Introduction to Machine Learning I Machine Learning- Part 1

//Introduction to Machine Learning I Machine Learning- Part 1

Introduction to Machine Learning

Introduction to Machine Learning

Introduction to Machine Learning

What is Machine Learning?

Machine learning is the brains behind Artificial Intelligence (AI technologies), and AI technologies do the actions.

More technically, it is the process of applying algorithmic analytical models to pre-processed data in iterations to facilitate the discovery of hidden patterns or trends that are useful for making predictions.

In machine learning, we usually break your data into test and training sets. We use random sampling to generate random samples and then break the data into these sets:

  1. Training set- 2/3rd of your dataset
  2. Testing Set- Remaining 1/3rd of your dataset

Training set is used to train your model while testing set is used to see how well the model performs.

Machine Learning can be further categorized into:

  1. Supervised Learning: Make predictions from labeled data

Spam Detection is a great example of Supervised Learning.

You go into your inbox, and you get an email that you know is spam, so you mark it as spam. After doing that several times, your email service provider starts making predictions for you, based on the characteristics of incoming emails and moving them into the spam box.

  1. Unsupervised Learning: Make predictions from unlabeled data

For unsupervised machine learning, an analogy would be, say, you have a house to put on sale, but you’re not sure what price

What you could do is- Take some old historical records, and based on key features like the number of rooms in the house, the square footage, acreage of the property etc., you can see what price those houses sold for, and then, based on that analysis, you could make a prediction what your house might sell for.

Machine Learning Use Cases

  • Sales forecasting
  • Customer segment analysis
  • Insurance claim fraud detection
  • Hedge fund classification

You may also like reading Factor Analysis and Principal Component Analysis (PCA) in Machine Learning.

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