Artificial Intelligence Vs Machine Learning
AI(Artificial Intelligence) application is able to do its own task without any human intervention
Ex: Netflix→ Recommendation system, Self Driving cars
ML(Machine Learning) is a subset of Artificial Intelligence (AI) that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed for every task.
Core Idea Of Machine Learning:
Learning from Data:
ML algorithms analyze historical data to identify patterns.
Making Predictions:
Once trained, models can predict outcomes for new, unseen data.
Improvement Over Time:
Models improve as they are exposed to more data.
Key terms of ML:
Features, labels, training data, testing data, overfitting, underfitting
Features
Features are the inputs — the variables or columns you use to make predictions. For example, in a housing dataset, features might include:
Number of bedrooms
Square footage
Location
Age of the house
Label
Label(also called target) is the output — the value you're trying to predict. In the housing example, the label would be:
House price
🔧Training Data
This is the data you use to build your model.
The model learns patterns from this data.
Example: If you're predicting house prices, the training data includes houses with known prices.
🧪Testing Data
This is new data the model hasn’t seen before.
You use it to evaluate how well your model performs.
It helps you check if the model can make accurate predictions on unseen data.
👉 Think of it like studying for an exam:
Training data = practice questions
Testing data = actual exam questions
🎯 Overfitting
The model learns too much from the training data — even the noise and random patterns.
It performs very well on training data but poorly on testing data.
Like memorizing answers instead of understanding concepts.
💤 Underfitting
The model is too simple to capture the patterns in the data.
It performs poorly on both training and testing data.
Like not studying enough and guessing on the exam.
ML Algorithm
An algorithm is a set of rules or instructions for solving a problem.
In machine learning, an algorithm is the method used to train a model.
Examples: Linear regression algorithm, decision tree algorithm, logistic regression algorithm.
ML Model
A model is the result you get after applying an algorithm to your data.
The model is what you use to make predictions.
Example: After training a decision tree algorithm on your housing data, you get a decision tree model that can predict house prices.
Simple Analogy
Algorithm: Like a recipe for baking a cake.
Model: The cake you get after following the recipe with your ingredients.
When We Say “Choosing the Right ML Model”
We mean: Choosing the right type of model and the right algorithm to create it.
For example, if you want to predict a number, you might choose a linear regression model (using the linear regression algorithm which we learn in coming posts!!).
If you want to predict a category, you might choose a decision tree model (using the decision tree algorithm which we learn in coming posts!!).
You choose an algorithm based on your problem and data.
The trained model is what you use to make predictions.
🍿 That was the trailer — the next post’s the feature film. Grab your snacks and dive in! 🎬📖 https://dev.to/codeneuron/types-of-machine-learning-algorithms-4h55
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