Machine Learning (ML) is one of the most exciting fields in technology today. But what exactly is it? In this introductory series, we will break down the core concepts of ML, starting from the very basics.
What is Machine Learning?
Wikipedia defines machine learning as:
“The scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task.”
In simple terms: Data -> Train -> Results.
Unlike traditional programming where you write explicit rules (If X then Y), in machine learning, you provide the data and the computer finds the patterns itself.
Relationship with Data Science
Machine learning and Data Science are closely related but not identical. Data Science is a broad field that involves: * Collecting data * Cleaning and processing data * Analyzing data to find insights * Applying formulas and machine learning models
ML is a key tool used by Data Scientists to automate the predictive part of their work.
Relationship with Statistics
Machine learning is essentially 100% applied mathematics, with Statistics playing the most critical role. While a statistician might focus on understanding the relationships between variables, a machine learning engineer uses those same statistical principles to build models that can repeat calculations faster and more accurately than a human ever could.
Real-World Applications
You encounter machine learning every day: * Image Recognition: Identifying faces in photos or digits on envelopes. * Recommendation Systems: How Netflix or Amazon suggests what you might like next. * Speech Recognition: Powering assistants like Siri, Alexa, and Google Assistant. * Fraud Detection: Banks identifying unusual spending patterns to stop criminals.
The Three Main Types of Machine Learning
There are three primary ways that machines learn:
- Supervised Learning: The machine is trained on labeled data (it knows the answers during training).
- Unsupervised Learning: The machine looks for patterns in unlabeled data.
- Reinforcement Learning: The machine learns by trial and error, receiving rewards or penalties.
In the next part of this series, we will dive deeper into Supervised Learning.
Written by
Abdur-Rahmaan Janhangeer
Chef
Python author of 7+ years having worked for Python companies around the world
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