Artificial Intelligence Tutorial for Beginners


Artificial Intelligence Tutorial for Beginners

What is AI?
A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solve problems are deemed to hold an artificial intelligence.

Artificial intelligence exists when a machine has cognitive ability. The benchmark for AI is the human level concerning reasoning, speech, and vision.

In this basic tutorial, you will learn-

What is AI?
Introduction to AI Levels
A brief History of Artificial Intelligence
Type of Artificial Intelligence
Where is AI used? Examples
Why is AI booming now?

Introduction to AI Levels

Narrow AI: An artificial intelligence is supposed to be narrow when the machine can perform a particular undertaking better than a human. The current research of AI is here at this point

General AI: An artificial intelligence reaches the general state when it can perform any learned errand with a similar accuracy level as a human would

Strong AI: An AI is strong when it can beat people in numerous assignments

These days, AI is utilized in practically all industries, giving an innovative edge to all organizations integrating AI at scale. According to McKinsey, AI can possibly create 600 billions of dollars of significant worth in retail, bring 50 percent more incremental incentive in banking compared with other analytics procedures. In transport and strategic, the potential revenue bounce is 89 percent more.

Concretely, if an organization utilizes AI for its marketing group, it can computerize unremarkable and repetitive errands, permitting the salesman to zero in on assignments like relationship building, lead nurturing, and so on. An organization name Gong provides a conversation intelligence service. Each time a Sales Representative settle on a telephone decision, the machine records transcribes and dissects the visit. The VP can utilize AI analytics and recommendation to formulate a triumphant strategy.

More or less, AI provides a forefront innovation to manage complex data which is difficult to deal with by an individual. Simulated intelligence robotizes redundant positions permitting a worker to zero in on the elevated level, esteem included errands. At the point when AI is actualized at scale, it prompts cost reduction and revenue increase.

A brief History of Artificial Intelligence

Artificial intelligence is a buzzword today, despite the fact that this term isn’t new. In 1956, a group of vanguard experts from different backgrounds chose to organize a summer research project on AI. Four bright personalities drove the project; John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

The primary purpose of the research project was to handle “every part of learning or some other feature of intelligence that can in principle be so precisely described, that a machine can be made to reproduce it.”

The proposal of the summits included

  • Programmed Computers
  • By what method Can a Computer Be Programmed to Use a Language?
  • Neuron Nets
  • Self-improvement

It prompted the possibility that shrewd computers can be created. Another era started, brimming with trust – Artificial intelligence.

Type of Artificial Intelligence

Artificial intelligence can be divided into three subfields:

  • Artificial intelligence
  • Machine learning
  • Deep learning


Machine Learning

Machine learning is the art of study of algorithms that learn from examples and experiences.

Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.

The difference from hardcoding rules is that the machine learns on its own to find such rules.

Deep learning

Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it means the machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. For instance, Google LeNet model for image recognition counts 22 layers.

In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.

AI vs. Machine Learning

Most of our smartphone, daily device or even the internet uses Artificial intelligence. Very often, AI and machine learning are used interchangeably by big companies that want to announce their latest innovation. However, Machine learning and AI are different in some ways.

AI- artificial intelligence- is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own.


Artificial Intelligence is a computer that is given human-like properties. Take our brain; it works effortlessly and consistently to ascertain the world around us. Artificial Intelligence is the idea that a computer can do likewise. It can be said that AI is the large science that imitates human aptitudes.

Machine learning is a particular subset of AI that trains a machine how to learn. Machine learning models search for patterns in data and try to finish up. Basically, the machine shouldn’t be explicitly programmed by individuals. The programmers give a few models, and the computer will learn what to do from those examples.

