The A-Z Guide To Artificial Intelligence with Certification For Complete Beginners In 2020

What is Artificial Intelligence?
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning and planning.



AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud.
At a very high level artificial intelligence can be split into two broad types
1.Narrow Artificial Intelligence
2.General Artificial Intelligence
What Can Narrow AI do?
Narrow AI is AI that is programmed to perform a single task — whether it's checking the weather, being able to play chess, or analyzing raw data to write journalistic reports. ANI systems can attend to a task in real-time, but they pull information from a specific data-set.
There are many examples of narrow AI around us every day, represented by devices like Alexa, Google Assistant, Siri, and Cortana. They include:
  • Self-driving cars
  • Facial recognition tools that tag you in pictures
  • Customer service bots that redirect inquiries on a webpage
  • Google’s page-ranking technology that determines which websites appear at the top of the search engine
  • Recommendation systems showing items that could be useful additions to your shopping cart based on browsing history
  • Spam filters that keep your inbox clean through automated sorting
Here are some of the barriers to ANI:
  • ANI needs a large amount of high-quality data to yield accurate results, and not all environments meet these data requirements.
  • The learning curve to institutionalize AI properly can be steep. Companies have to set up and train their staff on new processes and technologies.
  • If a task changes, the effectiveness of an ANI system decreases, since it is programmed for a specific purpose.
  • Sometimes, replacing humans with rules-based machines leads to greater frustration and lowers customer satisfaction—for example, in the hospitality industry, where guests value personalized service and human interaction. 


Artificial general intelligence is very different, and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, the likes of HAL in 2001 or Skynet in The Terminator, but which doesn't exist today and AI experts are fiercely divided over how soon it will become a reality.
Real Examples:
Is Siri narrow AI?
Siri is a narrow artificial intelligence algorithm that brings the functions of machine learning to the mobile platform of an iPhone. While Siri is helpful at completing various specific tasks, it is by no means a strong AI, and often has challenges with tasks outside its range of abilities.

Why is artificial intelligence important?

AI automates repetitive learning and discovery through data.
Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.

AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products.

I adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.

AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.

AI achieves incredible accuracy through deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

How Artificial Intelligence Works

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields: 

Artificial Intelligence Course | AI Training & Certification‎

Introduction to Artificial Intelligence

Offered by NPTEL

Commitment: 4 Hours per week.

What will you learn

This course covers the introductory concepts of Artificial Intelligence, inherent challenges in developing an Intelligent System, key paradigms of AI, core techniques and technologies.

This course is covered in around 40 lectures and would include the following areas of learning:

 -Introduction to AI and intelligent agents
 -Problem Solving by Searching, heuristic search techniques and other methods
 -Game Playing
 -Knowledge Representation
 -Planning, partial order planning
 -Introduction to Natural Language Processing

Who would benefit?

This course has been designed for the students and teachers of the Computer Science & Engineering department associated with Artificial Intelligence.
Since this course is just the stepping stone to the vast field of AI, it can help the aspirants to be familiarized with the concepts of Artificial Intelligence and its role in the fields of Science & Engineering.


This is a basic course to the concepts of Artificial Intelligence and developing an Intelligent System, its challenges and solutions. So, aspirants having concepts of Data Structures, Algorithms and basic mathematics can take up this course.


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