Implementation of Swagger in webapi core 2.1

 Swagger Implementation:

• Create an Asp.Net Core 2.1 web api project and install Swashbuckle.AspNet core(5.0.0) nuget package.

• Swashbuckle comprises of 3 packages - A swagger generator, middleware to expose the generated  swagger as Json end points and middle ware to expose swagger-UI.

• They can be installed togather via Swashbuckle.AspNet core(5.0.0) meta package: Like:                                                                                                                                                           PM > Install-Package Swashbuckle.AspNetCore

• In Asp.net core 2.1, open Startup.cs file to add swagger service to middleware. Like:     

public void ConfigureServices(IServiceCollection services) { services.AddSwaggerGen(c => { c.SwaggerDoc("v1", new Info { Version = "v1", Title = "Example API", Description = "Example API", }); }); }

• And enable the Swagger ui in configure() method: Like

  public void Configure(IApplicationBuilder app, IHostingEnvironment env) { if (env.IsDevelopment()) { app.UseDeveloperExceptionPage(); } app.UseMvc(); app.UseSwagger(); app.UseSwaggerUI(c => { c.SwaggerEndpoint("/swagger/v1/swagger.json", "Example API"); }); }

This is all required to set up swagger. The one last thing to do change is, change in the application launch url, so that the swagger UI loads on launching. To set swagger UI as launch url, following steps to be as follows...

              1. Right click on the Asp. net core web application.
              2. And then click on properties.
              3. Navigate to debug tab.        

              4. On debug tab change Launch Browser value to "Swagger".

• Now run the application, now you should see the swagger UI like this:
  
     
  
  
  
          
• Swagger uses different colors for each HTTP verb to differentiate Api actions. Clicking on any method will give you details about the accepted parameters, return type and allows you to test         

 Swagger Versioning:

• In Asp.Net core 2.1 web api project, install Microsoft.AspNetCore.MVC.Versioning(2.1.0) nuget package.
• Then open Startup.cs file in your project, and then add the following code in Startup.cs file

• And then open the controllers in your application, add below lines of code in route level.

• Now run the application, you should see the versioning in swagger UI as follows:

Note: In our application, we use swagger versioning as one, here you can use versioning as per your requirement.

         

                                                                                                                                                                

Our Future Technology with AI

As one of the most prevalent and transformative technologies of all time, artificial intelligence (AI) has been incorporated in various major industries since the term was first coined in the 1950's. 

There are many new technological innovations that are changing how we live our lives, but artificial intelligence, or AI, may present the most exciting changes. While AI has been around for a while now, recent improvements have made the technology much more adaptable. Looking into the future, it’s easy to predict a world in which artificial intelligence plays a more significant role in our daily lives.

Getting around with AI:

           Self-driving cars are already beginning to make their way on the roadways, but we can expect this technology to advance considerably in the coming years. The U.S Department of transportation has started making regulations about the use of AI-driven vehicles and, as a result, they have designated three levels of self-driving vehicles. Currently, we’re at the lowest level with Google’s version of the vehicle, which still requires a human driver to be at the wheel. Ultimately, the goal is to create an entirely automated self-driving car, which is expected to be much safer. Logistics companies and public transportation services are also looking at incorporating AI technology to create self-driving trucks, buses, taxis, and planes.

AI and Robotics will come together:

   The field of cybernetics has already begun to associate with artificial intelligence and that trend is expected to continue. By incorporating AI technology into robotics, we’ll soon be able to enhance our own bodies, giving us greater strength, longevity, and endurance. While cybernetics may help us enhance our healthy bodies, the application of this technology is really aimed at helping the disabled. Those individuals, who have cut off limbs or permanent paralysis, can be given a much higher quality of life. Cybernetic limbs that can communicate with the brain can become almost as useful as natural limbs. In the future, artificial limbs may even become stronger, faster, and more efficient.

