Machine Learning (ML) is a sub-branch of Artificial Intelligence (AI) that enables computers to learn, adapt, and perform the desired functions on their own. ML algorithms can learn patterns from the previous input and results and adjust tasks accordingly. Machine learning can be categorized in one of three major ways.
- Supervised Learning: Supervised learning uses labeled data that includes inputs and rectified outputs to train models.
- Unsupervised Learning: Unsupervised learning uses unlabeled data to train models. In which the output variable is unknown. Therefore, the models need to learn from the data, discover patterns, and provide the desired output.
- Reinforcement Learning: In reinforcement learning, algorithms need to learn from their environment, like human beings. It gets favorable or unfavorable rewards based on the environment.
Who uses machine learning and how do they use it?
Because machine learning extracts knowledge from existing data then makes decisions or takes action based on past data, industries with a large volume of data use ML technology for various purposes such as preventing fraud, assessing data in real time, and suggesting actions based on previous decisions.
Significant application areas of ML include:
- Image recognition: One of the best uses of ML application is to recognize images of places, things, objects, and more.
- Financial services: Banks and other financial service providers use ML to detect and prevent fraud and to provide insights on investment opportunities.
- Healthcare: ML technology allows health care professionals to assess the real-time condition of their patient’s health.
- Marketing and sales: Marketing teams use ML to analyze the past performance of their outreach efforts so they can create effective marketing campaigns and customer segmentation to improve sales.
- Education: ML identifies students who are struggling to learn and suggests necessary actions teachers can use to improve their learning
- Product recommendation: E-commerce industries use ML to analyze the purchase history of customers. It helps to identify their product of interest and to add with their inventory lists.
- Language translation: ML applications help to convert text from any language to the user’s known language.
- Social media: ML has numerous applications in social media for social media monitoring, sentiment analysis, image recognition, and chatbots.
- Web search engines, recommendation systems, online ad placement, email spam filters, and many other applications are additional examples of machine learning.
How does machine learning work?
By taking the healthcare industry as an example, understanding the working process can be easy. The three main steps include:
- Training: Train the models to identify diseases by providing the definitions to parameters of every disease as input.
- Validation: Validate the outcome by checking whether the ML predictions are positive or negative while diagnosing diseases.
- Testing: It’s essential to check the trained set of algorithms for a disease fit for new patient data.
Key features of ML
- Automation: Businesses can use ML to automate the most repetitive tasks such as paperwork and email sending.
- Data visualization: ML helps businesses visualize and analyze the data to understand the relationships in data and provide new business insights.
- Customer engagement: ML enables businesses to have valuable conversations with their customers by sourcing the information in which customers will be interested based on previous engagements.
- Accurate data analysis: ML’s data-driven models and algorithms process and analyze massive volumes of data in real-time.
- Business Intelligence: The wide use of ML technology in all verticals improves business efficiency.
What are the benefits of using ML applications?
- ML has a wide range of applications for businesses and organizations to revolutionize the way businesses work.
- ML holds the ability to automate everything by allowing algorithms to learn, predict, and improve on their own.
- ML can analyze large volumes of data to identify the trends and patterns of consumers.
- ML algorithms can continuously learn from their environment and provide more accurate predictions.
Origin of the phrase
“Machine learning” was first defined in 1959 by Arthur Samuel, a pioneer in the field of artificial intelligence and machine learning. Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”.