Machine Learning Fundamentals

How does AI get intelligent? It passes through a phase of training, more generally known as Machine Learning (ML).

Broadly, this means giving an algorithm (i.e. a piece of running software) a ton of data and teaching it to interpret that data.

Once this training phase is complete, you can give the algorithm new data and it deploys what it has learnt to interpret and predict for you.

ML has become an essential component of many industries, helping to streamline processes, make better decisions, and improve user experiences. There are several types of ML, each with its unique approach to learning from data. These include

• supervised learning

• unsupervised learning

• reinforcement learning

• semi-supervised learning

• active learning

Supervised Learning

Supervised learning is the most common type. In this approach, algorithms learn from labeled data, which consists of input-output pairs. In other words, you give the algorithm the pattern to look for. 

For example, imagine you have a dataset of images of cats and dogs, and you want to train a model to recognize them. In supervised learning, each image would be labeled with either "cat" or "dog" so that the algorithm can use these labels to learn the characteristics of each animal.

The primary goal of supervised learning is to create a model that can accurately predict the correct output given new, unseen input data. Common tasks include classification (e.g., determining if an email is spam or not) and regression (e.g., predicting house prices based on various features).

Unsupervised Learning

In unsupervised learning, algorithms work with unlabeled data, meaning there are no correct outputs provided for the input data. You don’t give it a pattern to look for. The algorithm must determine the patterns on its own. 

Instead, these algorithms search for underlying patterns, structures, or relationships within the data. Unsupervised learning can be useful for tasks such as clustering (grouping similar data points together) or anomaly detection (identifying unusual or unexpected data points).

A common example of unsupervised learning is customer segmentation. By analyzing customer data, companies can discover patterns that reveal different customer groups, allowing them to create targeted marketing campaigns or tailor their products and services to specific demographics.

Reinforcement Learning

Reinforcement learning (RL) is a type of machine learning where algorithms learn by trial and error, receiving feedback in the form of rewards or penalties. The goal of reinforcement learning is to develop a strategy, or policy, that maximizes the cumulative reward over time.

A popular example of reinforcement learning is in robotics. A robot can learn how to navigate a maze or pick up objects through trial and error, gradually improving its strategy based on the feedback it receives from its environment.

Semi-supervised Learning

Semi-supervised learning combines elements of both supervised and unsupervised learning. In this approach, a small amount of labeled data is used alongside a larger unlabeled dataset. The advantage of semi-supervised learning is that it can produce better models with less labeled data, making it more efficient and cost-effective than supervised learning in certain situations.

For instance, a language translation model may use a small set of labeled sentence pairs (input in one language, output in another) alongside a large dataset of unlabeled text to improve its translation capabilities.

Active Learning

Active learning is an approach in which algorithms interactively query users or experts to label data points. By prioritizing the most informative examples, active learning can reduce the effort required for labeling and improve learning efficiency.

Imagine training a facial recognition system: Instead of labeling every image in a large dataset, the algorithm could ask a human expert to label only the most uncertain or ambiguous examples, allowing it to learn more effectively.

ML is so hot right now. By understanding the basics of machine learning types, including supervised, unsupervised, reinforcement, semi-supervised, and active learning, we can better appreciate the potential of these techniques to transform the way we live and work. As models get better and better trained, they will become an increasingly central part of operations across all sectors.