AI will help to create Humanoid Robots:

A lot of work, finances and research are put into making these humanoid robots. The human body is studied and examined first to get a clear picture of what is about to be imitated. Then, one has to determine the task or purpose the humanoid is being created for. Humanoid robots are created for several purposes. Some are created strictly for experimental or research purposes. Others are created for entertainment purposes. Some humanoids are created to carry out specific tasks such as the tasks of a personal assistant using AI, help computer visioning, and so on.

The next step scientists and inventors have to take before a fully functional humanoid is ready is creating mechanisms similar to human body parts and testing them. Then, they have to go through the coding process which is one of the most vital stages in creating a humanoid. Coding is the stage whereby these inventors program the instructions and codes that would enable the humanoid to carry out its functions and give answers when asked a question.

Although humanoid robots are becoming very popular, inventors face a few challenges in creating fully functional and realistic ones. Some of these challenges include:

·         Actuators: These are the motors that help in motion and making gestures. The human body is dynamic. You can easily pick up a rock, toss it across the street, spin seven times and do the waltz. All these can happen in the space of ten to fifteen seconds. To make a humanoid robot, you need strong, efficient actuators that can imitate these actions flexibly and within the same time frame or even less. The actuators should be efficient enough to carry a wide range of actions.

·         Sensors: These are what help the humanoids to sense their environment. Humanoids need all the human senses: touch, smell, sight, hearing and balance to function properly. The hearing sensor is important for the humanoid to hear instructions, decipher them and carry them out. The touch sensor prevents it from bumping into things and causing self-damage. The humanoid needs a sensor to balance movement and equally needs heat and pain sensors to know when it faces harm or is being damaged. Facial sensors also need to be intact for the humanoid to make facial expressions, and these sensors should be able to carry a wide range of expressions.

Making sure that these sensors are available and efficient is a hard task.

Facial Recognition:

What is a Facial Recognition System? In simple words, a Facial Recognition System can be defined as a technology that can identify or verify a person from a digital image or video source by comparing and analyzing patterns based on the person’s facial contours.

Starting from the mid-1900's, scientists have been working on using computers to recognize human faces. Face recognition has received substantial attention from researchers due to its wide range applications in the real world.

WHY FACIAL RECOGNITION IS IMPORTANT?

Facial recognition is now considered to have more advantages when compared to other biometric systems like palm print and fingerprint since facial recognition doesn’t need any human interaction and can be taken without a person’s knowledge which can be highly useful in identifying the human activities found in various applications of security like airport, criminal detection, face tracking, forensic, etc.

 

Your Face will become your ID:

        We’re already seeing bio metric incorporated into our daily lives and that technology is expected to evolve. Eventually, many in the tech industry anticipate AI-driven applications allowing machines to recognize your face to complete transactions. Your credit cards and driver’s license may be linked to your face, allowing pattern recognition devices to know you instantly. This can make everyday transactions far more efficient, saving us from having to wait in line at the store, bank, or movie theater.

Receive better Medical care:

     Research is already underway to develop new software applications that use AI to help doctors diagnose and treat patients. It won’t be long before wearable devices can measure blood sugar levels for diabetics and transmit that data to the patient’s doctor. Already, devices are in use that measure heart rate, respiration, and other vital functions. Artificial intelligence may also help patients better understand their care options and communicate more effectively with their caregivers.

AI Response will be more Empathetic:

     Internet users have already seen the effect artificial intelligence has had when they visit a website with a chat bot. In the past, chat bots were pre programmed to give specific answers to specific inquiries. Today, artificial intelligence software allows chat bots and virtual personal assistants to research any question and provide an accurate response. While that is impressive, there’s still room for advancement. Ultimately, AI-driven devices will analyze our speech or actions to interpret our needs, so they can offer more insightful information. This type of programming might best be described as “digital empathy” and it may provide the best human-device interactions possible.

How AI Technology change our lives on a Personal level:

Artificial intelligence has already made its way into many homes, but it will soon be indispensable in most households. As we move closer toward becoming a technologically driven society, AI applications will fulfill the promise that computers would make our lives easier. AI technology will help us live happier and healthier lives, while also helping us conserve time, energy, and money.

