Eyes on AI - Episode 01

Apr 04, 2021

What is machine learning? It is an emerging field in data science and is a branch of artificial intelligence. Machine learning involves processing data in order to make the systems learn from available data and make decisions upon them. There are three major types of machine learning as supervised learning, unsupervised learning, and reinforcement learning. There are many projects and innovations that one can come up with by studying machine learning. We can apply the techniques of machine learning in almost any instance in the real life. It would be simply amazing when the machines can communicate with human thoughts to make human lives a whole lot easier.


Science of understanding the underlying meaning of data

  • Pattern mining/trend analysis - Analyzing available data and making predictions based on them (applications: Understanding future probabilities in sales)
  • Business intelligence - Coming up with business solutions by analyzing business-related data using technology. (applications: Amazon, Twitter, Uber, Starbucks)
  • Personalization - To give a unique experience with customized solutions for each individual involved with the system. These use special algorithms to handle the data in the most probable user-friendly way. (applications: Personalized marketing, website personalization)
  • Healthcare informatics - Improving disease analysis, medical diagnosis, and pharmaceutical technologies by handling available data. (applications: disease diagnosis)
  • Autonomous driving - Driver less driving techniques
  • Expert opinions

Data Science

Flavors of Machine Learning

As the diagram shows, several areas together comprise the area of artificial intelligence. There are many approaches you can use in order to solve a problem using AI. You have to have a general understanding of the whole set of technologies in order to find out the best solution for you.

  1. Knowledge Base - A knowledge base contains all sorts of information related to a certain field. It can be used to give users instant information within seconds.
  2. Expert Systems - Uses what-if conditional analysis to make decisions based on a collection of data provided.
  3. Deep Learning - A subset of machine learning. Mostly used in the healthcare industry for finding conditions related to human neurons.
  4. Computer vision - Utilizes images and videos to supply machines with an understanding of real-life objects and people.
  5. Machine learning - Improving machine performance to make independent decisions based on data fed into the system.
  6. Natural Language Processing - Increasing interaction between human languages and computers.

A real-life example for the application of machine Learning


Thought controlled robotic arm
Controlling someone else's arm with your brain


Turing Test


In the Turing test, there is a particular human and another crowd in the audience. The crowd is in separate covered cubicles. Here, there is a computer in one of the covered cubicles too. The specific individual asks questions. All the humans and the computer gives answers to the questions. If the individual can differentiate who answered the question, human or computer, then the test is a failure. If the individual cannot specify who gave the answer, that is if he cannot differentiate the computer from the humans, then the intelligence level has passed the test.


Why we need data science?

Deep Learning


It is an emerging area and a lot of facts are still being discovered. One major concept here is CNN. The basis of deep learning relies on different human behaviors.

Recognition and Distinction


Humans naturally can recall seeing someone naturally when seeing particular people for the second time in their lives. We can distinguish between people, objects, animals, etc. But can a machine can do that? Would a machine be able to distinguish between a dog and a cat? From birth, humans are used to identifying colors, shapes, and objects. We are trained to identify between the colors, types of food, animals, etc. from our early childhood. That leads to the development of data storage and cognitive memory in a human all his life along. And now, the machines can be fed with data and can be trained to perform these recognition and distinction techniques on their own.

Demand for Data Science


The path to becoming a data scientist costs a lot of experience and hard work. Because of that, there is still a huge shortage of data scientists in the industry. The demand is almost everywhere. The construction industry, healthcare, finance, almost every field in the field are benefited from the contribution of data science.

Employer Demand

The top two technologies you should know to become a data scientist

  1. Fluency in Python
  2. TensorFlow

Crucial data scientist skills

  1. Geo spatial skills
  2. Computer vision
  3. Data storytelling
  4. Explainable AI
  5. Healthcare skills
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