# Notes on ML and TensorFlow: Linear Regression

Linear Regression is an important algorithm of supervised learning. In this article, I introduced how to solve a linear regression problem by using Gradient descent algorithm and Matrix derivatives.

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# Infer.NET — A Library for People Who Love Probability

Infer.NET is an open source library that can be used to create probabilistic programming systems. We can use Infer.NET to solve many different kinds of machine learning problems, such as classification, recommendation, and so on. In this article, I am going to introduce how to use the infer.NET library in Visual Studio 2017 Community. Infer.NET supports both C# and F#, and I am going to use C# in this article.

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# An introduction to the TensorFlow: Vectors

In Machine Learning, vectors can be used as a good way to represent numeric data. When using vectors, we can meet the following basic operations:

• Add two vectors
• Subtract two vectors
• Multiply a vector with a scalar (i.e., a number)
• Norm (i.e, magnitude or length of a vector)
• Dot product of two vectors

In this article, I will introduce basic operations on vector objects by using TensorFlow library.

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# Introducing the ML.NET — A Machine Learning Library for .NET Developers

Most of the common Machine Learning (ML) libraries are written in Python and it is not so easy for .NET developers. The ML.NET library occurs as a bridge between ML libraries and .NET applications.

ML.NET is an open source library that can be used directly in .NET applications. In this article, I am going to introduce how to use the ML.NET library in Visual Studio 2017 (I am using VS 2017 Community).

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# Introduction to support vector machine (SVM)

Support vector machines (SVMs) are a type of classifier. They’re called machines because they generate a binary decision; they’re decision machines. SVMs do a good job of learning and generalizing on what they’ve learned.

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# An Introduction to Logistic Regression

In statistics, the logistic model is a statistical model that is usually taken to apply to a binary dependent variable. In regression analysis, logistic regression is estimating the parameters of a logistic model.

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# An introduction to the naive Bayes classifier

Using probabilities can sometimes be more effective than using hard rules for classification. Bayesian probability and Bayes’ rule gives us a way to estimate unknown probabilities from known values. Read more here.

# An Introduction to Decision Trees in Machine Learning

The decision tree is one of the most commonly used classification techniques. One of the best things about decision trees is that humans can easily understand the data. You can read more here.

# Running Python scripts by using Anaconda prompt

After installing Anaconda, we start to write some Python code in the Anaconda prompt. The following line of code is used to display a text message (such as Hello World) to the screen in the Python 3.6.4:

`print("Hello World");`

How to execute this line of code in the Anaconda prompt? Here is solution:

• Open the Anaconda prompt and type python command
• Type above line of code

The result can look like the following screenshot:

If I want to execute more than one code of line at a time as follows:

`print("Hello World");print ("My name is Ngoc Minh")`

I can…

# Using Kotlin in Android Studio 3.X

Kotlin is a modern language for Android developers. I am learning Kotlin for my job and I have also shared some articles to DZone community. If you are also excited about Kotlin like me, you could begin with the following steps:

• Download and install Android Studio
• Begin to learn Kotlin in Android Studio with my articles here. 