Where is AI utilized? Models

Simulated intelligence has broad applications-

Artificial intelligence is utilized to reduce or maintain a strategic distance from the repetitive assignment. For example, AI can repeat an undertaking constantly, without exhaustion. Truth be told, AI never rests, and it is indifferent to the errand to carry out

Artificial intelligence improves a current product. Before the time of machine learning, core products were expanding upon hard-code rule. Firms introduced artificial intelligence to upgrade the functionality of the product rather than starting from scratch to plan new products. You can think about a Facebook picture. A couple of years prior, you needed to label your friends physically. These days, with the assistance of AI, Facebook gives you a friend’s recommendation.

Man-made intelligence is utilized in all the industries, from marketing to gracefully chain, fund, food-processing sector. According to a McKinsey survey, budgetary services and cutting edge correspondence are driving the AI fields.


Why is AI booming now?


A neural network has been out since the nineties with the seminal paper of Yann LeCun. However, it started to become famous around the year 2012. Explained by three critical factors for its popularity are:

  1. Hardware
  2. Data
  3. Algorithm

Machine learning is an experimental field, meaning it needs to have data to test new ideas or approaches. With the boom of the internet, data became more easily accessible. Besides, giant companies like NVIDIA and AMD have developed high-performance graphics chips for the gaming market.


Over the most recent twenty years, the power of the CPU has detonated, permitting the user to train a little profound learning model on any PC. However, to process a profound learning model for computer vision or profound learning, you need a more powerful machine. On account of the speculation of NVIDIA and AMD, another generation of GPU (graphical processing unit) are accessible. These chips permit parallel calculations. It implies the machine can separate the calculations over several GPU to accelerate the estimations.

For example, with a NVIDIA TITAN X, it takes two days to train a model called ImageNet against weeks for a traditional CPU. Moreover, big organizations use clusters of GPU to train profound learning model with the NVIDIA Tesla K80 on the grounds that it assists with reducing the data center cost and provide better performances.


Profound learning is the structure of the model, and the data is the liquid to make it alive. Data powers the artificial intelligence. Without data, there is no hope. Latest Technologies have pushed the boundaries of data storage. It is easier than at any other time to store a high measure of data in a data center.

Internet revolution makes data assortment and distribution accessible to take care of machine learning algorithm. In the event that you are familiar with Flickr, Instagram or some other application with pictures, you can figure their AI potential. There are a large number of pictures with labels accessible on these websites. Those pictures can be utilized to train a neural network model to recognize an article on the picture without the need to physically gather and mark the data.

Artificial Intelligence combined with data is the new gold. Data is an interesting competitive favorable position that no firm should disregard. Artificial intelligence provides the most fitting answers from your data. At the point when all the firms can have similar advancements, the one with data will have a competitive preferred position over the other. To give a thought, the world creates about 2.2 exabytes, or 2.2 billion gigabytes, every day.

An organization needs extraordinarily diverse data sources to have the option to discover the patterns and learn and in a considerable volume.


Hardware is more powerful than at any other time, data is effectively open, however one thing that makes the neural network more reliable is the advancement of more accurate algorithms. Primary neural networks are a basic increase matrix without top to bottom measurable properties. Since 2010, remarkable discoveries have been made to improve the neural network

Artificial intelligence utilizes a progressive learning algorithm to let the data do the programming. That is to say, the computer can show itself how to perform different assignments, such as discovering peculiarities, become a chatbot.


Artificial intelligence and machine learning are two befuddling terms. Artificial intelligence is the study of training machine to imitate or reproduce human assignment. A researcher can utilize different strategies to train a machine. Toward the start of the AI’s ages, programmers wrote hard-coded programs, that is, type every consistent possibility the machine can face and how to respond. At the point when a framework grows complex, it gets hard to deal with the rules. To overcome this issue, the machine can utilize data to learn how to deal with all the situations from a given environment.

The most important features to have a powerful AI is to have enough data with considerable heterogeneity. For instance, a machine can learn different dialects as long as it has enough words to learn from.

Artificial intelligence is the new bleeding edge innovation. Ventures capitalist are putting billions of dollars in startups or AI project. McKinsey gauges AI can support every industry by in any event a twofold digit growth rate.