Simplified Machine Learning: Part 1
Let me make your life easier by making you understand Machine Learning buzzwords in a very simple manner.
“Machine intelligence is the last invention that humanity will ever need to make.” — Nick Bostrom
This is article 1 of many in this series, stay tuned!
What is Machine Learning?
Machine Learning (ML), as the name implies, means machine learning on its own. It is a branch of artificial intelligence (AI) that allows computers to learn from data and make decisions without being explicitly programmed. Instead of following fixed rules, like fixed if-else statements for example, ML models recognize patterns in data by themselves and improve their performance over time.
Simply put, it’s like teaching a computer to perform a specific task without actually writing the code to do that task.
Applications of Machine Learning
You interact with ML daily, often without realizing it! Here are some common applications:
- Spam Filters: Email services use ML to detect and filter spam messages.
- Recommendation Systems: Netflix, YouTube, and Spotify suggest content based on your past preferences.
- Voice Assistants: Siri, Alexa, and Google Assistant understand and respond to your voice commands.
- Fraud Detection: Banks use ML to detect unusual transactions that might indicate fraud.
- Self-Driving Cars: These vehicles rely on ML to navigate roads and avoid obstacles.
Although there are many kinds of Machine Learning, two of them are the most sought after; Supervised Learning and Unsupervised Learning.
Supervised Learning
Supervised learning is a type of ML where the model is trained using labeled data. This means that for each input, we provide the correct answer (output) during training. The model learns from these examples and makes predictions when new data is given.
Example: Imagine teaching a child to recognize apples and oranges. You show labeled images of apples and oranges, and the child learns to identify them. Similarly, in ML, a model is trained with labeled data to make predictions.
Some common applications of supervised learning include:
- Image recognition
- Spam detection
- Credit score prediction
Unsupervised Learning
Unsupervised learning is different because it works with unlabeled data. So no labelled inputs and outputs are given, instead the model tries to find hidden patterns or groupings in the data without being given explicit instructions.
Example: Imagine giving a child a basket of mixed fruits without labels. The child may group them based on color, size, or shape. Similarly, ML models cluster similar data points without prior knowledge of categories.
Some common applications of unsupervised learning include:
- Customer segmentation for marketing
- Anomaly detection (e.g., fraud detection)
- Topic modeling in text analysis
Machine Learning is a vast and exciting field, and this is just the beginning! In the next article, we’ll explore more fascinating ML concepts in a similar way.
Stay tuned!
