Easily apply Quality Assurance and Testing in the ML Project
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 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.
Alan Turning Created a
test to check if a
machine could fool a
human being into
believing it was taking
to a machine
The first computer
learning program, a
game of checkers, was
written by Arthur
First neural network
for computers was
invented by Frank
simulated the thought
processes of the
The Nearest Neighbor
Algorithm was written.
Students of Stanford
invented the Stanford
Cart which could
navigate and avoid
obstacles on its own.
IBM's Deep Blue beats
the world champion at
A Software library for
named torch is first
Alpha Go algorithm
developed by Google
Deep Mind managed to
win five games out of
five in the Chinese
Board Game Go
There are many opportunities are available for Machine Learning.
• The following are some of the features of a Machine Learning model that needs to be
1. Quality Of data
2. Quality of Features
3. Quality Of ML algorithms
• Quality assurance is a set of practices that allow
you to assess the state of the System and improve
• 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
• 1) Failure testing
• 2) Statistical control
• 3) Total quality management and many others.
• 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.
• 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.
• Testing will be used for the performed for securing the high performance of machine
• 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