Outline

Introduction

Do you know the word Machine Learning(ML) was first introduced by Ahmed Ibn Musa Ibn Shakirs, one of the greatest writer . He gave the idea that machines are able to learn. He mentions this idea in one of his great books called “Ingenious Devices” and this book was published in  850.

The book describes about one hundred devices and how to use them. Humans started to discover ML from that day but the idea about ML is not much in details, they only knew about what a machine is? What are their parts and how they work? After that many discoveries took place in the field of ML.

For example,

1950 - Alan Turing creates the Turing Test” to determine if a computer has real intelligence to pass the test a computer must be able to fool a human into believing it is also human.

1952 - Arthur Samuel wrote the first computer “Learning Program”.

1957 - Frank Rosenblatt designed the first Neural Network”..

1981 - Geral Dejong introduces the concept of “Explanation Based Learning(EBL)” in which a computer analyses framing data.

1990 - work on ML shifts from aKnowledge-Driven to a Data-Driven Approach”.

2006 - Geoffery Hinton coined the term Deep Learning” to explain new algorithms that let computers “See” and “Distinguish” objects, text in image and videos.

2011-”Google Brain” is developed and it’s deep Neural Network can learn to discover and categories objects much the way a cat does.

These are some discoveries that were held in past years. There is also a new word which is commonly used in ML “Neural Network”. Here the question comes in mind: What is the Neural Network? Is it related to ML?

Before you are going in depth  about the Neural Network let’s have a look towards the definition of ML, which will help you to  get more understanding about the  Neural Network.

“ML is the one of the popular applications of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so ML algorithms use historical data as input to predict new output values. It is also a process in which computer software and devices perform via cognition (very similar to the human brain).

Neural Network

The word Neural Network that we discuss above is the process through which ML is taking place or an Artificial Neural Network is a function of deep learning which mimics the behavior of the human brain to solve complicated data problems.Well Neural Network takes place in our daily life for example, face recognition, speech recognition and in marketing these functions are performed by Neural Network.

Deep Learning is part of ML and these systems work under the umbrella of AI. A Neural Network functions when some input data is fed to it. This data is then processed via layers of perception to produce a desired output.

Programming Languages Use In ML

With the passage of every new day we find new technology trends. I did a detailed research of various papers and online forums on the internet to find the best programming languages for ML.

Here are some of them.

  1. Python
  2. R
  3. C++
  4. C#
  5. Java

Python:  Python is very popular language for ML. In the recent years Python become one of the fast growing programming languages that got the support of Machine via a variety of libraries and tools. Python is the first choice of ML developers.

Now you may wonder why Python has gotten that much attention in a very short span of time? On of the biggest reasons for popularity is that Python is easy to use, nobody likes complicated things and so the ease of using makes it popular fro a crowd of other languages. The simplicity of Python means that developers can focus on actually solving the ML problems rather than spend all their time and energy to understand just the technical nuances of the language.

Another reason is, Python is also supremely efficient, it allows developers to complete more work using fewer lines of code. Python code is also easily understandable by humans, which makes it ideal for making ML models with all these advantages.

R: According to a survey in 2015, R is the most powerful ML programming language and used by the top data scientists. R is powerful because of the breadth of techniques it offers. Any techniques that you can think of for data analysis, visualization, sampling, supervised learning and model evaluation are provided in R.

One of the best reasons why R has so many techniques is because academics that develop new algorithms are developing in R and releasing R packages, it is also a free tool to use because it is open source software you can download it right now for free and it runs on any workstation platform you are likely to use.

C++: Many ML platforms support C++. ML algorithms need to be fast and well coded, the thing with C++ is that you can implement it on computer vision and ML systems from groped-up. C++ gives you access to direct pointer manipulations features such as choice of memory management system.

C#: C# can be used for ML applications via a NET core ML platform. NET is a cross-platform open-source ML framework that makes ML accessible to NET developers. ML allows NET developers to develop their own models.

Java: Java is an incredibly useful, speedy and reliable programming language that helps development teams to build a multitude of projects from data mining and data analysis to the building of ML applications.

Sub - Categories of ML

There are three basic sub-categories of ML

  1. Supervised Learning (Train Me)
  2. Unsupervised Learning(I am Sufficient in Learning)
  3. Reinforcement Learning(My Life My Rules)

Supervised Learning (Train Me)

Supervised Learning is the stage of ML where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine.

One the model gets trained it can start making a prediction or decision when new data is given to it. For example supervised learning acts like a baby who can identify other objects based on past data which feeded by the environment. That’s why you can call it “Train Me”.

Unsupervised Learning (I am Sufficient in Learning)

The model learns through observation and finds structures in the data which we give input. Once the model receives input details, it automatically finds patterns and relationships in the input dataset by creating clusters(groups of similar things) in it and giving detailed dataset of output.

Suppose we presented images of apples, bananas and mangoes to the model , so what it does is based on some patterns and relationships it creates clusters and divides the similar into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.

Reinforcement Learning (My Life My Rules)

It is the ability of an agent to interact with the environment and find out what is the best outcome.

It follows the concept of “Hit and Trial” method. The agent is rewarded or penalized with a point for correct or a wrong answer and on the basis of the positive reward points gained the model trains itself and again once trained it gets ready to predict the new data presented to it.

For example, video games at first level when we start a game and win that level we are then moving towards the next level. The input data “game” when we play and win it is the reward for that dataset and predicate to move on to the next level.

Uses of ML

Image Recognition: The image recognition is a most common use of ML applications. It can also be referred to as a digital image and for these images, the measurement describes the output of every pixel in an image. Face recognition is also one of the great features that have been developed by ML. It helps to recognize the face and send the notifications related to that person.

Voice Recognition: ML also helps us in developing the application for voice recognition, it is also referred to as Virtual Personal Assistants(VPA). It will help you to find the information when asked over voice.

Predictions: It helps in building the applications that predict the price of cab or travel for a particular duration and congestion of traffic where it can be found. While booking the cab and the estimates the app approximate price of the trip that is done by the uses of ML.

Social Media Platform: Social Media is being used for providing better news feed and advertisement as per the user interest is mainly done through the use of ML only. Friend suggestions for Facebook, songs, videos are work through ML.

Conclusion

Well all the above information shows that ML is a basic part of upcoming technology, they provide us new ways in the technical fields. Although ML is still an ongoing process and many changes are still taking place in it. Well let’s see what will come to see next.