Graphic design python turtle 🐢

By Kajal Vishwakarma -
from turtle import * import colorsys bgcolor('black') pensize(0) tracer(50) h=0 for i in range(300): c=colorsys.hsv_to_rgb(h,1,1) h+=0.9 color(c) forward(300) left(100) fd(i) goto(0,0) down() rt(90) begin_fill() circle(0) end_fill() rt(10) for j in range(5): rt(30) done() Please follow my blog and subscribe my channel for more videos and newly updates 👍👍👍👍👍 import turtle as t import colorsys t.bgcolor('black') t.tracer(100) h=0.4 def draw(ang,n): t.circle(5+n,60) t.left(ang) t.circle(5+n,60) for i in range(200): c=colorsys.hsv_to_rgb(h,1,1) h+=0.005 t.color(c) t.pensize(2) draw(90,i*2) draw(120,i*2.5) draw()
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Machine Learning & It's Types

By Kajal Vishwakarma -

 

Machine Learning and it’s Types:-

Machine learning is a tool for turning information into knowledge.  Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that  we would otherwise struggle to discover. The hidden patterns and knowledge about  a  problem  can be used to predict future events and perform all kinds of complex decision making.

Tom Mitchell gave a “well-posed” mathematical  and relational definition that  “A computer program its performance on T, as measured by  P, improves with experience  E”.

Types of Machine Learning:-

  • 1)      Supervised  Learning:-  Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.
  • 2)      Unsupervised Learning:-  The model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationship in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.
  • 3)      Reinforcement Learning:- It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct  or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it

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