Posted by Diwakar Reddy at 10:53 PM  

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Nine Machine Learning Methods For A Data Scientist

     Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts  and potentially overwhelming for beginners.

To demystify machine learning let’s look at ten different methods, including simple descriptions, visualizations, and examples for each one.

What is Machine Learning:

The purpose of machine learning is to discover patterns in your data and then make predictions based on those often, complex patterns to answer business questions, and help solve problems. For example, if an online retailer wants to anticipate sales for the next quarter, they might use a machine learning algorithm that predicts those sales based on past sales and other relevant data. Similarly, a windmill manufacturer might visually monitor important equipment and feed the video data through algorithms trained to identify dangerous cracks.

Broadly, there are 3 types of Machine Learning Algorithms

1. Supervised Learning

 This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree,Random Forest,KNN Logistic Regression etc.

2. Unsupervised Learning

 In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning:  K-means algorithm.

3. Reinforcement Learning:

 Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process

List of Common Machine Learning Algorithms

Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. SVM (Support Vector Machine)
5. Naive Bayes
6. kNN (K-Nearest Neighbour)
7. K-Means
8. Random Forest
9. Dimensionality Reduction Algorithms                                  

1. Linear Regression

It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variables. Here, we establish relationship between independent and dependent variables. This is represented by a linear equation Y= a *X + b.

The best way to understand linear regression is: For Example Let us say, you ask a child in fifth grade to arrange people in his class by increasing order of weight, without asking them their weights! What do you think the child will do? He / she would likely look (visually analyze) at the height and build of people and arrange them using a combination of these visible parameters. This is linear regression in real life! The child has actually figured out that height and build would be correlated to the weight by a relationship, which looks like the equation above.

In this equation:

• Y – Dependent Variable
• a – Slope
• X – Independent variable
• b – Intercept

These coefficients a and b are derived based on minimizing the sum of squared difference of distance between data points and regression line.

2. Logistic Regression

 It is a classification not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of independent variables. In simple words, it predicts the probability of occurrence of an event by fitting data to a Logit function. Hence, it is also known as logit regression. Since, it predicts the probability, its output values lies between 0 and 1.

Again, let us try and understand this through a simple example.

Let’s say your friend gives you a puzzle to solve. There are only 2 outcome scenarios – either you solve it or you don’t. Now imagine, that you are being given wide range of puzzles / quizzes in an attempt to understand which subjects you are good at. The outcome to this study would be something like this – if you are given a trigonometry based tenth grade problem, you are 70% likely to solve it. On the other hand, if it is grade fifth history question, the probability of getting an answer is only 30%. This is what Logistic Regression provides you.

Coming to the math, the log odds of the outcome is modeled as a linear combination of the predictor variables.

odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence




ln(odds) = ln(p/(1-p))




logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk

Above, p is the probability of presence of the characteristic of interest. It chooses parameters that maximize the likelihood of observing the sample values rather than that minimize the sum of squared errors (like in ordinary regression).

3. Decision Tree

        Decision tree is a type of supervised learning algorithm that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets based on most significant splitter / differentiator in input variables.

• One simple example of a Decision Tree as seen below.

          The top-most item, in this example, “Am I hungry?” is called the root. It’s where everything starts from. Branches are what we call each line. A leaf is everything that isn’t the root or a branch.

4. SVM (Support Vector Machine)

   It is a classification method. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

For example, if we only had two features like Height and Hair length of an individual, we’d first plot these two variables in two dimensional space where each point has two co-ordinates (these co-ordinates are known as Support Vectors)

Now, we will find some line that splits the data between the two differently classified groups of data. This will be the line such that the distances from the closest point in each of the two groups will be farthest away.

In the example shown above, the line which splits the data into two differently classified groups is the black line, since the two closest points are the farthest apart from the line. This line is our classifier. Then, depending on where the testing data lands on either side of the line, that’s what class we can classify the new data as.

5. Naive Bayes

    Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

Naive Bayesian model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

Bayes theorem provides a way of calculating posterior probability P(c|x) from P(c), P(x) and P(x|c). Look at the equation below:

Here,

• P(c|x) is the posterior probability of class (target) given predictor (attribute).
• P(c) is the prior probability of class.
• P(x|c) is the likelihood which is the probability of predictor given class.
• P(x) is the prior probability of predictor.

