Tech Port 21

 Machine Learning It is basically a Subset of AI (Artificial Intelligence) which enables Machine to Learn from Data.     Hmm... So, What is AI and what does a Subset of AI means? Artificial Intelligence is basically the domain of producing Intelligent Machines. All the machine which performs some smart activity can be called as an Artificially Intelligent Machine like an Air Conditioner changing the Temperature inside the room by sensing the Temperature from the outside. Such machines are not using any Machine Learning or Deep Learning Techniques.So such machine might come in the Artificial Intelligent set but not in the Machine Learning or Deep Learning part.   How does a Machine Learns from Data? A Machine Learns from data using a Machine Learning Algorithm which take's the data as an input and predicts the output by finding patterns in the data.   Machine Learning Algorithms can be broadly divided into 3 types : Supervised Learning Unsupervised Learning Reinforcement Learning S

Media files tends to have a lot of information and processing it need a lot of time and computation. Resizing of image and videos is done to prevent computational strain Resizing is basically modifying the width and height. Many image recognition and machine learning applications benefit from scaling. By Resizing, the training time of a neural network can be significantly reduced. We'll use CV2.resize() method :- cv2.resize(src, dsize, interpolation)   src - takes the input image   dsize - take the output dimension as input in Tuple   Interpolation take three method as input  cv2.INTER_AREA : This is used when we need to shrink an image.It is the preferred method  cv2.INTER_CUBIC : A Bicubic method, is slow but more efficient.  cv2.INTER_LINEAR : This is primarily used when zooming is required. This is the default interpolation technique in OpenCV   The Function defined below will always work for Images, Video and Live Camera Feed . CODE FOR RESIZING. import cv2 frame= cv2.imre

 Image An Image consists of a set of pixels, which are the buildings blocks for any image. Every Pixels defines the color or the intensity of light.      Suppose an image has a resolution of 1000 x 750,which mean that it is 1000 pixels wide and 750 pixels tall. So the total number of pixels in our image will be 1000 * 750 = 7,50,000 pixels. An Image can be of two type :- Grayscale Color A Grayscale image can have a pixel value between 0 and 255, here 0 means the pixel is 'Black' and 255 means the pixel is ' White'. All the values in between represents various shades of gray. The matrix obtained from a Grayscale Image is 2-Dimensional ie it has width and height. A Color image is represented in RGB color space.The matrix obtained from a color image is a 3D matrix with parameter of Width, Height and Depth. Pixels in the RGB color space are no longer a scalar value like in a grayscale/single channel image – instead, the pixels are represented by a list of three values:

What is the difference between List and Tuple in Python?   A list can hold ordered sets of all data types in Python (including another list). A list's elements can be modified after creation. The implication of iterations is time-consuming in the list. Operations like insertion and deletion are better performed and consumes more memory. They are mutable The tuple type is very similar to the list type but the elements cannot be modified after creation (similar to strings). Implications of iterations are much faster in tuples. Elements can be accessed better and consumes less memory.They are immutable. What type of language is Python? Python is a dynamically typed interpreted language. These types of languages are typically referred to as “scripting” languages because code is not compiled to a binary form. By dynamically typed I mean that types do not need to be declared when coding, the interpreter figures them out at runtime. Python is neither a true compiled time nor pure

 What is Linear Regression? This Algorithm is used to find the relationship between 2 continuous variables [ one independent variable and one dependent variable ]. It is a linear model which assumes a linear relationship between input and output variables. If we have single input variables then we call it as simple linear regression, if we have multiple we call it as multiple linear regression. It is both a statistical algorithm and machine language algorithm.  The Equation is 'Y = M * X + C' Y = Independent Value M = Slope/Weight X = Dependent Value C = Bias The Core idea is to obtain a line that best fits the data. 'Y' is the output variable we want to predict, X is the input variable and M & C can be called as coefficients that we need to estimate. To find m and b values we have methods like statistical method or ordinary least squares or gradient descent. How does it Work? Goal is to find the best fit line which minimize the error ( distance between the line and

  Machine Learning sub-set of Artificial Intelligence focuses mostly on Machine. In this, computer algorithms are reinforced by training automatically, resulting in increased efficiency and better prediction. It detects similarities in the data, allowing data-driven decisions to be taken by the computer or system rather than being directly designed to do a certain task. Machine Learning Terms You Should Know Data Wrangling Data Wrangling also know as Data Cleaning or "munging", is the process of gathering, selecting, cleaning , structuring and enriching raw data into the desired format for better decision making in less time. Data wrangling help to create an efficient ETL (Extract Transform and Load) or create beautiful data visualizations.It can take a lot of work and time but it is worth the time and effort as it can give vital information from the data. Data Imputation  Data Imputation is the substitution of estimated values for missing or inconsistent data item (fields).