Machine Learning (ML) is a field of computer science that is responsible for “learning” given a set of data. In other words, it is responsible for representing the structure and generalizing behaviors of the given data. You know we always strive to provide any help we can, here is the list of top 10 machine learning developers in Canada. Check them out and make an informed decision.
What Exactly is Machine Learning?
Machine Learning is the buzzword. Everyone is talking about it, but few know precisely what it is. In this post, we will simply tell you what all this fuss is about. One could say that machine learning is to computer science, just as probability and statistics are to mathematics. In this context, learning – learning is not about memorizing and collecting data. The idea is to create a model based on the information provided to be able to generate conclusions about copies never seen before.
Types of Algorithms in Machine Learning
These are the main types of algorithms associated with Machine Learning:
Supervised
This branch of the ML takes care of the problems that bring data with labels. For example, try to classify the dogs and muffins in the image above. These types of algorithms seek to generalize and predict based on the information provided.
Not supervised
In the absence of a more creative name, this branch of the ML takes care of the rest of the problems, that is, those that do not bring the data with labels. For example, given a group of users of a social network, group them (clustering) into communities. These types of algorithms seek to extract structure and find patterns from the information provided.
Additionally, each of these types of MLS is subdivided according to the type of data they handle:
Continuous:
Quantitative / numerical information. For example, predict the price of a house.
Discreet:
Qualitative information. For example, predicting which sports team a person is a fan of.
Check Also: Benefits of Machine Learning for Business
How does it work?
The main objective of every learner is to develop the ability to generalize and associate. When we translate this to a machine or computer, it means that they should be able to perform with precision and accuracy, both in family tasks, as in new or unforeseen activities.
And how is this possible? Making them replicate the cognitive faculties of the human being, forming models that “generalize” the information presented to them to make their predictions. And the key ingredient in this whole issue is the data.
The origin and format of the data are not as relevant since machine learning is capable of assimilating a wide range of data, which is known as big data, but it is not perceived as data but as a huge list of practical examples.
What are your current perspectives and applications?
Once you see how easy and practical it is to apply machine learning techniques to problems that you thought would be impossible, it is when you start to believe that it could solve virtually any problem * – as long as there is enough data.
*Eye! It should be remembered that this only works if the problem in question is solvable.
For the modern consumer, machine learning is a key facilitator of many of their daily tasks. From translation services to weather forecasts, to guessing what users want based on their recent activities; the benefits offered are incomparable.
Concerning business, many companies have begun to incorporate this technology into their operating systems, with high expectations of improving and automating their processes.
According to this year’s Global Digital IQ survey, 54% of the organizations they interviewed are making significant investments in AI and that percentage is expected to increase to 63% in less than three years. For its part, HubSpot reported according to a survey on artificial intelligence that is conducted, that 63% of people use AI daily, without even knowing it.
Exactly to what areas can this type of technology be applied?
Since machine learning is a system based on the processing and analysis of data that is translated into findings, it can be applied to any field that has sufficiently large databases. At the moment, some of its most popular and developed uses are:
- DNA sequence classification
- Economic predictions and fluctuations in the stock market
- 3D mapping and modeling
- Fraud detection
- Medical diagnoses
- Internet search engines
- Voice recognition systems
- Optimization and implementation of digital advertising campaigns
Canada is already investing in the state of the art technologies like Machine Learning and its other sibling branches like deep learning. Here software houses are very progressive and not carrying any undue past burden. Canada has become the land of opportunities, if you have decided to opt for a Machine Learning project here, Indeed it’s one of your best decision.
