Machine Learning Chronicles: Sit Back and Let Machines Take the Stage
Published: 2024-02-08 18:04:52

In today's tech-focused world, we often come across terms like Machine Learning, Artificial Intelligence, and Model Building, but many of us aren't entirely sure what they mean. Googling these terms usually brings up overly technical explanations, which can be confusing. So, in this blog, let's break down these concepts using simple examples and references from everyday life. By the end, you will be equipped to understand and discuss these topics more confidently, even when they are thrown around in conversation.

What is Machine Learning?

Machine learning Is where machines learn how to function independently with pre-provided data using artificial intelligence to make our day-to-day lives easier. It can be explained as building machines and gadgets around us with technical features and an inbuilt learning process to work autonomously so that we don't have to teach it to work for us.

The first step to achieve the same is feeding good quality data to our machines and training them to build their algorithms. Similar to your language coaches teaching and guiding you through the rules of grammar, algorithms drive the ML into model learning.

They are essentially a subdomain of Artificial intelligence but are less broad comparatively. It is worth noting that while all kinds of machine learning are forms of artificial intelligence, not all AI is machine learning.

Other commonly known aspects of ML include training and predictions. Algorithms are trained to learn patterns and to use those to make predictions. One of the common examples could be the movie recommendation system used by many of the online entertainment platforms, which is based on your watch history and liked content.

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How does Machine learning work?

Tradition Machine Learning: A system that combines statistical tools with provided data to predict outputs useful for making relevant insights. It is pretty much like training your favourite fictional character, so let's understand it in that manner to make it simple:

Gathering different kinds of data using various processes;

Deep Learning: Neo from 'The Matrix' downloading information directly into his brain could be an example of understanding this technique, which is powerful but currently beyond the scope of our understanding.

Historical data: Where the box office predicts how certain movies would work based on their past productions, the actors' popularity, and script analysis is another excellent example of understanding how historical data is used to train models for predicting future events.

Supervised Learning: observing labelled data and building a mental model based on it is called supervised Learning, like when S.Holmes analyses clues at the crime scene (labelled data) to predict his culprits or Professor X telepathically reads people's thoughts (unlabelled data) to uncover hidden patterns.

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Choosing the training regime: There are a few distinct types of training regimes you may choose, like supervised Learning, as explained prior, or reinforcement learning, where the model learns through a trial-and-error process similar to our defeated superheroes from who learn from their past mistakes to predict their opponent's future actions, and finally transfer Learning which uses pre-trained models on similar tasks to suggest learning for new problems. You can understand this with the example of the film 'Inception' where Cobb planting ideas into Dom Cobb's dreams is a simple case of 'transfer learning.'

Machine learning models must also work towards refining their skills using model optimization, continuous Learning, and development with testing. Finally, after a new model is built, it is also made to be helpful in case of unprecedented challenges with model improvement and models' ability to generalize. ML algorithms work best when combined to solve different complex problems.

Examples and Tasks taken up by ML models

ML models are commonly used to provide recommendations using recommender systems, like your food delivery apps showing you your favoured restaurants based on your order history. Reinforcement learning also exists, which is used to improve the above-mentioned recommendation system by providing further feedback to the system to change its recommendations accordingly.

Conclusion

Nowadays, ML models are being used in every sphere of the existing economy to improve business decisions, increase productivity, detect diseases, forecast weather, etc.

The tech world is growing super-fast! So, we need better ways to understand all the information we have now and get ready for even more information in the future. To do that, we must keep building really smart machines, and that could actually be helpful.

So next time you watch Ironman, let's remember that Jarvis, our favourite AI assistant, is simply using natural language processing, a branch of ML, to understand Tony Stark and that the cute robot Baymax is using machine learning as well to personalize medical care for diagonizing illness.