What is machine learning to a kid?

 

As a kid, you must be excited to know about machine learning, as the name suggests it is a process of teaching machines to help them learn new things with the help of certain rules or by its own, exactly the way we humans learn new things.

The Definition

Let us first understand that machine learning is a process by which includes two things, the first part is a machine and the other part is learning. Now, we must focus on the word learning which is a process of identifying and classifying objects based on certain features and characteristics. 

How it works

We can teach machine certain rules and systems of classification. Based on the rules, the machine learns to identify and classify objects based on certain characteristics which it has learned. 

How the machines are trained

Training of the machines is done with the help of suitable examples over a period of time. This particular process of training the machine in order to clearly identify and act according to the requirement of the user can be defined as machine learning. For example, if you want your computer to identify your friends by name what you need to do is that at first, you need to train your computer with the pictures of your friend and the characteristics of each of your friend along with their name. 

 Why we need more examples

 The computer will learn with the examples that you have set, and then next time a friend comes to your home, the computer can identify and classify your friend and successfully name them. As more examples are fed into the system, more accurate classification and identification of your friends become easier for your computer. This particular process of helping the computer learn is defined as machine learning.

 The Next Step

 Now, you may be curious to know about the different types of machine learning which we will discuss subsequently. If you are interested to know more about machine learning, you can mail at smartsubu2020@gmail.com.

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What is the difference between KNN and K means clustering?

 

We are already aware of supervised and unsupervised machine learning algorithm as discussed in our previous sections. Let us understand K means clustering first. It is an unsupervised machine learning algorithm used for clustering where the algorithm is trained to divide the whole data set into different clusters as decided by the user. K is the number of predefined clusters.

The number of clusters that the algorithm is supposed to form is defined by the k-means clustering.

Whereas, KNN is K Nearest Neighbour algorithm, it is a supervised learning technique used for regression or classification. The algorithm is trained to identify any random unit and after that, the algorithm tries to locate the nearest neighbours based on the association of characteristics.

The number ‘K’ represents the number of nearest neighbours that the particular algorithm is supposed to find.

If You are Interested to know more about Machine Learning Algorithms, You can mail to smartsubu2020@gmail.com.

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What is Artificial Intelligence?

Over a period of time, Artificial Intelligence has influenced life and industry equally. It has acted as the main driver of emerging technologies, like the Internet of Things (IoT), Robotics, Big Data and continues to act as a technological innovator to rule the future.

Machine learning along with computer vision and deep learning algorithms have made a big stride in the technological empowerment, wherein, the future looks completely dominated by this technological advancement.

With simple devices like smartphone, it is possible to incorporate the power of technological advancement and the artificial algorithm coupled with machine learning capacities miracles can happen overnight.

Today, the software as well as technological advancement has made it possible to virtually make any device at home with available resources without much sophistication. It is also possible to design systems which can start to learn from each other and in the process of learning evolve and be better and better.

What is very pertinent in this time period is the requirement of incorporating the learning and technical skills in the mindset of the people so as to become future-ready.

 In the ecosystem of artificial intelligence it is possible to design systems and procedures where in each of the stakeholders on board will be able to learn and help each other in a symbiotic way and develop the whole ecosystem.

If you are interested to know more about Artificial Intelligence, you can mail at smartsubu2020@gmail.com.

 

 

 

 

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What is Internet of Things (IoT)?

In simple terms, the Internet of Things can be defined as a collection of interconnected objects, equipment, workplaces or business. We need to understand first, why we need IoT? It is because IoT can provide an advanced level of services, it can be made responsive and can be tailor-made for building smart cities, smart homes and smart agriculture.

Technically IoT are embedded systems with basic computing system (with embedded processors and systems).

The IoT operates with the unification of technologies like low power embedded systems, big data, machine learning, cloud computing and networking.

Two approaches are used, one is based on expanding the existing network and the other by building a new network from scratch.

Different types of technologies like RFID, Nano Technology, Sensor & Actuators and smart networks or communication paradigms are used for the functioning of IoT.

IoT devices for proper functioning should have the following characteristics

• ·        
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• ·         An abundance
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In a nutshell IoT consist of Sensors along with smart systems having an application layer acting as a user interface.

The building block of IoT consist of Sensors ( senses signal from the environment), Network ( the sensed information is passed through the network ) and Actuators ( which perform an action based on the input received from the sensors).

Enjoy the video on Managed Services - Supply chain transformation with IOT and Blockchain.

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Logistic Regression

 

It is important to understand the different machine learning algorithms. Keeping abreast with the understanding of the algorithm can help the data science enthusiast understand their problem, their data set and the application procedure to derive the intended results.

We will start our discussion with Logistic Regression

Logistic regression is one of the popular mathematical modelling procedure which is used in many of the data analysis algorithms. It is basically a regression analysis where the value of the outcome variable (dependent variable) is restricted between 0 and 1. In order to achieve this, a logistic function is used which is the mathematical function on which the logistic model is based. The beauty of the logistic model is that the desired output value can be easily truncated within the comprehendible range of 0 and 1 irrespective of the range of output value.

