Artificial Intelligence Techniques, Languages and Top Free Online Courses


AI techniques are methods that can be utilized to develop and make PC programs generally saw as types of artificial intelligence. By and large, artificial intelligence alludes to a program that can copy or re-make the perspectives showed by the human mind. This ordinarily includes taking care of problems, mentioning observable facts or getting contribution for use in investigation or problem explaining, and the capacity to arrange and distinguish distinctive items and the properties of those objects.

What is Artificial Intelligence?

AI as opposed to natural intelligence is the intelligence (or decision-making skills) that are synthetically generated. Any human tasks that can be automated come under the AI umbrella. Some of these tasks could be quite simple like finding the shortest distance between two points, some could be complex like playing a chess game, others could be extremely difficult like driving a car. An AI system can be broadly split into two unofficial categories:


What is Artificial Narrow Intelligence?

Artificial Narrow Intelligence (ANI) refers to an AI system that can perform a specific task at near-human or super human-level accuracy. Examples include playing Chess, Speech Recognition, Image Recognition, etc. This is our current stage of AI development.

What is Artificial General Intelligence?

Artificial General Intelligence (AGI) is a theoretical AI system that can perform multiple complex human-level tasks i.e. a single system that should be able to do what a human can do including walking while simultaneously holding a conversation, able to associate lectures in class to the text in the books, etc.

What is Machine Learning?

Machine Learning is the process in which a system identifies patterns and relations from data to obtain an optimal solution. Machine Learning is a subset of AI. In a nutshell, an AI system could be created using machine learning algorithms.

Eg: Building a recommendation engine would involve using machine learning algorithms like association rules, decision trees, etc… on customer data. The Recommendation Engine is an AI system, The process involves machine learning.AI is any system that automates human tasks, some AI tasks involve machine learning to achieve it.

  • ML stands for Machine Learning which is defined as the acquisition of knowledge or skill

  • The aim is to increase accuracy, but it does not care about the success

  • It is a simple concept machine takes data and learns from data.

  • The goal is to learn from data on certain tasks to maximize the performance of machines on this task.

  • ML allows the system to learn new things from data.

  • It involves creating self-learning algorithms.

  • ML will go for the only solution for whether it is optimal or not.

  • ML leads to knowledge.

Source: ValueLabs (Company), Expert in Data Science


Types of Machine learning:

  • Supervised

  • Unsupervised

  • Semi-Supervised

  • Reinforcement

  • Transfer learning

What is Deep Learning?
Deep learning is a branch of machine learning which is completely based on artificial neural networks, as the neural network is going to mimic the human brain so deep learning is also a kind of mimic of the human brain. In deep learning.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. 


The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture.


Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.

Common Types of Algorithm Learning/Training:

  • Regression is simply drawing a curve or line through data points.

  • Classification is determining to what group something belongs to. Binary classification (two groups) is determining if something belongs to a class or not, such as whether the animal in the picture is a dog or not. Sticking with the animal example, multiclass classification (more than two groups) is whether the animal is a dog, cat, bird, etc.

  • Clustering is similar to classification, but you don’t know the classifications ahead of time. Again using the examples of animal pictures, you may determine that there are three types of animals, but you don’t know what those animals are, so you just divide them into groups. Generally speaking, clustering is used when there is insufficient supervised data or when you want to find natural groupings in the data without being constrained to specific groups, such as dogs, cats, or birds.

  • Time series assumes that the sequence of data is important (that the data points taken over time have an internal structure that should be accounted for). For example, sales data could be considered time-series because you may want to trend revenue over time to detect seasonality and to correlate it with promotional events. On the other hand, the order of your animal pictures doesn’t matter for classification purposes.

  • Optimization is a method of achieving the best value for multiple variables when they do not move in the same direction.

  • NLP (natural language processing) is the general category of algorithms that try to mimic human use and understanding of languages, such as chatbots, scrubbing unstructured writing like doctor’s notes for key data fields, and autonomous writing of news articles.

  • Anomaly detection is used to find outliers in the data. It is similar to control charts but uses lots more variables as inputs. Anomaly detection is especially useful when “normal” operating parameters are difficult to define and change over time, and you want your detection of abnormalities to adjust automatically.


What are the 5 Most Common AI techniques?

  1. Heuristic.

  2. Support vector machines.

  3. Artificial neural networks.

  4. Markov decision process.

  5. Natural language processing.


  • It is one of the most popular search algorithms used in Artificial Intelligence.

  • It is implemented to solve problems faster than classical methods or to find solutions for which classical methods cannot.

  • Heuristic techniques basically employ heuristics for their movements and are used to reduce the total number of alternatives to the results.

  • This technique is one of the most basic techniques used for AI and is based on the trial and error principle. Learn from mistakes.

  • Heuristics are one of the best options for solving difficult problems. For example, to know the shortest route to any destination, the best way is to identify all possible routes and then the shortest.


