Getting to know ML

Machine learning is like teaching a computer how to learn from examples and make decisions on its own. Imagine you have a friend who loves to identifies pictures of different fruits. At first, you show your friend different pictures of red apples. Your friend learns from these examples and starts to recognize the differences between different red apples. Later, you show your friend new pictures of green apple, and they can tell you if it's still an apple based on what they learned from the previous examples. That's how machine learning works!

Image source: IntelliPaat

For these ML algorithms, the more examples they see, the better they get at making accurate decisions without explicit programming, just like your friend learning to distinguish between different colours of apple. However, unlike humans who needs years of gathering experience in a particular task, such as image recognition, ML learns to do this in just a matter of hours using quality data.

Categories of ML

In ML, the type of data we have and the problem we plan to solve largely determines the type of ML to use. Watch this video from IBM on different categories of ML.


At the high level, we have 3 broad categories of ML.

  • Supervised learning
  • Unupervised learning
  • Reinforcement learning

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Applications of ML

ML is a powerful tool that can be used to solve a wide variety of problems. As machine learning technology continues to develop, we can expect to see even more innovative applications of machine learning in the future. Here are some other examples of machine learning:

  • Spam filtering
  • Fraud detection
  • Weather prediction
  • Medical diagnosis
  • Recommendation systems
  • Image and speech recognition
  • Predictive maintenance.
  • Natural language processing
  • Self-driving cars
  • Environmental Monitoring

Now, let's understand a use case of day-to-day application of ML in solving everyday problems. The video below talks us through finding personal moments in videos and other applications of ML.

👩🏾‍🎨 Practice: Categories of machine learning... 🎯

There are 3 problem set provided below. Based on your understanding of ML and its categories, classify each of the problem set into one of supervised, unsupervised, and reinforcement learning.

  1. You're given a dataset of patient medical records, including symptoms, test results, and final diagnoses. Your task is to develop an ML model to predict whether a patient has a certain medical condition based on their symptoms and test results. What type of ML problem is this?

  2. Imagine you're building an autonomous car that needs to navigate through a simulated city. The car learns to drive safely and efficiently by receiving rewards for following traffic rules and penalties for collisions. What suitable ML problem type is this?

  3. You have a dataset of patient medical records, including symptoms, test results, and final diagnoses. Your task is to develop an ML model to predict whether a patient has a certain medical condition based on their symptoms and test results. What suitable ML problem type is this?


➡️ Next, we'll look at supervised learning... 🎯