6. kNN (k- Nearest Neighbors)

        K-Nearest Neighbors is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining and intrusion detection.

It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data.

Example:

The following is an example to understand the concept of K and working of KNN algorithm.

Suppose we have a data set which can be plotted as follows:

Now, we need to classify new data point with black dot (at point 60,60) into blue or red class. We are assuming K = 3 i.e. it would find three nearest data points. It is shown in the next diagram.

We can see in the above diagram the three nearest neighbors of the data point with black dot. Among those three, two of them lies in Red class hence the black dot will also be assigned in red class.

7. K-Means

  K-means clustering is a simple unsupervised learning algorithm that is used to solve clustering problems. It follows a simple procedure of classifying a given data set into a number of clusters, defined by the letter "k," which is fixed beforehand. The clusters are then positioned as points and all observations or data points are associated with the nearest cluster, computed, adjusted and then the process starts over using the new adjustments until a desired result is reached.

K-means clustering has uses in search engines, market segmentation, statistics and even astronomy.

  It is used mainly in statistics and can be applied to almost any branch of study. For example, in marketing, it can be used to group different demographics of people into simple groups that make it easier for marketers to target. Astronomers use it to shift through huge amounts of astronomical data; since they cannot analyze each object one by one, they need a way to statistically find points of interest for observation and investigation.

The algorithm:

1. K points are placed into the object data space representing the initial group of centroids.
2. Each object or data point is assigned into the closest k.
3. After all objects are assigned, the positions of the k centroids are recalculated.
4. Steps 2 and 3 are repeated until the positions of the centroids no longer move.

                                       A Simple example of K-Means

8. Random Forest

Random forest is a supervised learning algorithm which is used for both classification as well as regression. But however, it is mainly used for classification problems. As we know that a forest is made up of trees and more trees means more robust forest. Similarly, random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by means of voting. It is an ensemble method which is better than a single decision tree because it reduces the over-fitting by averaging the result.

Working of Random Forest Algorithm:

We can understand the working of Random Forest algorithm with the help of following steps −

• Step 1 − First, start with the selection of random samples from a given data set.
• Step 2 − Next, this algorithm will construct a decision tree for every sample. Then it will get the prediction result from every decision tree.
• Step 3 − In this step, voting will be performed for every predicted result.
• Step 4 − At last, select the most voted prediction result as the final prediction result.

The following diagram will illustrate its working:

9. Dimensionality Reduction Algorithms

Dimensionality reduction is a series of techniques in machine learning and statistics to reduce the number of random variables to consider. It involves feature selection and feature extraction. Dimensionality reduction makes analyzing data much easier and faster for machine learning algorithms without extraneous variables to process, making machine learning algorithms faster and simpler in turn.

Dimensionality reduction attempts to reduce the number of random variables in data. A K-nearest-neighbors approach is often used. Dimensionality reduction techniques are divided into two major categories: feature selection and feature extraction.

Feature selection techniques find a smaller subset of a many-dimensional data set to create a data model. The major strategies for feature set are filter, wrapper (using a predictive model) and embedded, which perform feature selection while building a model.

Feature extraction involves transforming high-dimensional data into spaces of fewer dimensions. Methods include principal component analysis, kernel PCA, graph-based kernel PCA, linear discriminant analysis and generalized discriminant analysis.

Future Scope :

       Machine Learning is currently one of the hottest topics in IT.. Technologies such as digital, big data, Artificial Intelligence, automation and machine learning are increasingly shaping future of work and jobs is a specific set of techniques that enable machines to learn from data, and make predictions. When the biases of our past and present fuel the predictions of the future, it's a tall order to expect AI to operate independently of human flaws.

Summary:

I’ve tried to cover the nine most important machine learning methods: from the most basic to the bleeding edge. Studying these methods well and fully understanding the basics of each one can serve as a solid starting point for further study of more advanced algorithms and methods.