Machine Learning Post Graduate Programs

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Program Advisors

  • Michael R. Roberts

    Michael R. Roberts

    Professor of Finance, The Wharton School

    Michael R. Roberts is the William H. Lawrence Professor of Finance at the Wharton School of the University of Pennsylvania. Professor Roberts earned his BA in Economics from the University of California at San Diego, and Ph.D. in Economics from the University of California at Berkeley.

  • Peter Cappelli

    Peter Cappelli

    Professor of Management, The Wharton School

    Peter Cappelli is the George W. Taylor Professor of Management at The Wharton School and Director of Wharton’s Center for Human Resources. He is also a Research Associate at the National Bureau of Economic Research in Cambridge, MA, served as Senior Advisor to the Kingdom of Bahrain.

  • Rick Hefner

    Rick Hefner

    Caltech CTME, Program Director

    Dr. Rick Hefner serves as the Program Director for Caltech’s CTME, where he develops customized training programs for technology-driven organizations. He has over 40 years of experience in systems development and has served in academic, industrial, and research positions. 

  • Venkata N Inukollu

    Venkata N Inukollu

    Assistant Professor, Purdue University

    Venkata N Inukollu earned his Ph.D. in Computer Science from Texas Tech University. He received his Master’s degree in Software Systems from BITS - Pilani, India. He has interests in software engineering and testing in AI and machine learning.



  • What are the highest paying Machine Learning Certificates?

    ML is a popular course, especially in developed nations. To set up a flourishing career in this domain, learners must possess machine learning certifications. It is a prerequisite to significant earnings and a position in a reputable firm. Whether a student or a skilled individual, the recognition enhances job prospects.

    Since this industry is expanding swiftly, it has given many employment prospects for dedicated students. If they wish to know about the highest-paying Machine Learning credentials according to earnings, check this table:


    IBM Recognized Expert - AI Industry Workflow V1

    IBM Enterprise



    Azure AI Engineer Assistant

    Microsoft Enterprise



    Proficient Machine Learning Expert

    Google Inc.



    AWS Recognized Machine Learning - Profession




    IBM Recognized Data Science Course - ML Expert V1

    IBM Enterprise



  • Which language should I learn first - AI or ML?

    Machine Learning is a component of Artificial Intelligence involving the designing and distribution of data from past statistics and references. It teaches computers by feeding information and statistical approaches to assist them in improving their job. AI for decision-making is the simulation of mortal intellect procedures through computers. Its apps comprise expert systems, NLP, speech knowledge, and Machine Learning.

    Learners must understand that Machine Learning comes under Artificial Intelligence. It means if they wish to excel in data science, begin with the best machine learning course. The online course will enhance your performance and accuracy of Machine learning techniques, design workflow, and create a portfolio to resolve business complexities and optimize production.

  • What are the best machine learning courses?

  • Is R or Python better for Machine Learning?

    If learners have no idea which machine learning certifications are ideal for employment growth, the following table will help clear the doubts.


    Basis of Difference



    Nature of PL


    General purpose

    Degree of Adaptability

    It is extremely rigid.

    It is quite adaptable.


    It is ideal for statistical research and data intelligence.

    It is ideal for various jobs such as web development, input manipulation, and ML.

    Ease of Understanding

    It is a little difficult to understand because of the complex syntax.

    It is simpler to comprehend because of the relatively simple syntax.

    Although both languages are open-source, Python is universally a more effective language. On the other hand, R’s scope is confined to statistical analysis. Since both languages are nearly identical, the selection relies on the preference and employment needs.

    However, it has been observed that Python performs adequately in input manipulation and monotonous jobs. It is a more suitable choice if willing to create a virtual device based on Machine Learning. But if the desire is to construct a machine for ad-hoc research, R is a better choice.

  • Which Coding language is highly relevant for Machine Learning?

    Machine Learning is a complicated but fascinating domain. Several data engineering and analytics experts have dedicated their jobs to comprehending it. Since plenty of programming expressions are accessible, it is tough to pick the most suitable one. But, we have made the decision easier by specifying the 5 best machine learning courses, high in demand, at the moment.


    It is a practical language adopted by statisticians and analysts to analyze and visualize inputs. It can consolidate data-heavy ML jobs and uphold other expressions.


    It is an object-oriented dialect perfect for performance-critical assignments and memory manipulation. Since it operates at a lower level, it can connect with computers in their aboriginal codes and provides a steeper knowledge arc.


    This language is among the machine learning courses with a complex syntax, ideal for constructing varied types of applications that can work on any medium.


    It is a superior-style language that grew into a general-purpose dialect within a few years. It is ideal for fronted jobs and stretches to the backend in the form of an API.


    It is another superior programming language whose reputation has spiked in the last few years. It has straightforward syntax and increased speed which makes it perfect for rapid prototyping and the favorite of Machine Learning practitioners and data analytics professionals.


  • How do AI and ML differ from each other?

    AI and ML courses are valuable for companies of all scopes and are employed in multiple ways to automate repeated processes. Although they have many similarities and are used in similar industries such as healthcare, manufacturing, retail, telecommunications, and financial services, they are not identical.

    If learners want to know how these languages vary from each other, check the following table:


    Basis of Difference

    Artificial Intelligence (AI)

    Machine Learning (ML)


    Its goal is to build a machine that can mimic human intelligence.

    It aims to teach a machine the art of performing a specific task and generating outcomes by determining the patterns.


    It has a broader scope.

    It has a narrower scope.


    It can work with structured, unstructured, and semi-structured data.

    It can only work with structured and semi-structured data.


    These systems rely on logic and decision trees to learn.

    These systems rely on statistical models to learn.

  • Is Coding necessary for Machine Learning?

    To pursue a profession in Artificial Intelligence and Machine Learning, students must learn the art of coding, as both languages execute via coding. If they know how to implement a code, it will give them a better grasp on the operation, monitoring, and optimization of algorithms.

    Out of R, C++, Java, Python, JavaScript, Prolog, and Lisp, students can learn any ML through the best machine learning course, depending on the industry they are working in. But, learn about the underlying concepts of Machine Learning before starting with coding.

  • How to get a Machine Learning Certification?

    To obtain machine learning certifications, they must appear for and clear the two hours examination. It includes around 50 to 60 MCQs, covering topics such as framing ML general issues, structuring solutions, and creating various models. This certification is valid for two years. When this duration is over, they must re-appear for the exam to maintain the certification.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.