Welcome back.
In this video we're going to learn about real world examples of machine learning.
The usage of ML is increasing with every passing day.
This technology is spearheading into the future with exceptional possibilities and various use cases.
The first field we can use machine learning is the facial recognition.
Facial recognition is a big use case and benefit of ML.
It has positively transformed various aspects of our everyday tasks and activities.
This example paired with deep learning is leaving an impact on different industries and sectors.
It aids in combating key social issues like detecting thieves, trafficking and smuggling in healthcare.
It tracks the patient's history of drug abuse, medication and detects genetic diseases.
The second use is self-driving technology.
ML powers the self-driving technology, wherein sensors are used to collect data surrounding the car
in real time.
This real time data is employed to guide the car and navigate its response in varying situations.
These could be an animal human crossing the road, red light, or another vehicle.
The third use the natural language processing.
Speech signals are supported and manipulated into text and commands by language models.
This is pretty similar to how ML recognizes images, as software that is coded with AI has the potential
to convert live and recorded speech into text files.
So this is the usage of ML to transform the recorded speech or live speech into text files.
Another ML usage is the social media optimization.
All big platforms like Facebook, Twitter, and Instagram utilize AI, ML, and big data to enhance
the user experience and offer better functionality.
ML has played a big role where here in tackling cyberbullying and inappropriate content.
Both there those risks the platform endangering its integrity and getting a bad image.
When data is processed via deep neural network DNNs, these platforms get a better understanding of
their user preferences.
This leads to target advertising and content suggestions.
Another usage spam filtering.
Successful implementation of ML leads to email automation and spam filtering.
This area is especially in focus for organizations that rely mostly on outbound leads.
Spam filtering means getting a better grasp of the patterns that make the email content undesirable.
Then we have the product recommendations.
Targeted marketing in retail uses ML to learn more about user preferences.
The third, demographic similarities, buying habits, and purchasing history are used to train the
models.
Depending on how good quality and accurate the data is, the predictions can either be bang or way off.
Not buying or clicking through a product also creates a data point for future preference.
Those are the most famous usage of machine learning.