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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...
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TURTLE GRAPHICS USING PYTHON || Turtle Star || 08 || ~xRay Pixy

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TURTLE GRAPHICS USING PYTHON || Turtle Star || 08 ||  STEPS Python Turtle Graphics Design First Import Turtle Here, variable r is considered a Turtle. Set Turtle Speed. Set Line Width. Set Background Color as while.  Select Colors for the Design. Create a List of Colors. For Loop Initialization. Access Colors from the List inside Loop. Set Direction for Turtle. SOURCE CODE import turtle r = turtle.Turtle() r = turtle.Pen() r.speed(1000) r.width(2) turtle.bgcolor('white') c = ['blue','lime','red','black','gold','aqua','purple','silver'] for x in range (600):     r.pencolor(c[x%8])     r.forward(x)     r.right(160) r.done() OUTPUT
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