Machine Learning is a subset of Artificial Intelligence. It is defined as a group of algorithms that aid software systems in predicting scenario outcomes based on training data. The algorithms then apply their training to new datasets and arrive at (hopefully) accurate predictions.
By definition, an ML program keeps learning as it acquires new data inputs. Outcomes that are not explicitly taught are deduced based on statistical analysis, and the “machine” continues to evolve as it encounters new scenarios in the form of fresh data items.
At the heart of machine learning is the ability to identify patterns and trends in a given data set. In that respect, ML is similar to predictive modeling or data mining, although the former is generally on autonomous mode once the initial training is complete.
Where is Machine Learning Used?
ML is primarily used in situations where a large number of data points exist. To use traditional data intelligence tools would be a humungous manual effort that would take too much time for the end decision or output to be of any great value.
As an example, have you wondered how recommendation engines work on eCommerce sites like Amazon? That’s machine learning at its commercial best. Amazon itself reported a year-over-year revenue increase of 29% during a particular quarter, and a significant portion of that is how Amazon deploys recommendations on-site and off-site.
Such ML models are common today, and they drive a lot of the growth that we see in eCommerce platforms.
Another use case for machine learning is fraud detection in the banking and financial services sector. According to Cognizant, one global bank achieved a 50% reduction in fraudulent transactions and saves $20 million a year in fraud losses. The machine learning model deployed to achieve this was based on Google TensorFlow and training a neural network on how to identify anomalies in physical checks.
Machine learning is also used in cyber security to detect network security threats. It can also be used to filter out spam messages received by email accounts. In most cases, the quantum of commercial benefit can be measured, thereby validating the use of machine learning.
Categories of Machine Learning
One of the main ways to categorize machine learning programs is to divide them into supervised and unsupervised algorithms. Supervised machine learning requires a data expert, also known as a data scientist, to help with inputs and outcomes, as well as provide feedback on how accurate the predictions are.
The higher, unsupervised, level of machine learning involves a self-learning process that requires no supervision. By using iterative methods like deep learning, neural networks arrive at their own conclusions based on the data they are fed.
Deep learning is more suited to extremely large datasets because they reveal deeper patterns and trends, leading to a greater degree of accuracy over time. However, it is still a subset of machine learning because it uses data to learn to be iteratively more accurate. Deep learning is typically used for complex processes like NLG (Natural Language Generation), image recognition and speech-to-text.
The applications of machine learning are virtually boundless. Various algorithms like neural networks, K-means Clustering, and Decision Trees are used to achieve specific outcomes, and these algorithms are chosen for the modelling based on various factors like the type of data used and whether or not the outcomes must lead to specific actions.
The Future of Machine Learning
Some AI experts believe that machine learning is the only type of artificial intelligence that man must develop. We’re still far from creating self-aware machines that are capable of creating their own armies of automatons without human input. This fear of self-aware machines has largely been fodder for the many science fiction movies made over the years about robots taking over the world and eliminating humans as we might eradicate a parasite.
The future of machine learning, however, is safe for now. It offers tangible commercial benefits and has already ingrained itself into so many industries that it will be impossible to go back to the pre-ML age. And its influence will continue to grow until a sizable portion of commercial activity that is currently the purvey of humans transitions into machine learning models that are more efficient and more accurate, among other things.