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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Scope and Limitation of Machine Learning

By Kajal Vishwakarma -

 Scope:-

  • Increase in Data Generation:- Due to excessive production of data, need a method that can be used to structure, analyze and draw useful insight from data. This is where machine learning comes in. It uses data to solve problems and find solution the most complex tasks faced by organizations.
  • Improve Decision Making:- By making use of Various algorithms, Machine Learning can be used to make better business decisions.
For example, Machine Learning is used to forecast sales, predict downfalls in the stock market, identify risks and anomalies, etc.
  • Uncover patterns & trends in data:- Finding hidden patterns and extracting key insight from data is the most essential part of machine learning. by building predictive models and using statistical techniques, machine learning allows you to dig beneath the surface and explore the data at a minute scale. Understanding data and extracting patterns manually will take days, whereas machine learning algorithms can perform such computations in less then a second.
  • Solve complex problems:-  Building self-driving cars, machine learning can be used to solve the most complex problems.
Limitations:-

  • What algorithm exist for learning general target function from specific training examples?
  • In what setting particular algorithm converge to the desired function, given sufficient training data?
  • Which algorithm performs best which types of problems and representations?
  • How much training data is sufficient?
  • when and how can prior knowledge held by the learner guide the process of generalizing from examples?
  • What is the best way to reduce the learning task to one more function approximation problem?
  • Machine Learning algorithm require massive to training data.
  • Labeling training data is a tedious process.
  • Machine cannot explain themselves.

     

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