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Eligibility for Artificial Intelligence Courses

  • A bachelor's degree in computer science, IT, statistics, or STEM (science, technology, engineering, and mathematics)
  • It is required to have at least 50% marks in 10th and 12th grades
  • Proficiency in programming languages like Python, Java, or other languages
  • Familiarity with mathematical and statistical concepts such as linear algebra, probability theory, and hypothesis testing
  • Possession of soft skills like problem-solving, critical thinking, and time management
  • Eagerness to explore AI applications and innovations in the industry

EXPLORE OUR COURSES

Eligibility Criteria For Different AI Courses

Check out the detailed eligibility requirements for the different AI courses offered by Great Learning.

Learning Outcomes

  • Understand the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML)
  • Gain knowledge of various ML algorithms and their applications
  • Learn to build and train ML models using popular libraries and frameworks
  • Acquire skills to pre-process and analyse data for ML tasks
  • Develop the ability to evaluate ML models and make data-driven decisions
  • Demonstrate proficiency in implementing AI and ML techniques in real-world scenarios

Artificial Intelligence Courses Eligibility Criteria

 

Great Learning offers several world-class artificial intelligence courses to suit various needs and skill levels. The eligibility requirements for these programs vary based on the particular program and its prerequisites.

 

Here are the details on the eligibility criteria for each AI and Machine Learning course to help aspirants meet the prerequisites and succeed in their careers:

 

Course Name

University

Eligibility Criteria

AI and Machine Learning Course (Online)

The University of Texas at Austin (UT Austin) McCombs School of Business

A bachelor's degree in computer science, IT, statistics, STEM (science, technology, engineering, and mathematics), or any other related field

AI and Machine Learning Course (Classroom)

Great Lakes Executive Learning

A bachelor's degree in computer science, IT, statistics, STEM (science, technology, engineering, and mathematics), or any other related field

Data Science and Machine Learning

Massachusetts Institute of Technology Institute for Data, Systems, and Society (MIT IDSS)

Data scientists, data analysts, and working professionals who want to extract

actionable insights from massive volumes of data

 

Early career professionals and senior managers, such as technical managers, BI analysts, IT practitioners, management consultants, and business managers

 

Those with some academic/professional background in statistics/applied mathematics. Nevertheless, participants without this experience will need to put in extra effort and will receive assistance from Great Learning

AI for Leaders

The University of Texas at Austin (UT Austin) McCombs School of Business

The course is designed for senior working professionals, requiring a minimum of 8 years of work experience

Applications of AI

Great Learning

Learners in the age bracket of 13-18 or Class 8-12

 

No coding experience required

 

Interest in learning and exploring new

ideas and concepts

PG Diploma in Artificial Intelligence

Indraprastha Institute of Information Technology,

Delhi (IIIT Delhi)

Graduation or Post-Graduation with at least 50% marks or equivalent CGPA

 

Experience in at least one programming language

No Code AI and Machine Learning

Massachusetts Institute of Technology (MIT) Professional Education

Fundamentals of mathematics and statistics (If you do not possess either (or both) of them, you are expected to put in extra effort to learn the concepts before the commencement of the program. Great Learning will provide you with pre-work sessions by seasoned instructors that can be useful in understanding the fundamentals of mathematics and statistics.)

 

Business leaders who want to learn how AI and ML solutions can be built

 

Operations and Product Managers interested in quickly getting a solution off the ground

 

Entrepreneurs, Consultants, and Solution-builders who want the ability to quickly build working prototypes or early solutions without needing large data teams

 

Working professionals from non-technical backgrounds aspiring to lead AI and data-driven teams and build innovation initiatives using AI technologies

MS in Information Science and Machine Learning

The University of Arizona

A 4 year U.S. bachelor’s degree or equivalent

 

A bachelor’s degree along with a master’s degree, 1st year of Master’s or 1-year Diploma. If the 3-year bachelor’s is equivalent to 120 credits or the transcript of the same states it is equal to 4 years, then it is acceptable

 

For students with a 3-year bachelor’s degree:

> Students who didn’t complete their degree in the U.S.: Their transcript evaluation must state that their degree is equivalent to a 4 year U.S. bachelor’s degree

> Students who completed their degree in the U.S.: They must possess a 4-year bachelor’s degree

 

The medium of instruction for the candidate’s bachelor’s degree must be English. If not, they would need to give an English language proficiency test like IELTS/TOEFL

Master of Data Science - 24 Months

Deakin University

Deakin’s minimum English language requirement

 

Minimum 3-year bachelor’s degree in a related discipline

 

Minimum 3-year bachelor’s degree in any discipline with at least 2 years of professional experience

Master of Data Science - 12 Months

Deakin University

Deakin’s minimum English language requirement

 

Minimum 3-year bachelor’s degree in a related discipline

 

Minimum 3-year bachelor’s degree in any discipline with at least 2 years of professional experience

 

Candidates must have successfully completed either PGP-DSBA or PGP-AIML offered by UT Austin and Great Learning

PGP in Machine Learning

Great Lakes Executive Learning

A bachelor's degree in computer science, IT, statistics, STEM (science, technology, engineering, and mathematics), or any other related field

 

Prerequisites for Artificial Intelligence and Machine Learning
 

There are several prerequisites for AI and machine learning to fulfill to excel in these cutting-edge fields. These prerequisites ensure a smooth and practical learning experience, providing a solid foundation to build upon.

