Decision Tree is a tree shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction.

Information Theory** **is the fundamentals of decision trees. In order for us to understand Decision Tree algorithm, we need to understand Information Theory.

The basic idea of information theory is that the “informational value” of a data-set depends on the degree to which the content of the message is surprising or messy. If an event is very probable, it is no surprise (and generally uninteresting) when that event happens as expected; hence transmission of…

*Takeaway** : Main takeaway from this article :*

- Logistic Regression
- Approaching Logistic Regression with Neural Network mindset

Logistic Regression is an algorithm for binary classification. In a binary classification problem the input (**X**) will be a feature vector of 1-D dimension and the output (**Y**) label will be a **1 **or **0**

The logistic regression output label lies between the range 0 and 1 .

0 ≤ Y ≤ 1, where Y is the probability of the output label being 1 given the input X

Y = P(y=1 | x) For a learning algorithm to find **Y i**t takes two…

This tutorial is the foundation of computer vision delivered as “Lesson 5” of the series, there are more Lessons upcoming which would talk to the extend of building your own deep learning based computer vision projects. You can find the complete syllabus and table of content here

Target Audience: Final year College Students, New to Data Science Career, IT employees who wants to switch to data science Career .

Takeaway: Main takeaway from this article :

**Image Classification Using Machine Learning****Image Classification : Machine Learning way**vs**Deep Learning way**

By definition, Image classification is a process…

This tutorial is the foundation of computer vision delivered as “Lesson 3” of the series, there are more Lessons upcoming which would talk to the extend of building your own deep learning based computer vision projects. You can find the complete syllabus and table of content here

Target Audience: Final year College Students, New to Data Science Career, IT employees who wants to switch to data science Career .

Takeaway: Main takeaway from this article :

- Morphological operations
- Exercise to extract the tabular structure in an invoice using Morphological operations

Morphological operations are simple transformations applied to binary…

**Basic Image Processing**

a. Rotation

b. Resizing

c. Flipping

e. Cropping

f. Image Arithmetic

The cv2.getRotationMatrix2D function takes three arguments. The first argument is the point in which we want to rotate the image about (in this case, the center of the image). We then specify \theta, the number of (counter-clockwise) degrees we are going to rotate the image by. In this case, we are going to rotate the image 45 degrees. The last argument is the scale of the image. We haven’t discussed resizing an image yet, but here you can specify a floating point value, where 1.0 …

This tutorial is the foundation of computer vision delivered as “Lesson 2” of the series, there are more Lessons upcoming which would talk to the extend of building your own deep learning based computer vision projects. You can find the complete syllabus and table of content here

Target Audience: Final year College Students, New to Data Science Career, IT employees who wants to switch to data science Career .

Takeaway: Main takeaway from this article :

**Loading an Image from Disk****Obtaining the ‘Height’, ‘Width’ and ‘Depth’ of Image****Finding R,G,B components of the Image****Drawing using OpenCV**

Text book definition of pixel normally is considered the “color” or the “intensity” of light that appears in a given place in our image. If we think of an image as a grid, each square contains a single pixel.

pixels are the raw building block of an image, there is no finer granularity than the pixel. Most pixels are represented in two ways:

- Grayscale/single channel
- Color

Lets take an example image :

The image in Fig 1.1 has 4000 pixels wide and 3000 pixels tall, total = 4000 * 3000 = 1,20,00,000 pixels

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