforcement learning takes a completely different approach by giving the model a set of actions it can do, a decision maker, and an environment where the algorithm is supposed to make decisions that will net more rewards within a given timeframe. How does the program learn how to maximize its efforts?
It learns through trial and error.
Do People Actually Use Machine Learning?
Believe it or not, machine learning, algorithms, and learning methods aren’t just textbook theory. In fact, you’ve probably encountered a form of machine learning at some point earlier today.
These are just a few common machine learning applications:
- Online Shopping: Have you ever bought a stunning pair of pants and then later seen a shirt or blouse that just so happened to fit the aesthetic of the outfit you were just looking at?
- Social Media Monitoring: A single Twitter feed can tell you everything from a person’s shopping habits to their musical tastes and even their level of brand loyalty. How do companies find them and develop buyer personas?
- Web searches: How do Google, Yahoo, and Bing scour thousands and thousands of web pages to help you find what you’re looking for within seconds?
That’s right. These tasks are all made possible through machine learning.
Conclusion
Machines learning new information sounds like the type of development. You’d expect to see in a sci-fi movie or in the far future. Yet not only is machine learning far from theoretical, but it’s also being put into practice right now with applications that range from online shopping to intelligent ad networks. As new algorithms continue to be developed, machine learning is only going to become more prevalent in the coming years.
Also Read: What is cloud computing?