 

Some of the essential requirements for artificial intelligence courses are:

 

  1. Mathematics: A solid understanding of mathematical concepts, such as linear algebra, calculus, and probability theory, is essential. These areas form the basis of many machine learning algorithms and will help you grasp AI and ML techniques more effectively.
     
  2. Programming: Proficiency in at least one programming language, such as Python, Java, or R, is critical. Python is particularly popular in the AI and ML domains due to its simplicity and extensive libraries, such as TensorFlow, Keras, and sci-kit-learn.
     
  3. Data Structures and Algorithms: Familiarity with fundamental data structures (arrays, linked lists, trees, graphs) and algorithms (searching, sorting, optimization) is essential, as they are frequently employed in AI and ML solutions.
     
  4. Statistics: A strong foundation in statistics is crucial for understanding the underlying principles of data analysis and ML techniques. Statistical concepts, such as hypothesis testing, confidence intervals, and regression analysis, play a vital role in AI and ML.
     
  5. Domain Knowledge: While not always a prerequisite, having domain-specific knowledge can significantly enhance your ability to apply AI and ML techniques to real-world problems.
     
  6. Soft Skills: One must possess specific soft skills, such as problem-solving, critical thinking, creativity, and effective communication. These skills facilitate collaboration, enable efficient project management, and contribute to the overall success of AI and ML endeavors.

 

By fulfilling these prerequisites, aspiring AI and ML professionals can better navigate the complexities of the field, maximize their learning experience, and increase their chances of success in AI and ML projects.

 

Who Should Learn Artificial Intelligence and Machine Learning?
 

AI and ML have become increasingly relevant across various industries, making these skills valuable for a wide range of professionals. Individuals who should consider learning AI and ML include the following:

 

  • STEM Graduates: Students or professionals with a background in Science, Technology, Engineering, or Mathematics can benefit immensely from AI and ML skills, as these disciplines often overlap and share common principles.
     
  • Data Analysts & Scientists: As AI and ML are deeply rooted in data analysis, data analysts and scientists can enhance their capabilities and career prospects by gaining expertise in these areas.
     
  • Software Developers & Engineers: Professionals in software development and engineering can leverage AI and ML to create more innovative, efficient applications and systems, opening up new possibilities in their field.
     
  • IT Professionals: IT professionals, such as system administrators and network engineers, can incorporate AI and ML solutions to improve infrastructure management, security, and automation.
     
  • Business Professionals: Those in management, marketing, finance, or other business roles can benefit from understanding AI and ML concepts to make data-driven decisions, optimize processes, and identify new opportunities.
     
  • Researchers & Academics: AI and ML techniques are becoming increasingly relevant in various research fields, such as medicine, environmental science, and social sciences, enabling researchers to gain new insights and make breakthrough discoveries.
     
  • Career Changers: Individuals seeking to transition into the rapidly growing field of AI and ML can benefit from learning these skills as the demand for AI and ML professionals continues to soar across industries.
     
  • Curious Minds: Anyone with a genuine interest in understanding how AI and ML work, and a desire to explore the potential of these technologies, can benefit from learning the principles and techniques that drive these innovations.

 

Artificial Intelligence Qualifications
 

In order to excel in an artificial intelligence course and pursue a career in this cutting-edge field, there are several vital qualifications that are highly valued:

 

  • Academic Qualifications

A bachelor's or master's degree in a relevant discipline, such as computer science, mathematics, engineering, or a related STEM discipline, lays the foundation for understanding complex AI algorithms and techniques. These subjects provide essential knowledge in programming, mathematical concepts, and problem-solving, which are critical to success in AI.

 

  • Practical Experience

Hands-on experience with programming languages and AI-related tools is vital for excelling in AI courses. Proficiency in languages like Python, Java, or R, along with experience using AI frameworks and libraries such as TensorFlow, Keras, or PyTorch, helps develop practical skills. Working on real-world AI projects further strengthens your understanding and application of AI concepts.

 

  • Industry-Specific Knowledge

Domain-specific knowledge can be crucial depending on the area of AI you are interested in. For example, if you want to work with AI in healthcare, a background in medical sciences and an understanding of healthcare data can be beneficial. Acquiring domain-specific knowledge allows you to apply AI techniques more effectively and address unique challenges within your chosen field.