Machine learning is actually taking a great part of our everyday life, it is the process that powers many of the services we use today, such as the recommendation systems on Netflix, YouTube, and Spotify, the search engines like Google and Baidu, social media feeds like Twitter and Facebook, or even the voice assistants like Siri and Alexa. Becoming a machine learning engineer or a data scientist is what machine learning education will offer you, along with understanding how to enable a system to learn from data rather than through programming.
To learn machine learning, it is first important to understand what it is. Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. Machine learning algorithms use statistics to find patterns in massive amounts of data, and this data includes different things, such as pictures, numbers, words, clicks, etc. that’s why it powers many of the different services that we are all using on a daily basis.
Machine learning is considered the process of feeding computers specific data and teaching them how to make accurate predictions. These predictions could be anything from guessing an element in a photo to telling whether a specific email you received is considered spam or not.
One of the things that differentiate machine learning from traditional computer software is that machine learning does not include the human part of writing codes in order to help the computer in these predictions, but instead a computer is trained to create these predictions based on the data it has been fed with; that’s mainly what machine learning is all about.
There are two important stages that led to the breakthrough of machine learning, the first one was back in 1959 by Arthur Samuel when he realized that computers could actually learn by themselves instead of teaching them everything and how to carry out tasks. The second stage was with the appearance of the internet and the huge increase in the amount of digital information being generated, stored, and made available for analysis.
Machine learning and artificial intelligence are not the same thing, although people also talk about them as if they are, which eventually led to some confusion. Machine learning is considered an application of AI, while artificial intelligence is the broader concept of machines which are able to carry out tasks in a way that we end up referring to as “smart”.
Artificial intelligence is the step beyond machine learning, yet AI needs ML to reflect and optimize decisions. AI actually uses what it has gained from ML to simulate intelligence, the same way a human is constantly observing their surroundings and making intelligent decisions.
Machine learning is actually not far from you, most of the services you use incorporate machine learning in them. Do you know that 75% of Netflix users actually select films from the ones recommended to them by ML? Not just Netflix, but even the ads you receive online are formulated through using your data, which also happens through the help of machine learning.
There are three basic types of artificial intelligence; the narrow intelligence (ANI) which you can find in video games and financial markets, artificial general intelligence (AGI) which is the AI that can think as human beings and perform daily tasks and which is still far away from humans, and finally the artificial superintelligence (ASI) which is based on the idea of computers building on one another to become super intelligent once AI reaches AGI.
In the future, it is actually believed that both machine learning and artificial intelligence will be imperative to society and people will be depending on these tools more than they might even imagine at the moment. People should definitely understand machine learning and artificial intelligence because they will be crucial in the future and will help them understand the changes taking place, as well as the tools that they will be depending on and using on a daily basis.
There are several reasons behind learning machine learning and artificial intelligence, such as:
Machine learning is being used in different fields; health care, finance, business and others, and that’s why it empowers any person working in any field. A report done by TMR noted that machine learning as a service is predicted to grow to $19.9 billion by the end of 2025, from a mere $1.07 billion in 2016. Actually, a lot of companies are now investing in machine learning since it covers different things, such as image recognition, medicine, cyber security, facial recognition and more.
When you come to understand and learn machine learning, you open more gates for yourself in the future. It is not just about finding a better career opportunity, but it is also about taking your business to another level or making it part of something global.
Better career opportunities always bring better salaries along with them, and learning machine learning will bring you better career opportunities with a better salary. According to Indeed, the average machine learning engineer salary is approximately $146,085 which is about 344% increase since 2015.
One of the other important reasons why it would benefit you to learn more about machine learning is being able to fill any of the job posts that are posted. The top tech companies are always searching for machine learning engineers who can build algorithms. According to Indeed, the number of machine learning jobs have been rising from 2014, stating that the number of machine learning engineer job postings outstrip the number of searches for machine learning. This lack could actually lead to a gap between the number of jobs posted and the number of employees to fill them, and that’s why it’s important to learn machine learning.
In addition to becoming a machine learning engineer, machine learning could actually help you land for a data scientist job since it is directly linked to data science. Learning machine learning will always make you a hot choice for employers since you will be able to serve them in both ways; as a machine learning engineer and a data scientist. This means that you can analyze data, extract value from it, and later use this information to train a computer how to predict results.
It has been known that a machine learning engineer partners with a data scientist in order to bring better synchronization for work products, but now one who has learned machine learning could actually do both.
If you have some plans to become a machine learning engineer or even considering a data scientist job in the future, there are specific things to learn and steps to take in order to become qualified. First of all, if you are new to the whole thing then there are some prerequisites which you should first go through, such as algebra, multivariate calculus, statistics, and Python, just a basic understanding and you will be fine.
It’s then important to learn more about the concepts of machine learning before you move to the more complicated stuff, such as knowing more about what is a model, feature, target, training, and prediction. It’s also important to know about the different types of machine learning that are found out there, such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Sometimes learning all by yourself from free online courses is possible, but you will need to practice and even ask those who might help you when needed. Other options for learning machine learning is through taking courses or else choosing a bootcamp. RoboGarden is offering a comprehensive 11-weeks machine learning and artificial intelligence bootcamp for those interested in the world of machine learning. You could learn more and register through this link.
Do you want to learn coding in less time than possible and without any money, join us on the best platform ever on learning coding as a game for kids?