Support Vector Machines

  • Support Vector Machine is a supervised machine learning algorithm used for regression challenges or classification issues.

  • However, in most cases it is only used for rating, for example, email systems use vector machines for email ratings like Social or Promotion or any other. It categorizes each mail according to its categories.

  • This technique is widely used for face recognition, text recognition, and image recognition systems.


Artificial neural network

  • Neural networks are usually found in the brains of living organisms.

  • These are basically the neural circuits that help living things transmit and process information.

  • To this end, there are billions of neurons that help create neural systems to make day-to-day decisions and learn new things.

  • These natural neural networks inspired the design of an artificial neural network. Instead of neurons, artificial neural networks are composed of nodes.

  • These networks help identify patterns from the data and then learn from them.

  • To this end, it uses different learning methods, such as supervised learning, unsupervised learning and reinforced learning.

  • From an application standpoint, it is used in machine learning, deep learning and pattern recognition.

Markov Decision Process

  • A Markov Decision Process (MDP) is a framework for decision-making modeling, wherein some situations the result is partly random and partly based on input from the decision-maker.

  • Another application where MDP is used is optimized planning. The basic objective of the MDP is to find a policy for the decision-maker, indicating what specific action should be taken in what state.

  • An MDP model consists of the following parts:

  • A set of possible states: For example, this may refer to the world of a robot's grid or the states of a door (open or closed).

  • A set of possible actions: A fixed set of actions that, for example, a robot can take, such as going north, left, south, or west. Or in relation to a door, closing or opening.

  • Transition Probabilities: This is the probability of going from one state to another. For example, what is the probability that the door will be closed after closing the door?

  • Rewards: These are used to direct planning. For example, a robot may want to go north to reach its destination. In fact, going north will result in a bigger reward.

Natural Language Processing

  • Basically, it is a technique used by computers to understand, interpret and manipulate human language. Going by its use, it is useful for speech recognition and synthesis.

  • This technique is already used for many applications by a multitude of companies. Apple Siri, Google Assistant, Cortana, and Alexa from Microsoft are some of the applications that use natural language processing techniques.

  • In addition, it is also used for parsing, part of speech, and text recognition techniques.


What are the Top 5 Programming AI Languages?

Here we look at the best five programming languages for artificial intelligence development. It is a big concept, so it is very hard to refer to a single programming language.


In artificial intelligence, Python is one of the most widely used programming languages because of its simplicity. The main use is for AI algorithms and data structure. It has a lot of useful libraries that are useful for AI development. For example, for advanced computing, Skype is used. For scientific computation capability, Numpy is used and for machine learning. There are tons of resources available online for AI using



Java is an Object-oriented programming language, so it is a great choice. This language provides all high-level features needed to work on AI projects. It offers inbuilt garbage collection, and it is a portable language. The plus point with Java community there will be somebody to assist you with your queries and effort. AI is full of the algorithm, so Java is the best choice it provides an easy way to code good algorithms. You can develop algorithms like search algorithms, natural language processing algorithm or neural algorithms. Java has the feature of scalability which best for AI projects. Java is still not as high level as Prolong and Lisp and not faster than C.



Lisp is the programming language developed between the 1970s and 1980s. It’s a great programming language used in large AI projects, such as Macsyma, DART, and CYC. Because of its best prototyping capabilities and its support for symbolic expression Lisp is used in AI field. This language is used in Machine Learning/ILP subfield because of its usability and symbolic structure. Lisp is the top programming language in AI field because of its best features. Lisp language has a feature of automatic garbage collection with the dynamic creation of new objects. Lisp generates efficient code with well development compilers. This language has a macro system that lets developers create a domain-specific level of abstraction on which to build the next level. Because of these features, Lisp excels compared to another language.



Prolog is an excellent programming language for artificial Intelligence. Some basic features of Prolog which are extremely useful for AI programming. It offers tree-based data structuring mechanisms, automatic backtracking and pattern matching combining these mechanisms provides a flexible framework to work with artificial intelligence. In the expert system of AI Prolog is extensively used for working on medical projects. Unlike traditional programming language, Prolog is a high-level programming language based on formal logic. It is a language performing sequence of commands and solving logical formulas. As its program consists of list facts and rules, it is rule-based as well as declarative language.


C++ is the greatest object-oriented programming language in the world. For AI project of the time, sensitive C++ is extremely useful. This language can talk at the hardware level and allows developers to progress their program execution time. For statistical AI techniques such as neural networks, C++ is the preferred language. The search engine can utilize C++ widely. Games in AI mostly coded with C++ for speedy execution and response time.

Summary: Before deciding a programming language for artificial intelligence makes sure that it can be utilized not partially but extensively. Freelance services are available in all of these programming languages. Also preferring a programming language for your AI project depends upon subfield. Python is well-known due to its flexibility, C++ and Java are also useful because of the best features they offer. Lisp and Prolog are always being used extensively because of their productive features.


