Arthur Samuel, a pioneer in the field of artificial intelligence and computer gaming, defined machine learning as:
Field of study that gives computers the capability to learn without being explicitly programmed.
In a very layman’s manner, machine learning is like teaching computers to learn by themselves. We do this by giving them good data and different techniques. They learn from this data without being told what to do step-by-step. We choose which techniques to use based on what kind of data we have and what we want the computer to do.
Machine learning is a subset of artificial intelligence that allows computers to learn from examples and improve themselves without being explicitly programmed. It combines data and statistical tools to make predictions that can be used to gain insights. The machine receives input data and uses an algorithm to provide accurate results.
A common example of machine learning is the personalized recommendations provided by Netflix based on a user's viewing history. Tech companies use unsupervised learning to improve the user experience by personalizing recommendations.
ML is also used for a variety of tasks like fraud detection, predictive maintenance, portfolio optimization, automated task, and so on.
Specific areas where ML is being used:
Predictive modeling: machine learning can predict customer behavior or disease development
Natural language processing: machine learning can understand human language for voice recognition and language translation
Computer vision: machine learning can recognize and interpret images for self-driving cars, surveillance systems and medical imaging
Fraud detection: machine learning can detect fraudulent behavior in financial transactions and online advertising
Recommendation systems: machine learning can suggest products and content based on past behavior
Basic Difference between Machine Learning and Traditional Programming
Traditional Programming: We feed in DATA (Input) + PROGRAM (logic), run it on the machine, and get the output.
Machine Learning: We feed in DATA(Input) + Output, run it on the machine during training and the machine creates its own program(logic), which can be evaluated while testing.
[Source: Geeksforgeeks.org]
Challenges and Limitations of ML
The main limitation of machine learning is the absence or insufficiency of data.
A lack of diversity in the dataset can pose a challenge for the machine to learn.
A heterogeneous dataset is necessary for a machine to extract meaningful insights.
Algorithms may struggle to extract information from datasets with limited variations.
At least 20 observations per group are recommended to aid the machine in learning.
These limitations can lead to poor evaluation and prediction by the machine.
The significance of machine learning lies in its capacity to help computers learn from data and enhance their performance on specific tasks without explicit programming. This ability to learn from data and adapt to new situations makes machine learning valuable for handling tasks that involve vast amounts of data, intricate decision-making, and changing environments.
If you want to learn more in debt about machine learning, visit Geeksforgeeks.org.
Reminder: None of this is financial advice. Do your own research. Don’t trust. Verify.