Learning Machine Learning — How to Code without Learning Coding

The 5th generation of artificial intelligence

Why we still have to learn machine learning? It is because so far artificial learning is still at its 4th generation (Yiu, 2019). We have started from the 1st generation which focused on automation to the 4th generation which emphasizes machine learning. These two approaches are exactly the opposite. Automation requires a hard-wired instruction (rules) provided by the programmer. Machine Learning, on the contrary, tries to identify the rules from experience (training) (Figure 1). For example, last week we have tried to train the computer to differentiate two people by analyzing their photos (Yiu, 2021). However, we still have to take the initiative to train the machine to learn something, so that it can perform the task. The 5th generation of artificial intelligence is a self-learning machine. It is more like a human baby who can learn by himself or herself even their parents do not train. Robots are now trying to learn by itself. When the 5th generation of AI can be successful, we do not have to learn machine learning anymore.

Figure 1 Difference between traditional programming and machine learning. Source: https://medium.com/@ndang.nguyen94/machine-learning-3e833be54bdf

Google Colaboratory

Google has provided a user-friendly interface called Colab for us to run the libraries of coding for machine learning. Provided that there can have enough libraries for us to combine and adapt easily, then we can code without learning coding.

Figure 2 Google Colab Lab5 Using Convolutions with Complex Images. Source: https://colab.research.google.com/github/lmoroney/mlday-tokyo/blob/master/Lab5-Using-Convolutions-With-Complex-Images.ipynb#scrollTo=o-qUPyfO7Qr8
Figure 3 Another two libraries of Python coding.

An Introduction to Colab libraries

If you want to know how to adapt the libraries to fit your purposes, then you may have to learn Colab from the very beginning. Here is the simplest introduction to Colab coding — running a simple regression to predict Y from a linear polynomial of X.

Figure 4 Python libraries to run a simple regression at Colab.

Other libraries

If we do not want to learn how to code, then there must have sufficiently large number of existing libraries for us to combine and adapt to achieve our purposes. It may take much longer time for the developers to create more libraries, and provide a more user-friendly interface to adapt the libraries to different purposes. You may help to develop the libraries and the interface. Now there are quite a large number of libraries at Colab and Github, you may also help contribute to build more.



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ecyY — easy to understand why, easy to study why. Finding the truths scientifically is the theme.