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When you think about data science and machine learning, the two most obvious programming languages are R and Python. But what if the developer is proficient in other languages?

Microsoft released build 2018 of its machine learning framework. This is especially useful for C# and.NET developers. It is free of charge and cross-platform, and it can be used on Windows, Linux, and macOS.

Microsoft Developers have always wanted a NuGet package they could plug into with a. A Net application to create machine learning applications. The first version was released. ML.NET is still a baby, but it already has the potential to become a tech giant.

This version can be used to perform machine learning tasks like classification and regression with advanced ML algorithms. You can also train and predict the model using other MAchine Learning tasks or algorithms.

ML.NET can also be extended to work with ML libraries such as TensorFlow and Accord.NET. CNTK is another example.

What's inside ML.NET?

We have seen that the framework can be extended so it can work with third-party libraries. This allows for smooth operation. There are some amazing libraries included as well. ML.NET supports both consumption and training for tasks such as classification and regression.

It also supports extensible pipelines, core data types, data structures, data structures, tooling support, and advanced performance math.

Let's learn how to add the NuGet package to your.Net applications. You must have installed.NET Core 2.0, or later, as ML.NET runs on a 64-bit system.

• Once you're done, open Visual Studio 2017 - New Project - Select Core Web Application as shown in the image.
• For now, click on the empty option:
• Create your application, then add Microsoft.ML NuGet package. Continue your search by entering 'Microsoft.ML" in NuGet Package Manager, and click on "Install".
• Once you have completed all steps, your package will be installed safely and is ready for use.
• You can also install ML.NET NuGet from the CLI.

Dotnet Add Package Microsoft.ML

You can also create your framework from GitHub.

What are the core components of ML.NET?

The ML.NET framework was launched as part of the.NET Foundation. Today's repo contains the.NET C# APIs for both model training and consumption, along with variant transformations. Learners are needed for many popular ML tasks such as classification and regression.

ML.NET is a tool that provides E2E workflow to infuse ML into.NET apps across various specializations such as pre-processing and feature engineering. It also supports evaluation and operationalization.

You can also create a sample app that will show you how to use ML models in your application by using ML.NET templates, which can be used directly in your applications.

Before you start using ML.NET, here are some things to consider

• Start your Model: First, machine learning requires that you choose the most suitable ML algorithms. Machine learning uses methods such as classification, regression, and anomaly detection.
• Train the Model: The first step in training the machine learning model is to analyze input data and save it as a trained model. You can create a CSV in your application. In the CSV file, you will enter stock details such as ItemID, location, InQTY and OutQTY, and TotalStock quantity. This CSV file is needed as input to the model. The model can then be trained and analyzed this data to predict the results. The model will learn the input pattern and can be trained to predict the outcome.
• Prediction: A score is another name for the prediction. It must have the same column as its trained model. This score is used to generate the results using the trained model.
• Evaluation: An evaluation will be conducted after the model training has been completed. The evaluation compares the model to the test data and predicts the final results.

In just a few steps, you can create a C# console app

Step 1: Create an App

After installing all prerequisites, select Visual Studio 2017 from Programs - New Project - Click Visual C# Windows Desktop - Console App in dot Net framework. Select OK and enter the project name.

Step 2: Get the Microsoft ML Package down

• Right-click on your project and select Manage NuGet Packages.
• Next, click on the Browser tab and search for Microsoft.ML.
• Click Install and accept the policy. Wait until installation proceeds.

The Microsoft.ML package will be installed, and all Microsoft.ML references will have been inserted into your project references.

Step 3: Create training and evaluate the data

You will now need to create a Model Training and Evaluation for the dataset. To do this, add two CSV files to a folder called data in the project.

• Add the Data folder

Right-click the project and select Add New Folder.

• Make a Train CSV File
• Right-click the Data folder and select Add - New item - Text file. Name it 'StockTrain.csv.
• To change the Copy to Output Directory set to 'Copy Always', click on properties
• Add CSV file details to the figure.

