Machine Learning 101: Geek Party Conversation Essentials

Imagine this. You are at a party, and your usual geek-gang members are flexing their newly-acquired emerging-tech knowledge muscles to impress the crowd.

Machine learning becomes the topic of choice, and you are at a loss for words. Well, worry not. Here we are with some core pointers from the world of ML that you can shoot!

Now please don't make that "but-I-am-already-an-ML-expert" face and roll your eyeballs to make us realize how stupid it was to write this article (show-off). Instead share it with a friend who might be in need (good karma+sick insult rolled into one you see).

Now, for the lesser mortals, here are four core concepts that are worth a read (guess what, we have added examples for each one as well. Brilliant, right?). Now read on.

Machine learning (ML) is a method of teaching computers to learn from data, without being explicitly programmed.

It involves training a machine or model on a dataset, and then using that trained model to make predictions or decisions on new, unseen data. Machine learning is a critical part of the broad gamut of Artificial Intelligence (Read: 10 core concepts of AI).

4 pointers on ML to appreciate:

Training a Machine to Learn from Data:

Instead of explicitly programming a machine to perform specific tasks, ML trains a machine to learn from data. This is done by using a set of labeled data (training set) to "teach" the machine how to make predictions or decisions.

Once trained, the machine can then be used to make predictions or decisions on new, unseen data (test set).

Example: A company wants to develop a system that can automatically classify customer emails as either "spam" or "not spam". The company would use a dataset of labeled emails (spam or not spam) to train the machine, and then test the machine's performance on new, unseen emails.

Features:

A feature is an input variable that is used to make predictions or decisions.

The process of selecting the most relevant features for a given task is known as "feature selection" and is an important step in the ML process.

Example: In a fraud detection system, the features used to predict fraud could include transaction amount, location, and past transaction history. The feature selection process would involve identifying the most important features to include in the model, and which are not relevant to the task.

Different Types of Algorithms:

Supervised Learning: the most commonly used type of ML algorithm, it involves training a machine on a labeled dataset, where the desired output is already known.

Unsupervised Learning: it involves training a machine on an unlabeled dataset, where the desired output is not known.

Reinforcement Learning: a type of ML that is based on the idea of an agent taking actions in an environment in order to maximize a reward.

Example: A retail company wants to segment its customer base for targeted marketing. It could use unsupervised learning to identify patterns in customer data and group similar customers together, without the need for predefined labels.

Overfitting and Underfitting:

Overfitting occurs when a model is trained too well on the training data and performs poorly on the test data.

Underfitting occurs when a model is not able to learn the underlying pattern in the training data, resulting in poor performance on both the training and test data.

Example: A model is trained to identify images of cats and dogs, with an accuracy of 90% on the training data. But when tested on new images, it only has an accuracy of 50%. This is an example of overfitting, where the model has learned the training data too well, and is not able to generalize to new data.

Now that you are kinda clear about these core topics, go attend more geek parties, and try to convert every single topic under the sun into an ML discussion. After all, flexing is everyone's right!

Note for the geeky ones: Here is an opportunity for you to flex (again) your expertise. Feel free to add more points to help the world learn better. Good karma again!

Consultants Factory (www.consultantsfactory.com) is a leading accredited provider of certification-based IT management training services.