Quick Guide to Artificial Intelligence

What is Artificial Intelligence??

- When most people hear the term Artificial Intelligence the first thing that comes to their mind is the fascinating robots. That’s because of the big budget films and novels weave stories about human-like machines that wreak havoc on earth.

- Artificial Intelligence commonly known as AI refers to the simulation of human intelligence in machines that are programmed to think like humans and also mimic their actions. This can also be termed as machines exhibiting traits associated with the human mind such as problem solving and learning.

- The Ideal characteristic of Artificial Intelligence is its ability to rationalize and take actions which are best for achieving goals.

  • The goals of Artificial Intelligence includes learning, reasoning and perception.

Subfields Of Artificial Intelligence:

- Artificial Intelligence can be considered superset of Machine Learning and Machine Learning can be considered superset of Deep Learning.

- Machine Learning: It is a subset of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. It builds models based on sample data basically called as training data. It is widely used in applications like email filtering ,Online Fraud Detection.

  • Deep Learning: It is a subset of Machine Learning in Artificial Intelligence that uses deep neural networks which are capable of learning unsupervised from data that is unstructured or unlabeled. It is widely used in the field of Natural Language Processing, Speech recognition, audio recognition, etc.

What are the Prerequisites of Artificial Intelligence??

- Before diving into any field it’s necessary to know the prerequisites of that field. So let’s learn prerequisites of Artificial Intelligence:

- Strong knowledge of Mathematics.

- Good command over programming languages.

- Good Analytical skills.

- Ability to understand complex algorithms.

- Basic knowledge of statistics and modelling.

- Good command on Python or R language.

- Basics ideas regarding data extraction, data manipulation and data visualization.

- Basic ideas regarding supervised and non-supervised learning.

- Basic ideas on Machine Learning Algorithms.

- Basic ideas regarding Neural Networks.

- Basic ideas on Deep Learning Algorithms.

  • Must have the ability to write algorithms to find patterns and learning.

Online Courses and Certification for Artificial Intelligence:

- Introduction to Artificial Intelligence (Offered by IBM):This online course is for everyone and no prior background in Artificial intelligence required. You will gain basics of Machine Learning, Deep Learning, Data Science And various job opportunities.You will also demonstrate AI in action with a mini project.You’ll also receive a certificate from IBM after completion of course.

- IBM Applied AI Professional Certificate:This online course will give you a firm understanding of AI technology, its applications, and its use cases. You will become familiar with concepts and tools like machine learning, data science, natural language processing, image classification, image processing, IBM Watson AI services, OpenCV, and APIs.In addition to earning a Professional Certificate from Coursera, you’ll also receive a digital badge from IBM recognizing your proficiency in applied AI after completion of course.

- AI Foundations for Everyone Specialization:This specialization course is designed for those with little or no background in AI. It is designed to give you a firm understanding of what is AI, its applications and use cases across various industries. You will become acquainted with terms like Machine Learning, Deep Learning and Neural Networks. it will familiarize you with IBM Watson AI services that enable any business to quickly and easily employ pre-built AI smarts to their products and solutions.You all will receive a certificate from IBM after the completion of course.

- Key Technologies for Business Specialization: This online course is offered by IBM.The courses in this Specialization provide foundational knowledge of Cloud, Data and AI, including business drivers behind their growth, the value they provide, their applications and use cases, and an understanding of how these technologies work. This specialization is suitable for all including beginners, Managers and executives. You will get a certificate after completion of course.

- IBM AI Foundations for Business Specialization:This specialization course will explain and describe the overall focus areas for business leaders considering AI-based solutions for solving business problems. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second course will introduce the technologies and concepts in data science. The third course will introduce the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions. You will gain a professional certificate from IBM after completion of course.

Online Courses and certification in Machine Learning:

  • Machine Learning (Offered by StandFord University): This is one of the best courses available online for learning Machine Learning. The course is taught by Andrew Ng who is co-founder of Coursera one of the top instructors in the world for Data Science. This course will provide a broad introduction to machine learning, datamining, and statistical pattern recognition. You will be gaining a professional certificate after completion of course.
  • Machine Learning A-Z in (Python or R): This course is offered by Udemy. This course has been designed by two professional Data Scientists Kirill Eremenko and Hadelin de Ponteves. You all will gain constant support throughout the course from the SuperDataScience team. You will gain a certificate from Udemy after completion of this course.

Career opportunities in Artificial Intelligence:

  • Machine Learning Engineer: A Machine Learning Engineer creates programs and algorithms that enable machines to take actions without being directed. One of the best examples is designing a self driving car.
  • Data Scientist: Data Scientists are responsible for solving vexing problems. Combining computer science, modelling, statistics, analytics and math skills along with sound business data scientist helps organizations to make objective decisions.
  • Machine Learning Architect: Machine Learning Architects are chief data scientists responsible for choosing the right technologies and evaluating the evolution of the architecture as the client needs change.
  • AI Engineer: An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organization development.
  • Data Analyst: A Data Analyst collects, organises and interprets statistical information to make it useful for a range of businesses and organisations.


  • With this we came to the end of our article on Artificial Intelligence. I hope you all have an idea on how to start your career in the field of Artificial intelligence.



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