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Artificial intelligence (AI) is a cutting-edge field with a massive demand for highly skilled professionals. However, pursuing a top-notch artificial intelligence course requires fulfilling specific eligibility criteria. Are you familiar with these requirements? Don't worry! This comprehensive page will provide all the details regarding the eligibility criteria for artificial intelligence courses. Embark on this journey with us and open the gateway to a thriving career in artificial intelligence!
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Check out the detailed eligibility requirements for the different AI courses offered by Great Learning.
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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
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 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
Candidates must have successfully completed either PGP-DSBA or PGP-AIML offered by UT Austin and Great Learning
PGP in 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:
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.
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:
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.
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