Easily apply Quality Assurance and Testing in the ML Project

1. How to Do Testing of Machine Learning Projects?

2. What is ML? • 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. HISTORY OF ML 1957 First neural network for computers was invented by Frank Rosenblatt, which simulated the thought processes of the human brain. 1950 Alan Turning Created a test to check if a machine could fool a human being into believing it was taking to a machine 1979 Students of Stanford University, California, invented the Stanford Cart which could navigate and avoid obstacles on its own. 2002 A Software library for Machine Learning, named torch is first released. 1952 The first computer learning program, a game of checkers, was written by Arthur Samuel. 1967 The Nearest Neighbor Algorithm was written. 1997 IBM's Deep Blue beats the world champion at Chess. 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. TYPES OF ML Machine Learning Supervised Learning Unsupervised learning Semi-supervised learning Reinforcement learning

5. Opportunities of ML There are many opportunities are available for Machine Learning. Image Recognition Advanced Customization Voice Reorganization Intelligent Data Analysis Sensory Data Analysis Optical Character Recognition

6. What Tested With ML? • The following are some of the features of a Machine Learning model that needs to be tested/quality assurance: • Quality Of data • Quality of Features • Quality Of ML algorithms

7. What is Quality Assurance? • 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. Role of QA in ML • 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. What is Machine Learning Testing? • 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. Role of Testing in ML • 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

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