In today's era, machine learning has become an indispensable part of the digital transformation journey. Organizations are highly required to utilize new concepts and technologies to provide better services and greater value to customers. Therefore, the presence of machine learning could help them respond to market dynamics changes and gain benefits.
Machine Learning is capable to automate many tasks, especially the ones that only humans can perform with their innate intelligence. It undoubtedly helpful for organizations analyze data and make accurate decisions. In addition, it also enables them to better respond to their customers needs while also striving for the highest level of business performance.
How Machine Learning Works
UC Berkeley breaks out the learning system of a machine learning algorithm into three main parts.
a. Decision Process
Machine learning algorithms are used to produce predictions or classifications in general. Your algorithm will generate an estimate about a pattern in the data based on some input data, which can be labelled or unlabelled.
b. Error Function
The model's prediction is evaluated using an error function. If there are known examples, an error function can be used to compare the model's accuracy.
c. Model Optimization Process
Weights are modified to lessen the difference between the known example and the model estimate if the model can fit better to the data points in the training set. This evaluate and optimize procedure will be repeated by the algorithm, which will update weights on its own until a certain level of accuracy is reached.
Categories of Machine Learning
a. Supervised Learning
Supervised learning (SL) is defined by its use of labelled datasets to train algorithms that reliably classify data or predict outcomes. Organizations can use it to tackle a range of real-world problems at scale, such as spam classification in a distinct folder from your email. It uses a training dataset to instruct models on how to get the desired result. This training dataset contains both correct and incorrect outputs, allowing the model to improve over time. The loss function is used to assess the algorithm's correctness, and it is adjusted until the error is suitably minimized.
b. Unsupervised Learning
Unsupervised learning (UL) analyzes and clusters unlabelled datasets using machine learning algorithms. Without the need for human intervention, these algorithms uncover hidden patterns or data groupings. It is the best solution for exploratory data analysis, cross-selling techniques, consumer segmentation, and image identification because of its capacity to detect similarities and differences in information.
c. Reinforcement Learning
Reinforcement Learning (RL) is a feedback-based machine learning technique in which an agent learns how to behave in a given environment by executing actions and seeing the outcomes of those actions. The agent receives positive feedback for each excellent action, and negative feedback or a penalty for each bad action. It also learns autonomously utilizing feedback and no labelled data. Because there is no labelled data, the agent must rely only on its own experience to learn. RL is used to tackle a certain sort of problem in which sequential decision-making is required and the aim is long-term, such as game-playing, robotics, and so on.
Machine learning is now considered as highly important because it gives enormous benefits to organizations. It helps them to gain real-time business decision as it analyses the existing data, predict customer behaviour to offer more customized services. As it has become a significant competitive differentiator, organizations are strongly encouraged to implement machine learning in their business.
Learn more about Machine Learning and gain best benefits of it with Multimatics!
Education, I. C. (2020, July 15). Machine Learning. IBM Cloud Education. https://www.ibm.com/cloud/learn/machine-learning
JavaTpoint. (n.d.). Reinforcement Learning Tutorial. https://www.javatpoint.com/reinforcement-learning