Undoubtedly, Machine Learning continues to shape the trajectory of industries with new innovations being added daily. Its fast paced growth is enabling individuals and organizations to enjoy improved performance and productivity.  In our last blog post we examined some of the applications and benefits of Machine learning in today’s world. We will go further to explore some key concepts in Machine Learning. This will help us gain understanding on key concepts and components in Machine Learning. 

As previously explained, Machine learning is a subset of AI that is characterized with the ability to perform tasks without explicit instructions. It focuses on the development of algorithms that allow computers to learn from data and make decisions based on that data. The software algorithms in ML can continuously learn from experience and improve predictive performance over time. 

Machine learning and Traditional Programming

Machine Learning is different from Traditional Programming in a number of ways. Before the advent of ML, programming was done traditionally. This means that programming was mainly done with a set of predefined rules and instructions curated by the developer. Traditional programming operates with rule based logic where possible circumstances are anticipated and coded by the programmer. This invariably means that the output will always remain the same as determined by the given input, and as such changes will require manual updates to the previously written code.

On the other hand, Machine Learning focuses on enabling computers to to perform and execute tasks by learning from data. After being equipped with large data sets, ML models can make predictions based on observed patterns. This approach enables ML models to continually adapt and evolve with new data input without the need for manual intervention. ML models are largely data driven, allowing them to detect complex patterns that might not be obvious to developers. 

The Main types of Machine Learning

 Supervised Learning

As the name implies, this ML model is concerned with feeding the algorithm information for its learning while being supervised. It works by providing  the ML algorithm with labeled datasets that indicate the desired input and output. The algorithm finds a method to determine how to arrive at these inputs and outputs by analyzing patterns from data. It learns and makes predictions and this process is repeated until the desired level of accuracy is achieved.

Supervised learning algorithms enable organizations to create complex models that can make accurate predictions. Consequently, they are applied across industries in many ways such as in predicting real estate prices, identifying fraudulent transactions, detecting anomalies in equipment and others. The supervised learning process generally includes classification, regression and forecasting.

Unsupervised Learning

Unsupervised Learning uses ML algorithms to analyze and cluster unlabeled datasets. Contrary to Supervised Learning algorithms that work with labeled datasets, Unsupervised learning uses self learning algorithms that can learn without previous training. Also, they are suitable for complex processing tasks that involve organizing large datasets into clusters. They are able to discover hidden patterns or data grouping without human supervision.

Additionally, Unsupervised Learning models have no established directions for desired outputs or relationships. Instead, the goal is to explore data and discover patterns, trends, and relationships. They  are useful for three main tasks which are Clustering, Association and Dimensionality Reduction. They are applied in many ways such as anomaly detection, customer segmentation, natural language processing amongst others.

Semi-supervised Learning

Semi -supervised learning is a hybrid technique that rests between supervised learning and unsupervised learning. It combines a small amount of labeled data with large amounts of unlabeled data to train a model. Semi-supervised models can draw insights from the output variable based on input variable in supervised learning to draw accurate predictions. While also utilizing a vast amount of unlabeled data in unsupervised learning to provide further accuracy. It uses labeled data to ground predictions and unlabeled data to learn the shape of the larger dataset. 

This ML technique is useful because gathering labeled data can be time consuming and unlabeled data may not provide desired accuracy. Semi-supervised learning therefore leverages both supervised and unsupervised learning models by combining their strengths to provide more accurate models. Some examples of its usage is in protein sequence classification and social media content classification.

Reinforcement Learning