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Machine Learning – Introduction

Today’s Artificial Intelligence (AI) has far surpassed the hype of blockchain and quantum computing. This is due to the fact that huge computing resources are easily available to the common man. The developers now take advantage of this in creating new Machine Learning models and to re-train the existing models for better performance and results. The easy availability of High-Performance Computing (HPC) has resulted in a sudden increased demand for IT professionals having Machine Learning skills. In this tutorial, you will learn in detail about: 
  • What is the crux of machine learning? 
  • What are the different types of machine learning? 
  • What are the different algorithms available for developing machine learning models? 
  • What tools are available for developing these models? 
  • What are the programming language choices? 
  • What platforms support the development and deployment of Machine Learning applications? 
  • What IDEs (Integrated Development Environment) is available? 
  • How to quickly upgrade your skills in this important area? 

Machine Learning – What Today’s AI Can Do?

When you tag a face in a Facebook photo, it is AI that is running behind the scenes and identifying faces in a picture. Face tagging is now omnipresent in several applications that display pictures with human faces. Why just human faces? There are several applications that detect objects such as cats, dogs, bottles, cars, etc. We have autonomous cars running on our roads that detect objects in real-time to steer the car. When you travel, you use Google Directions to learn the real-time traffic situations and follow the best path suggested by Google at that point in time. This is yet another implementation of the object detection technique in real-time. Let us consider the example of the Google Translate application that we typically use while visiting foreign countries. Google’s online translator app on your mobile helps you communicate with the local people speaking a language that is foreign to you. There are several applications of AI that we use practically today. In fact, each one of us uses AI in many parts of our lives, even without our knowledge. Today’s AI can perform extremely complex jobs with a great accuracy and speed. Let us discuss an example of the complex task to understand what capabilities are expected in an AI application that you would be developing today for your clients. 

Example 
We all use Google Directions during our trips anywhere in the city for a daily commute or even for inter-city travels. Google Directions application suggests the fastest path to our destination at that time instance. When we follow this path, we have observed that Google is almost 100% right in its suggestions and we save our valuable time on the trip. You can imagine the complexity involved in developing this kind of application considering that there are multiple paths to your destination and the application has to judge the traffic situation in every possible path to give you a travel time estimate for each such path. Besides, consider the fact that Google Directions covers the entire globe. Undoubtedly, lots of AI and Machine Learning techniques are in-use under the hood of such applications. Considering the continuous demand for the development of such applications, you will now appreciate why there is a sudden demand for IT professionals with AI skills. In our next chapter, we will learn what it takes to develop AI programs.

Machine Learning – What is Machine Learning? 

Consider the following figure that shows a plot of house prices versus its size in sq. ft. 



After plotting various data points on the XY plot, we draw a best-fit line to do our predictions for any other house given its size. You will feed the known data to the machine and ask it to find the best fit line. Once the best fit line is found by the machine, you will test its suitability by feeding in known house size, i.e. the Y-value in the above curve. The machine will now return the estimated X-value, i.e. the expected price of the house. The diagram can be extrapolated to find out the price of a house which is 3000 sq. ft. or even larger. This is called regression in statistics. Particularly, this kind of regression is called linear regression as the relationship between X & Y data points is linear. 

In many cases, the relationship between the X & Y data points may not be a straight line, and it may be a curve with a complex equation. Your task would be now to find out the best fitting curve which can be extrapolated to predict the future values. One such application plot is shown in the figure below.


 Source: https://upload.wikimedia.org/wikipedia/commons/c/c9/Segmented_linear_regression_graph_showing_yield_of _mustard_plants_vs_soil_salinity_in_Haryana%2C_India%2C_1987%E2%80%931988.jpg 

You will use the statistical optimization techniques to find out the equation for the best fit curve here. And this is what exactly Machine Learning is about. You use known optimization techniques to find the best solution to your problem. Next, let us look at the different categories of Machine Learning. 
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