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How to Do Testing of
Machine Learning Projects?
• ML Stands for Machine Learning.
• Machine learning (ML) is the scientific study of algorithms and statistical models tha...
1950
Alan Turning Created a
test to check if a
machine could fool a
human being into
believing it was taking
to a machine
...
Machine Learning
Supervised
Learning
Unsupervised
learning
Semi-
supervised
learning
Reinforceme
nt learning
There are many opportunities are available for Machine Learning.
Voice
Reorganization
Image
Recognition
Optical
Character
...
• The following are some of the features of a Machine Learning model that needs to be
tested/quality assurance:
1. Quality...
• Quality assurance is a set of practices that allow
you to assess the state of the System and improve
it.
• Quality assur...
• There is quality assurance not the particular official role for the machine learning.
• Here some cases when preparing d...
• Software testing will be one of the most critical factors that determine the success of a
machine learning system.
• Tes...
• Testing will be used for the performed for securing the high performance of machine
learning models.
• the main problems...
https://www.nexsoftsys.com/articles/how-to-perform-quality-assurance-
and-testing-for-ml-projects.html
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Easily apply Quality Assurance and Testing in the ML Project

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Testing and Quality assurance is the most important and critical part of a machine learning project. Here in this ppt, you can get more idea about How to Do Testing of Machine Learning Projects?

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Easily apply Quality Assurance and Testing in the ML Project

  1. 1. How to Do Testing of Machine Learning Projects?
  2. 2. • ML Stands for Machine Learning. • Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. • Machine learning is affected by computer programs that automatically improve their performance through experience. • Machine learning is a subset of artificial intelligence. In the machine, learning computers don’t have to be explicitly programmed but can change and improve their algorithms by themselves. • Machine Learning is changing the way software products and applications think and respond to queries.
  3. 3. 1950 Alan Turning Created a test to check if a machine could fool a human being into believing it was taking to a machine 1952 The first computer learning program, a game of checkers, was written by Arthur Samuel. 1957 First neural network for computers was invented by Frank Rosenblatt, which simulated the thought processes of the human brain. 1967 The Nearest Neighbor Algorithm was written. 1979 Students of Stanford University, California, invented the Stanford Cart which could navigate and avoid obstacles on its own. 1997 IBM's Deep Blue beats the world champion at Chess. 2002 A Software library for Machine Learning, named torch is first released. 2016 Alpha Go algorithm developed by Google Deep Mind managed to win five games out of five in the Chinese Board Game Go competition.
  4. 4. Machine Learning Supervised Learning Unsupervised learning Semi- supervised learning Reinforceme nt learning
  5. 5. There are many opportunities are available for Machine Learning. Voice Reorganization Image Recognition Optical Character Recognition Sensory Data Analysis Intelligent Data Analysis Advanced Customization
  6. 6. • The following are some of the features of a Machine Learning model that needs to be tested/quality assurance: 1. Quality Of data 2. Quality of Features 3. Quality Of ML algorithms
  7. 7. • Quality assurance is a set of practices that allow you to assess the state of the System and improve it. • Quality assurance is the process of checking mistakes and errors manufactured products and avoiding the problem when delivering products or services to customers. • There is Quality assurance have the following approaches. • 1) Failure testing • 2) Statistical control • 3) Total quality management and many others.
  8. 8. • There is quality assurance not the particular official role for the machine learning. • Here some cases when preparing data for machine learning • There might be categorical (Textual, Boolean) values in the data set and not all algorithms work great with textual values. • Some features strength have higher values than others and are expected to be changed for equal importance. • Some time data will take the large dimensions and it will reduce after some time.
  9. 9. • Software testing will be one of the most critical factors that determine the success of a machine learning system. • Testing of the machine learning is not same as the testing process because in Machine Learning Testing, looking for exactly the right output is exactly the wrong approach. and generally in a testing situation, you seek to make sure that the actual output matches the expected one.
  10. 10. • Testing will be used for the performed for securing the high performance of machine learning models. • the main problems you will encounter while dealing with machine learning are:  Understanding the questions being asked  Understanding the data supplied  Understanding the measure of success
  11. 11. https://www.nexsoftsys.com/articles/how-to-perform-quality-assurance- and-testing-for-ml-projects.html Original Source

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