Machine Learning (ML) is a type of technology that enables computers to learn and make decisions without being explicitly programmed.
Here are key points explained in simple words with a real-world example:
- Learning from Data:
ML systems learn from data patterns and examples, improving their performance over time.
2. Prediction and Decision-Making:
ML helps computers predict outcomes or make decisions based on the patterns it identifies in data.
3. Real-world Example:
Imagine teaching a computer to recognize cats. Instead of telling it explicitly what a cat looks like, you show it many pictures of cats. The ML algorithm learns from these pictures, identifies patterns like pointy ears and whiskers, and becomes capable of recognizing cats in new pictures it has never seen before.
4. Applications:
ML is used in various applications such as recommendation systems (like Netflix suggesting movies), speech recognition (like Siri or Google Assistant understanding your voice commands), and autonomous vehicles (where cars learn to navigate based on real-time data).
5. Feedback Loop:
ML systems often have a feedback loop – they make predictions, receive feedback on their accuracy, and then adjust to improve future predictions.
Differences between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)
Artificial Intelligence (AI):
Definition: AI refers to the development of computer systems that can perform tasks that typically require human intelligence.
Real-world Example: A smart home system that adjusts the temperature, lighting, and music based on your preferences and habits.
Machine Learning (ML):
Definition: ML is a subset of AI that focuses on giving computers the ability to learn and improve from experience without being explicitly programmed.
Real-world Example: An email spam filter that learns to identify and filter out spam by analyzing patterns in emails.
Deep Learning (DL):
Definition: DL is a type of ML that uses neural networks with multiple layers (deep neural networks) to analyze and learn from data.
Real-world Example: Image recognition technology in your phone’s camera that can identify objects or people by learning from a vast dataset.
To sum up:
The general idea behind AI is the development of intelligent machines.
ML is a branch of AI that specializes in making data usable for machines to learn.
DL is a branch of ML that deals with complicated tasks by leveraging deep neural networks.
Consider artificial intelligence (AI) as the broad canopy, machine learning (ML) as a tool that facilitates learning, and deep learning (DL) as an advanced ML technique that uses deep neural networks.
You can join the Free course provided by google using the link below.
Course link:– Machine Learning Crash Course
with TensorFlow APIs