An Overview of Machine Learning
In today’s AI-driven world, everything is transforming to reduce human effort and manual work. But what exactly is AI?
Artificial Intelligence (AI) is the science of creating machines that can think and act like humans. It simulates human cognitive abilities—such as learning, reasoning, and problem-solving—to make tasks easier and more efficient.
Machine Learning is a subset of Artificial Intelligence (AI) where machines learn from data, analyze patterns, make predictions, and ultimately take decisions.
How it works?
- Data Collection – Provide the model with examples (input + output). Example: If the input is A, the output is C.
- Training – In this phase, algorithms are used to find patterns and relationships in the data to make predictions.
- Prediction – When new or unseen data is given, the trained model provides predicted outcomes based on what it has learned.
- Improvement – The more data we provide for training, the better and more accurate the outcomes become.
Example :- Predicting Student Exam Scores
- Data Collection
Past data of hours studied vs exam scores:
| Hours Studied | Score |
|---|---|
| 2 | 40 |
| 4 | 60 |
| 6 | 80 |
| 8 | 100 |
- Training
The model analyzes the pattern:
“Every 2 extra hours of study increases score by 20 points.”
- Prediction
If a student studies 5 hours, the model predicts:
- Base: 4 hours → 60 points
- Extra 1 hour → 10 points
- Predicted score = 70 points
- Improvement
The more data you give (like previous grades, sleep hours, class participation), the more accurate the predictions become.
🔹 Real-World Examples
- Netflix recommending movies 🎬
- Gmail detecting spam emails 📧
- Self-driving cars 🚗
- Predicting stock prices 📈
Thank you for reading the blog! I’m excited to announce that I’ll be creating a complete course on Machine Learning. Stay tuned!
