The purpose of these demos is to just illustrate the power or Machine Learning. Some of these demos are solved behind the scenes using Deep Learning ( which is a specialization in Machine Learning that uses a technology called Neural Networks ) which we will not be learning in this course. However, most of the fundamental principles remain the same

Google vision is based on Google's proprietary image recognition algorithms and can easily identify all the objects in an image.

Google Quickdraw is an experimental project hosted by Google that recognizes pictures based on your doodles. You can view many more such AI experiments here.

Predicting house prices is a daily job for real estate consultants. There are a whole lot of parameters that are used in determining the price of a house - like

  • Number of rooms
  • pollution
  • traffic
  • crime
  • etc

Most of the time, the consultants use some kind of mental heuristics based on their experience to come up with a price. This is a very good place to use Machine Learning as there are no precise rules. There basically are no rules that can be formulated. Here is a 50 line python file.

If you run this file you should be able to see a prediction of the house prices based on just 1 parameter - Number of rooms. For example, here is a quick snapshot of some of the rows of data.

      CRIM    ZN  INDUS  CHAS    NOX     RM  ...    RAD    TAX  PTRATIO       B  LSTAT  PRICE
0  0.00632  18.0   2.31   0.0  0.538  6.575  ...    1.0  296.0     15.3  396.90   4.98   24.0
1  0.02731   0.0   7.07   0.0  0.469  6.421  ...    2.0  242.0     17.8  396.90   9.14   21.6
2  0.02729   0.0   7.07   0.0  0.469  7.185  ...    2.0  242.0     17.8  392.83   4.03   34.7
3  0.03237   0.0   2.18   0.0  0.458  6.998  ...    3.0  222.0     18.7  394.63   2.94   33.4
4  0.06905   0.0   2.18   0.0  0.458  7.147  ...    3.0  222.0     18.7  396.90   5.33   36.2

As you can see, there are so many columns here and the last column is the house price in thousands of dollars. This is data from the 1970's. So the actual prices are very low compared to modern day prices. However, we can still learn a lot from this simple dataset.

Here is a plot of the number of rooms vs the price of the house.

As you can see from the plot, there is no relationship between the number of rooms and the price that we can identify. It seems random. However, if we were to approximate the relationship, we can see 2 things.

  • As the number of rooms go up, the price of the house goes up too.
  • The relationship seems linear in nature

Under these assumptions, we can run the program to determine a straight line that approximates the relationship between the number of rooms and the house price. This is how that prediction would look like.

The red line is what Machine Learning has predicted. Of course it does not account for all the data points - it is just an approximation. However, it is an intelligent approximation. It is not just random line. There is a bit of math behind it.

How did ML solve the problem ? In this case, ML uses a statistical method called Linear Regression that approximates a linear correlation between the predictors and response variables. The predictor being the number of rooms and the response being the house price. We are not limited to just one predictor - we can use any or all of the predictors that we have seen above - crime rate, pollution, taxes etc.

The point I want you to take away is that fundamentally this is a fuzzy problem to solve using traditional IF ELSE computing model. In this specific case, we are taking the help of statistics to solve the problem using approximations. Statistics just happens to be a major tool used in the field of ML. However, ML is not limited to using statistics. The key being, data. As long as there is an algorithm that can use existing data to predict new data points, the field of ML is more than happy to use it. While the goal of traditional statistics is to summarize and analyze existing data, the goal of machine learning is to learn from data.