Machine Learning in Summary
Machine learning has made it possible for various computer innovations in different automating processes worldwide. Machine Learning is the study and use of computational statistics and mathematical functions to make predictions and decisions without being “explicitly programmed to do so”.
These algorithms are used to develop models on the foundation of a dataset or sample data known. This data sample is known as “training data”. With an understanding of this, it is safe to say that machine learning helps the computer to carry out future tasks by training it with similar tasks using sample data.
Machine learning is a method that automates analytical model building. It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
How Useful is Machine Learning Today?
Machine Learning, since its inception, has been a huge part of our changing society. Humans have been able to adopt Machine learning algorithms into various aspects of life. Below are examples of how machine learning has advanced.
- Fraud Detection: Machine Learning has proven to be very effective in detecting fraudulent actions in online transactions. Algorithms would be used in creating models that can differentiate patterns of legitimate transactions from illegitimate ones.
- Recommendation: One of the most widely used applications of machine learning is the recommendation systems. Algorithms are used to design recommendations based off of individual or market wide characteristics or choices. Many can relate with this. For example, Netflix uses this to recommend shows for you, I bet you can relate now. The accuracy of these systems is based on the features considered in training the dataset used in developing the model.
- Image Recognition: A machine learning model essentially looks for patterns of pixel values that it has seen before and associates them with new ones. With this, it would be possible for the computer to identify and classify images on its own. This is also referred to as Computer Vision.
- Voice Recognition: With Machine Learning, it is possible for computer to identify and classify voices and voice patterns.
- Financial Analysis: Machine Learning has been adapted to the world of finance and it has become increasingly useful. Predictions can be made using algorithms to determine if an investment would be profitable or not, too risky or not.
Industries of Use
Many industries have adapted ML into their activities. ML has proven useful in these industries, such as; Financial and fintech, Healthcare, Manufacturing, Engineering, Public and Government institutions and so on.
Types of Machine Learning
Supervised and Unsupervised Learning are major and important types of machine learning.
Supervised Learning: In this type of learning, algorithms are developed by using labeled data. What this simply means is that the type of data that is being trained is one whose features are labeled and known. For example, data containing features of different types of food. When the algorithm is trained with these features, if the computer is given new data, it would be able to identify the types of food present in it based on the features of the data it was earlier trained with. This type of learning is regarded to be more accurate than the other and is less computationally complex.
Examples of Supervised Learning algorithms include; Regression, Logistic Regression, Support Vector Machines, Decision Tree
Unsupervised Learning: Unsupervised learning does not work with labeled data as Supervised Learning does. In this case, it works with data having features, but features are unknown. For instance, an algorithm being trained with a dataset that is unlabeled can be used to create a model that will in turn identify other types based on the features of the data it was trained with. The model works by creating clusters of data with similar features or creating data with positional association.
Examples of Unsupervised Learning algorithms include; Principal Component Analysis, K-Means Clustering, K-Nearest Neighbours