If you are interested in how to apply logistic regression and the mathematical intuition behind it, you can express your interest in joining the data science boot camp for free by email to

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What is a Manipulator?

By manipulator, we mean that it is a robot with a fixed base. This manipulator could be either serial manipulator or parallel manipulator.

As we change the program of a robot, it can be made to do multiple tasks. And, it is multifunctional, that means, the same robot, the same manipulator can perform the different types of machining operations. It can do some sort of peak and place type of operation, and so on. Now, here actually, we are using the term: manipulator.

In CNC machine-like Computerized Numerical Control Machine, we can perform a variety of tasks by changing the program.

Similarly, in robots, the same robot can be used to serve a variety of purposes, simply by changing the program. But, here, there is a basic difference between the level of re-programmability, which can be achieved by a robot, and that can be achieved by a CNC machine.

It is important to note, that the level of re-programmability, which can be achieved by a robot is more compared to that of the CNC machine. And, that is why a CNC machine is not a robot. 

If You are Interested in Robotics, You can get mail at smartsubu2020@gmail.com.

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What is the process of deployment of models?

In simple terms, deployment of any machine learning model should start from identifying the application that we want to build.

Second, we should understand the data requirements for the model and then gather the data. After that, we need to identify our model and train the model with the data and test for accuracy of prediction of the model.

After that, we need to create a new web application using the appropriate framework, for example, the most common Python-based web application framework is Flask. Then, we need to complete the code or rather put the code in some repository like GitHub.

We need to create an account in any of the service providers specialized in providing a platform as a service and the common being Heroku. After that, we need to link the account that we have created in Heroku with that of the GitHub.

Heroku is a free platform which can be used to you create the API for the machine learning model that we have created. We need to understand that there are requirements of the certain libraries for running the code in the Heroku platform and environment.

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What are the different types of machine learning?

We hope you have understood about machine learning and if you have not gone through the post on machine learning, kindly  

As the name goes supervised learning is just like the learning process used in schools, where there are a set of instructions given by our teacher and we are asked to perform a certain specified task. But, in case of machine learning the training data set is provided to the computers, and the user acts as a teacher where the algorithms are designed  to give a very specific output (just as the students).

The machines are trained on the predefined data set before it can be used to make any decisions based on new data. For example, the machines are set to identify certain objects based on very specific characteristics which are very unique to that particular object.

Suppose a machine is trained on identifying whether a particular creature is a dog or cat. Specific features of dog and cat are fed to the computer during the training process and levelling of the pictures are done so that whenever new characteristics or features are fed into the computer it is able to correctly identify those features and come up with the outcome of whether it is a dog or cat.

The second type of learning is unsupervised learning where the machines are given a particular data set and left to learn on its own. To simplify, the machine is fed with data and the algorithms by itself tries to find pattern and features in the data set and put them into different categories by identifying patterns and correlation within the units of the data set.

It can be mostly said as a self-study process, where the machine learning algorithm is put into its own to identify and act by identifying relationship among the features of the different clusters. Clustering is one of the most commonly used unsupervised machine-learning techniques.

The Third category of machine learning is reinforcement learning, where the machines are left to learn by itself, but, here the algorithms of learning are rewarded or penalized based on the actions that they perform. For example as a child if you do good things you are rewarded, and hence, you are encouraged to perform that action again and again, whereas if you are given punishment for a particular act of yours you tend to not repeat it.

Similarly in reinforcement learning the algorithm is rewarded when it performs the correct task and thus the algorithm learns and tries to repeat the same task again and again. Whereas if penalized for a task it tries not to repeat those task.

In this reinforcement learning process, the machine learns by the trial and error method. In terms of machine learning that rewards and penalties are decided based on the predefined actions which the machine needs to perform in interaction with its environment.

If you are interested to know more about machine learning, you can mail at smartsubu2020@gmail.com.

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What are List and Tuples in Python?

In Python, List and Tuples both are data structures. In case of List, we can include integers, as well as characters, in the data structure and the values in the List, can be edited or altered, in technical terms, it is known as mutability.

Whereas in the case of Tuples, characters and integers are included, but those cannot be edited, hence only they are immutable. But, the question which comes in mind is that if List and Tuples have the same characteristics of data structure, why we need two types of data structure.

Why we need two types of data structure

When there is a requirement of a fixed data structure which cannot be altered throughout the scripting of the program we can safely use Tuple. But, if we need a dynamic data structure which can be altered and edited as required during the scripting process of the programming, List is more preferable.

 So in order to meet the requirement of the programming and to make the scripting efficient, either List or Tuples are used. Tuples are preferred in a memory-intensive task, whereas, List is preferred in case of buffers. When we are sure about the number of columns in a data structure we should select Tuple and when we are not we should go for List.

If you are interested in Computer Programming, You can mail at smartsubu2020@gmail.com.

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