This is a reminder to users: You must fix the labels and features. A label field is the one that we must predict in our sample

• ItemID -- Product ItemID (Feature)
• LocCode -- Warehouse Location (Feature).
• InQty -- Total Item arrived at the location (Feature).
• OutQty -- Total Item Delivered to the Location (Feature)
• ItemType -- ItemType "In" means locally manufactured, and "out" refers to outsourcing. (Feature)
• TotalStockQty -- No total item stock. (Label)

Remember that your model must have at least 100 records of data in order to train it. You will also need to create a new CSV file called StockTest.csv for evaluation.

Step 4: Create a class to receive input data and predict

You will now need to create a new Input Data and Prediction class by right-clicking on your project and adding a class called 'ItemStock.cs. You first need to import the Microsoft.ML.Runtime.API in your class as given:

We must also add all columns in the same order in the class, and set the column from 0 to 5.

To add your prediction column, create a prediction type. The prediction column should be renamed to 'Score' using the data type float. The predicted results from the regression model are contained in the scoring column.

Step #5: Program.cs

To work with ML.NET, you will need to open the program.cs> file and import the necessary references.

Dataset Path
The 'StockTrain.csv" path is required for train data. This will allow you to save the final trained model for evaluation and production. The model path can be set as a zip file. Your bin folder will automatically save the trained model in the zip file during runtime.

static readonly string _Traindatapath = Path.Combine(Environment.CurrentDirectory, "Data", "StockTrain.csv");

static readonly string _Evaluatedatapath = Path.Combine(Environment.CurrentDirectory, "Data", "StockTest.csv");

static readonly string _modelpath = Path.Combine(Environment.CurrentDirectory, "Data", "Model.zip");

You can also change the Main method to async Task Main methods, like the code below:

To run your program efficiently, you must first perform two important tasks. Set Platform Target to x64 because ML.NET runs on x64. Second, run the program using the async Task Main method. Change the language version to c#7.1.

Use the Training Model to Your Advantage
Firstly, you need to train the model and later save it to the zip file which is your main method to call the prediction model and pass the ItemStock itemStockQtyPrediction class to return the model to the main method:

The above method requires you to add the function that will train the model to your code and then save it to a zip file. Training will first require you to use the LearningPipeline(), which loads all the data necessary for the model's training.

The TextLoader is the second. It retrieves all data from the train CSV files for training and sets the header to prevent the CSV file from reading the first row. The training model must contain the expected values. You will need numeric features to transform categorical data. Also, you must add all featured columns to be evaluated and trained.

Learning Algorithms
This code snippet will use FastTreeREgressor Learner. It must be added to your pipeline. The last step is to save and train the model using the following method:

You must also evaluate the model. This involves comparing the model to the test data and predicting the final results. To do this, you will need to call the Evaluate Method from your main method. Then pass the trained model to the model.

The Evaluate method passes the trained model with the CSV sample test data. Use a text loader to load the CSV test data file data for model evaluation.

The RegressionEvaluator method allows us to evaluate the model using test data. We can also produce evaluation metrics by displaying the Root Mean Squared and RSquared metrics values.

Final Prediction Results
Now, it is time to create the final results. Create a new class called 'ItemStock.cs.' Add values to it. These are the values that were created in the columns for Model training.

You can also run it and view the results in the command windows, as shown below.

We can view the Model.zip file after creating and running our program. This folder is located inside the application root folder. It contains the Model.zip file. This CSV file is used to train the model and will be saved in the Model.zip.

Summarizing

We conclude that ML.NET provides a powerful framework for all.NET developers interested in machine learning. Currently, the preview version is not available. However, we can't wait to see the full public release. If you're a Microsoft developer but don't know much about the machine-learning framework, ML.NET might be for you. We hope that you enjoyed this article. Keep Learning!

IT professionals have many opportunities in the emerging IT world. We are seeing a lot of new tools and opportunities to use them in the expanding Microsoft .NET development environment. It is obvious that .NET developers are highly sought-after, versatile, and talented in the programming world. The .NET market is competitive, however, as with any other market.

There are many skills and specializations that can be applied to this expanding field. If someone wants to be a certified microsoft developer at a well-established company and earn a good salary, they will need to improve their technical skills. It's easier to increase market demand and earn potential this way.

Who is a.NET Developer?

