Introduction to Machine Learning

Machine learning is a subset of artificial intelligence (AI) that involves the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task. It focuses on the development of algorithms that can learn from and make predictions or decisions based on data.

Here are five use cases of machine learning spanning different industries or applications:

• Banking - Fraud Detection?

Banks and other financial organizations spend enormous money each year on recovery costs due to fraud. Machine learning solutions can help banks to predict fraudulent behavior. These solutions work in real-time to constantly check the possibility of fraud and generate alerts accordingly. CitiBank has used Machine learning-based outlier detection systems to control better and monitor payments while improving security. ?

• Business - Email Monitoring?

Email is a primary platform for malware and other cybersecurity attacks. Machine learning is instrumental in detecting these attacks through real-time email monitoring. For example, Natural Language Processing technologies can analyze the content of an email message and determine the possibility of phishing attempts. Tessian uses machine learning-based email monitoring software to prevent phishing attacks, information breaches, and malware attacks.?

• Healthcare - Medical Imaging?

Microsoft’s Project InnerEye is open-source software that harnesses computer vision and machine learning to differentiate between tumors and healthy anatomy using 3D radiological images that assist medical experts in radiotherapy and surgical planning and accelerate the treatment of cancer patients.?

• Retail - Dynamic Pricing?

Online retailers such as Amazon use machine learning algorithms to determine the dynamic pricing of a product or a service, making it possible to map the best price for each product. Algorithms allow them to enhance sales while optimizing inventory at the same time. Real-time discounts can be applied to engage customers and maintain an edge in the market.?

• Manufacturing -?Predictive Maintenance?

In manufacturing, predictive maintenance pre-empts the failure of vital machinery or components. By analyzing data from previous maintenance cycles, machine learning can identify patterns to predict equipment failures and needed future care. Predictive maintenance saves manufacturers significant time and money by allowing them to tackle specific issues exactly when required.

Types of Machine Learning Analysis

Machine learning encompasses different algorithms or models with broad applicability, while others are suited for specific applications. Machine learning is divided into three primary learning model approaches supervised, unsupervised, and reinforcement. Each model differs in training; each has its strengths and faces different tasks or problems. When choosing a machine learning model to deploy, an organization needs to understand the available data and the problem to be solved.

Supervised:

Supervised machine learning algorithms are the most commonly used for predictive analytics. Supervised machine learning requires human interaction to label data read for accurate supervised learning. In supervised learning, the model is taught by example using input and output data sets processed by human experts, usually data scientists. The model learns the relationships between input and output data and then uses that?information to formulate predictions based on new datasets. For example, a classification model can learn to identify plants after being trained on a dataset of properly labeled images with the plant species and other identifying characteristics.??

Supervised machine learning methods commonly solve regression and classification problems:??

• Regression problems involve estimating the mathematical relationship(s) between a continuous variable and one or more other variables. This mathematical relationship can then compute the values of one unknown variable given the known values of the others. Examples of problems that use regression include estimating a car's position and speed using GPS, predicting the trajectory of a tornado using weather data, or predicting the future value of a stock using historical and other data. ?
• Classification problems consist of a discrete unknown variable. Typically, the issue involves estimating which specific sample belongs to a set of pre-defined classes. Examples of classification are filtering email into spam or non-spam, diagnosing pathologies from medical tests, or identifying faces in a picture. ?

Unsupervised

Unsupervised machine learning algorithms do not require human experts but autonomously discover patterns in data. Unsupervised learning mainly deals with unlabeled data. The model must work on its own to find patterns and information. Examples of problems solved with unsupervised methods are clustering and association: ??

• Clustering methods?- Clustering is the grouping of data that have similar characteristics. It helps segment data into groups and analyzes each to find patterns. For example, clustering algorithms identify groups of users based on their online purchasing history and then send each member targeted ads. ?
• Association methods - Association consists of discovering groups of items frequently observed together. Online retailers use associations to suggest additional purchases to a user based on the content of their shopping cart.??

Reinforcement

Reinforcement learning teaches the machine through trial and error using feedback from its actions and experiences, also known as learning from mistakes. It involves assigning positive values to desired outcomes and negative values to undesired effects. The result is optimal solutions; the system learns to avoid adverse outcomes and seek the positive. Practical applications of reinforcement learning include building ratification intelligence for playing video games and robotics and industrial automation.