AI can be used ideally for these purposes:

  • Biometrics

  • Decision Management

  • ​Machine Learning Platforms

  • Speech Recognition

  • Robotic Process Automation

  • Text Analytics and NLP

Final Remarks about artificial intelligence

A computer can be said to be intelligent if it can achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator.  In order to be artificially intelligent and pass the computer should possess the following,

  • Natural language processing to enable it to communicate successfully in English (or some other human language).

  • Knowledge representation to store the information provided before or during the interrogation.

  • Automated reasoning to use the stored information to answer questions and to draw new conclusions.

  • Machine learning to adapt to new circumstances and to detect and extrapolate patterns.

Artificial Intelligence is a broader class that includes Machine Learning.

[2] Artificial Intelligence, A modern approach by Stuart. J. Russell and Peter Norvig

The 10 Best Free Online Artificial Intelligence And Machine Learning Courses For 2020


Bernard Marr


Enterprise Tech

Elements of AI - Helsinki University

This is an elementary-level class aimed at anyone who wants to understand what AI does, how it might affect them, and what it can be used for, without getting involved in the underlying mathematics and statistics. It demonstrates that in-depth knowledge of those fields isn't necessary to start taking advantage of the opportunities offered by AI and machine learning, and includes practical exercises. Originally available only in Finland (and in Finnish) as part of the government's drive to educate its population on AI, last year, the decision was made to make it available to the world.

Learn with Google AI

This collection of tutorials, guides, and resources has grown considerably since I last highlighted it. As well as basic tutorials covering the fundamentals, it also offers instructions on applying AI and ML to social, environmental and humanitarian challenges, as well information on how to ensure that your AI implementations are both ethical and “people-centric". This all helps to build a broad understanding of the many factors – technological or otherwise – that are important to consider when considering how AI could work for you.

Intro to Artificial Intelligence - Udacity

This course starts with the fundamentals of statistics and logic before progressing to discussing more applied, specific uses of AI, including robotics, computer vision, and natural language processing. It is taught by two experienced AI researchers, Peter Norvig and Sebastian Thrun, and is designed to take around four months to complete.

Machine Learning – Stanford University (Coursera)

Often cited by AI experts as the single most important online resource for anyone wanting to learn AI, this course is led by Andrew Ng, who founded Google’s pioneering Google Brain deep learning program. This course gives a thorough grounding in the mathematical, statistical, and computer science fundamentals that go into developing and deploying automated learning machines.

AI for Everyone – Andrew Ng (Coursera)

Another course from Andrew Ng – this one explicitly aimed at those who don’t need an in-depth technical understanding of the subject but who may want to begin leveraging AI in their organizations or working to roll out AI initiatives while working with non-technical teams. It covers the workflow of running AI projects as well as how to develop a strategy around AI deployments in business.

Data Science and Machine Learning Essentials – Microsoft (EdX)

This course is also listed in my guide to the best free data science courses. Of course, there's a great deal of crossover between the two subjects, as data science is the foundation of all of today's AI. If you're confused about the terminology, then think of machine learning as a technique that leverages data science to work towards achieving what we currently understand as AI. This course gives a great overview as it starts by explaining the core data science concepts before moving on to demonstrate how they are applied in machine learning.

Machine Learning Crash Course - Google

Another Google course, and this one is said to be required reading for everyone whose work is involved with AI at the tech giant. This course covers the basics but also moves onto the theory and practical applications of TensorFlow, Google’s open-source deep-learning library that it uses in many of its own AI-enhanced services and projects.

Learning From Data (Introductory Machine Learning) - Caltech (EdX)

Starting with theoretical principles such as "what is learning?" and "can machines learn?" this course covers advanced practical applications including creating ML algorithms used to power neural networks. It aims to help those who are set on a career as a data scientist or analyst. Like many of the courses covered here, all of the materials are freely available, but you can pay $50 for official certification at the end.

Artificial Intelligence A-Z: Learn How To Build An AI - Udemy

Another course that takes a slightly different approach, here you are taken through the practical steps necessary to build machines that solve a number of real-world AI problems, such as driving a car or playing a game. It also covers Q-learning, a form of machine learning based on reinforcement learning, that is gaining in popularity in cutting-edge applications.

Creative Applications of Deep Learning With Tensorflow – Kadenze (Class Central)

Deep learning is one of the most advanced fields of AI, and one that is pushing the boundaries of creating machines that can think and learn like humans. This is another course focused on the open-source TensorFlow framework originally created by Google for use in Deep Learning, and is one that has received good reviews for giving an easy-to-follow guide to a complex technical subject.  



AI Techniques and Languages
Best Ai Techniques for Computer Science


Thanks for your interest. For more information, feel free to get in touch and I will get back to you soon!

Chesapeake, VA

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