Today’s digital world
relies heavily on our ability to build intelligent and smart systems by
deploying artificial intelligence successfully. One of the applications of AI
is Machine Learning which supports systems and enables them to learn and
improve from experience. It focuses on developing programs that can access data
and use it to draw decisions without being programmed as such. In the growing
field of data science, machine learning has tremendous applications. By using
statistical methods, programmers write algorithms and train them to classify
data and make predictions. It is used to uncover deep data insights and use
them to drive decisions. Machine Learning has a growing impact on global
business applications and with greater impact, comes a greater scope for job
opportunities in this field.
How does machine learning work?
According to UC
Berkley, the algorithm of a machine learning model that enables the system to
make predictions is divided into three parts
1. A Decision
Process – Making predictions
or classifying data is the first step of a machine learning model. Depending on
what data we input, it is possible to label it by producing an estimate about
patterns in data and how closely they are related to something that the model
has seen before.
2. An Error
Function – It serves to
evaluate the accuracy of the model by checking if the prediction is correct.
3. An Model
Optimization Process – In
order to fit the machine learning model to the data points used in the training
set, we can adjust the weights to get rid of the inconsistency in the
predictions. The algorithm is trained such that it will repeat this process to
optimize the model.
Applications of machine learning
There are numerous
applications of machine learning including:
1. Speech recognition
– This model is used in
converting speech to text and visa-versa and uses a natural language process to
process speech into a written format. A common example of this is included in
many mobile phones to perform a voice search. For e.g. Siri, Alexa, etc.
2. Customer Service
– With the introduction of
online chatbots, the services have improved across businesses. Redundant tasks
are recognized and replaced with bots to provide a better and seamless flow to
users. They answer all frequently asked questions and take the place of a
virtual assistant to make our lives easier.
3. Computer Vision
– The advancement of AI
technology has led programmers to derive useful information from digital
images, assets, videos, and visual inputs. Computer vision helps in taking the
action based on the inputs and is powered by convolutional neural networks.
This technology is being used in computer vision applications such as photo
tagging, radiology imaging, etc.
Best Machine Learning courses in the
market
1. Google AI – ML
Crash Course
2. Coursera – Machine
Learning with Python
3. Coursera – Deep
Learning Specialization
4. EdX – Machine
Learning
5. Fast.ai –
Introduction to Machine Learning for Coders
6. Coursera – Advanced
Machine Learning Specialization
7. Udemy – Machine
Learning
8. Udacity – Machine
Learning for Beginners