Before we go into detail about the skills needed to be a top-notch .NET developer, let's first understand what a .NET developer is. It is easy to assume that a .NET Developer is someone who uses the keyboard to update data or creates new software. In reality, however, the responsibilities of a .NET developer are much more.

• A .NET developer creates text-based applications and then they are run on a variety of .NET applications.
• A .NET developer creates the code that accesses the applications and then assists with resolving system-related issues & defects.
• The professional develops and manages the defect tracking software, then settles all issues by updating the system.
• He/she estimates the risk.
• A .NET developer prepares the policy & procedures documents to run applications.
• S/he designs different application system
• A .NET developer keeps an eye on the system.

This isn't all. These skills are essential for a .NET developer to be able to fulfill his responsibilities well.

Skills that every.NET developer must have

1) Learn ASP.NET Framework

Today, ASP.NET framework dominates the web market and is quickly replacing other frameworks. ASP.NET MVC allows developers to build robust and feature-rich web apps, speeding up deployment. ASP.NET MVC can be used on the client-side web technologies and is easily paired with server-side programming to create complete software solutions. ASP.NET MVC can also be used for unit testing.

2) Increase your SQL skills

It is a well-known fact that data dominates technology. A strong SQL skill can help you land a job. It doesn't matter if the developer is focused only on Microsoft SQL Server, or MYSQL. However, it is essential to be able both to design and maintain a database as an architect. Developers who are skilled in data & science can increase their demand and offer great opportunities to work as a .NET developer for a top company.

3) Databases

Microsoft .NET uses several databases, but it is still vital that the Microsoft developer has a good knowledge of this area. The system is available, but skilled developers are needed. This is why employers hire dedicated microsoft developers. It adds value to a developer's portfolio if he/she is proficient in SQL and can work with systems such as Entity Framework. This skill is a requirement for most employers in the .NET Development sector. The one who has experience in managing databases such as Microsoft SQL and Oracle gets preference. Modern apps don't use NoSQL databases like CouchDB and MongoDB. Employers will be more inclined to hire developers who have skills in these databases.

4) Client-side web technology

It is crucial for a Microsoft developer to be proficient in creating interactive, attractive, and creative web applications. This increases their market value, and can also help in other ways. In fact, this is an added quality to the developer's portfolio. The more the better. The latest technologies added to the list of client-side technologies include HTML, CSS, HTML, jQuery, and JavaScript. These aspects are a great asset to .NET developers.

5) Microsoft Most Valuable Professional, (MVP)

This is a highly-valued, high-ranking prestigious program where .NET developers work alongside the Microsoft MVP program. It is therefore highly sought-after. A developer who achieves this profile status is considered to be a strong leader and technical expert in the .NET framework. This skill is available to .NET developers who are able to act as mentors.

6) Microsoft Certified Solutions Developers (MCSD)

Again, the MCSD certification has a high demand. This certification is highly sought after by .NET developers as it comes with Microsoft's development and maintenance. It requires modifications every two years. A developer who is skilled in this area will be a favorite again.

7) More experience is better

A developer must have a lot of experience in .NET to be proficient. This will allow them to learn the processing and handling well. You will gain the necessary expertise. You can search for projects and opportunities that involve programming on Microsoft's platform. Also, you can do further research on your own. There are many tutorials online that can help developers improve their skills. Itransition, for example, is a company that seeks out promising developers in .NET.

8) Create your own app

Before a developer embarks on the task of designing an app for a large company, it is a good idea to first create a Windows application. They will be able to see firsthand the challenges associated with working with the .NET framework. This will also help them know what to expect when they enter the big leagues. You can also create your own apps to learn how to code and edit code.

9) Must have excellent communication skills

As .NET developers work with clients, it is common for them to have poor communication skills. To avoid any misunderstandings, a web developer must be able to convert complex codes into simple features that can be understood by everyone. Developers must have the confidence to hold their ground when clients make unreasonable demands.

Conclusion

The .NET developers' onlookers aren't satisfied with just their theoretical knowledge. They are also concerned about the practical application of their skills. While there may be many .NET developers in your area, it is the most highly sought-after one in the tech sector. Because there are always new technologies in the market, .NET developers need to be eager to learn them all. It is a never-ending process. They are paid more if they have more knowledge.