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Frequently Asked Questions

Answer all of your questions

The AppliedAICourse attempts to teach students/course-participants some of the core ideas in machine learning, data-science and AI that would help the participants go from a real world business problem to a first cut, working and deployable AI solution to the problem. Our primary focus is to help participants build real world AI solutions using the skills they learn in this course.
We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 150+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:
  1. Undergrad (BS/BTech/BE) students in engineering and science.
  2. Grad(MS/MTech/ME/MCA) students in engineering and science.
  3. Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.
  4. ML Scientists and ML engineers.
Yes, we do give a course completion certificate. We also help participants solve real world AI problems using publicly available datasets. We encourage the participants to document their work (code, plots, models and outcomes) on github. This github profile could be used by our participants to showcase their work to their peers and potential employers. This is similar in idea to a portfolio for photographers, models, artists, designers etc. We believe that the portfolio of work built by the participants is more important than the certificate.
Given our background in the industry, we believe that students completing this course and building a strong portfolio consisting of multiple projects stand a good chance of getting an AI engineer job in the industry. But, we cannot guarantee that. We would surely refer you to various teams, hiring managers and recruiters in the industry who are looking to hire AI engineers.
This course will focus on practical knowledge more than mathematical or theoretical rigor. That doesn't mean that we would water down the content. We will try and balance the theory and practice while giving more preference to the practical and applied aspects of AI as the course name suggests. Through the course, we will work on 20+ case studies of real world AI problems and datasets to help students grasp the practical details of building AI solutions. For each idea/algorithm in AI, we would provide examples to provide the intuition and show how the idea to used in the real world.
We love the work done by Coursera, Udacity and Udemy in this field. We are standing on their shoulders to push the horizon a little further. Following are the key differences:
  1. AML is very applied in nature. We cover topics like posing a real world, open ended business problem into a ML problem and machine learning process while leveraging case based teaching. All of our content will be motivated through examples to make it accessible to all.
  2. Our courses are targeted at a significantly wider audience than other courses: every one from a Undergrad student to a ML Scientist can benefit from them.
  3. Our courses are hyper-personalized through ideas like Content maps(explained later) which help students learn at their own pace to reach varying degrees of expertise. This course is designed in such a way that an ML expert can revise the fundamentals using this course in just a day. This course is also designed such that someone who has no background in ML can spend 6-12 months learning all the details.
  4. In addition to giving a course completion certificate, we help our participants build a high quality portfolio to showcase their skills to potential employers.
  5. Unlike some of the online courses, we provide a 24 hour guarantee to respond to any questions you might have regarding the content in the course. We believe that helping our students as quickly as possible keeps them motivated.
  6. We have contextual comments where in any student can leave us a question/comment/rating at any instance of time in the videos. This enables us to understand the students’ concerns and modify the content accordingly.
  7. The course content is highly dynamic. It is constantly edited and modified based on feedback from our participants.
  8. For each concept, there will be varying levels of details provided so as to cater to various audience segments based on their desired outcomes.
  9. This is our first attempt at building a good applied ML course to cater the widest possible audience. We will learn from the feedback from our participants during the course of the next few months and modify our course extensively.
Given our background in the industry, we believe that students completing this course and building a strong portfolio consisting of multiple projects stand a good chance of getting an ML engineer job in the industry. But, we cannot guarantee that. We would surely refer you to various teams, hiring managers and recruiters in the industry who are looking to hire ML engineers.
We are building this course like a “workbook” and not like a “textbook”. It is hard and uncomfortable to share a workbook which has lots of content that the participant generates overlayed with the course content. If multiple people use the same account credentials to go through this course, it would result in very sub-optimal results for all. Additionally, all of the content in this course is copyrighted and piracy of this content is punishable by law.

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