5 FAQs about Machine Learning Answered

Machine learning

Last Updated on June 2, 2023 by

I love playing a certain game of tennis on my mobile phone. I signed in to play the game today and a pop up appeared saying that my AI agent has won a game on my behalf using my style of game play. They even offered me some highlights. I went on to watch the highlights and found that indeed my AI doppelganger played like me. And mind you, it is a game that involves diverse finger movements, swipes, and double taps, and whatnot! That is an incredible feat of machine learning right inside a free gaming application. We surely have come a long way in this path.

However, only a miniscule percentage of the population that is influenced by machine learning on a regular basis knows anything about it at all. Luckily, most of those who know something about it find it intriguing and they have questions. We will try to answer some of them here.

1.   What is machine learning?

Yes, that was totally obvious, was it not? Anyway, it is a process in which a machine is trained to learn from data and improve its functionality without human intervention. Let us say you expose a program to a lot of pictures of yourself telling it that it is you. Then you show it a group picture which of course, has your face and it recognizes you, that is machine learning. It allows facebook to let you know whenever someone posts a picture of you without tagging you.

2.   Are machine learning and AI the same thing?

No,  they are not.

These two names are pronounced in the same breath as if they were the same thing. But they are not. Just focus on the names and you will understand. Artificial intelligence is a system that is powered by multiple learning models. It is more like a facilitator of AI.

3.   Can I learn at home?

Yes, you can. Some help from experts can never hurt and is in fact recommended. But for the sake of the argument, you can learn at home. Read more about how to learn AI tools by yourself.

If you have a strong grip over linear algebra, statistical models, calculus, and a couple of programming languages, learning some algorithms at home should not be impossible at all.

If you find it too difficult, do not fret, there are a bunch of good machine learning courses. Get into one and you will find that it is worth every penny.

4.   Are data science and machine learning the same?

Well, had they been the same we could well have called them by the same name. Data science is a multidisciplinary field – a marriage of statistics and computer science. It focuses on new methods of dealing with data and finding ways of making more out of the data. It has quite a different purpose as we’ve already explained.

However, data science and machine learning have a synergistic relationship. While machine learning algorithms can help data scientists churn more data. Data science facilitates machine learning by curating good quality data. It is truly the legacy of data science.

5.   Are machine learning and deep learning the same thing?

No, but they are pretty close. In fact deep learning is one sort of machine learning. In traditional learning methods are categorized as supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning occurs through training a machine with labeled data. Unsupervised learning happens when the machine is trained to recognize features of unlabeled data. In case of reinforcement learning the machine learns to achieve a certain goal through trials and feedback.

As opposed to these, deep learning uses multiple layers of neural networks (the emulate the functionality of the neurons) to recognize patterns in unstructured data. Deep learning has opened the floodgates of developments in the areas of computer vision, and natural language processing.

Bonus. How to kickstart your career

You should start by understanding the basics of data analytics. Your mathematical knowledge will come in handy. Improve your coding skills. Learn Python if possible. Do undergo a machine learning course if you can. It will always make a difference.

With a knowledge of a few coding languages, a strong grip over linear algebra and calculus, and a head, set straight towards knowledge acquisition you can definitely enter the field. It is a great time to join the bandwagon,  if you were wondering.

Apart from that, if you are interested to know more about AI applications and their importance in building then visit our Technology category.