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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Advantages and Disadvantages of machine learning

By Kajal Vishwakarma -

Advantages of machine learning

The advantages of machine learning are as follows-

  1.       Accurate
  2.      Automated
  3.      Fast
  4.      Customizable
  5.       Scalable

 

  • 1)      Accurate:- Machine learning uses data to discover the optimal decision making engine for your problem. As you collect more data, the accuracy can increase automatically.
  • 2)      Automated:- as answers are validated or discarded, the machine learning model can learn new patterns automatically. This allows users to embed machine learning directly into an automated workflow.
  • 3)      Fast:- Machine learning can generate answers in a matter of milliseconds as new data streams in, allowing systems to react in real time.
  • 4)      Customizable:- Many data-driven problems can be addressed with machine learning. Machine learning models are custom built from your own data, and can be configured to optimize whatever metric drives your business.
  • 5)      Scalable:- As your business grows, machine learning easily scales to handle increased data rates. Some machine learning algorithms can scale to handle large amounts of data on many machines in the cloud.

 Disadvantages of machine learning

The disadvantages of machine learning are as follows-

  •  Machine learning has the major challenge called acquisition. Also based on different algorithms data need to be processed. And , it must be processed before providing as input to respective algorithms. Thus, it has a significant impact on results to be achieved or obtained.
  •  As we have one more term interpretation. That it result is also a major challenge. That need to determine the effectiveness of machine learning algorithms.
  • We can say uses of machine algorithm is limited. Also, it’s not having any surety that it’s algorithms will always work in every case imaginable. As we have seen that in most cases machine learning  fails. Thus, it requires some understanding of the problem at hand to apply the right algorithm.
  •  Like deep learning algorithm, machine learning also needs a lot of training data. As we can say it might be cumbersome to work with a large amount of data. Fortunately, there are a lot of training data for image recognition purposes.
  •  One notable limitation of machine learning is its susceptibility to errors. Brynjolfsson and McAfee  said that the actual problem with this inevitable fact. That when they do make errors, diagnosing and correcting them can be difficult. As because it will need going through the underlying complexities.

 https://rgpvnotesforcsestudents.blogspot.com/2021/03/introduction%20types%20classification%20in%20ML.html

https://rgpvnotesforcsestudents.blogspot.com/2021/03/regression-in-ML.html




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