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Machine Learning AI Startup Case Studies with Sramana Mitra
Highest Rated
Rating: 4.7 out of 5(102 ratings)
1,948 students
Created bySramana Mitra
Last updated 11/2024
English

What you'll learn

  • How a Machine Learning startup is built.
  • How an Artificial Intelligence startup has been built.
  • How a ML startup is built.
  • How an AI startup has been built.

Course content

3 sections13 lectures5h 28m total length
  • Introduction1:43
  • How to Build Machine Learning AI Startups9:30
  • Rev CEO Jonathan Spier on Applying AI to Lead Generation41:28

    I did a startup in 1998 by applying AI to the lead generation and qualification problem. It was early. The data was not yet rich enough. Now, the data is there. Can the problem finally be solved at the right level of sophistication?

  • Qure.ai CEO Prashant Warier on Building a Global AI Venture for Medical Imaging50:27

    Qure.ai CEO Prashant Warier has built a wonderful Healthcare AI company with over 1,600 customers worldwide. This is extremely sophisticated navigation.

  • Bootstrapped First to Build an AI Startup with Yellow.ai CEO Raghu Ravinutala37:28

    Yellow.ai CEO Raghu Ravinutala has built an incredible, world class AI startup from India with a global base of enterprise clients. Fabulous story!

  • From PhD Student to ML Entrepreneur: SuperAnnotate CEO Tigran Petrosyan35:13

    SuperAnnotate CEO Tigran Petrosyan and his brother were PhD students when they decided to quit their PhD program and build a company out of their PhD research technology. They have since raised over $15M in funding and built a customer base of ~200 in their ML Ops business. They are leveraging countries like Armenia and Bangladesh for development and data services.

  • Observe.AI CEO Swapnil Jain on Building a Venture Scale AI Company47:03

    Observe.AI CEO Swapnil Jain makes a very clear distinction between his goal of becoming a Centaur ($100M+ revenue company), not a Unicorn ($1B+ valuation).

  • Osprey Security CEO Rohit Anabheri Bootstrapped an AI Security Startup to $10M+37:15

    Osprey Security CEO Rohit Anabheri has effectively used the Bootstrapping Using Services technique to bootstrap an AI-Powered Enterprise Security venture to mid eight figure revenues. In that process, he has turned down offers for Venture Capital. I believe, in Artificial Intelligence and Machine Learning, currently, Bootstrapping Using Services is one of the best ways to find problems to solve and build successful companies.

  • Probability of Fund Raising2:55

Requirements

  • No prerequisite required.

Description

The 1Mby1M Methodology is based on case studies. In each course, Sramana Mitra shares the tribal knowledge of tech entrepreneurs by giving students the rare seat at the table with the entrepreneurs, investors and thought leaders who provide the most instructive perspectives on how to build a thriving business. Through these conversations, students gain access to case studies exploring the alleys of entrepreneurship. Sramana’s synthesis of key learnings and incisive analysis add great depth to each discussion.


There are over six million students enrolled in Machine Learning courses on Udemy. The most daring will try to start their own businesses.

This course shares a list of Udemy courses based on the 1Mby1M methodology that will assist budding entrepreneurs in creating a pragmatic strategy.

I believe, strongly, that entrepreneurship and entrepreneurial capitalism can be democratized, and wealth can be created in the middle of the pyramid using capitalistic principles. In the next 2-3 decades, the potential for distributed capitalism is very high and the outcome should be extremely positive around the world. That is the mission upon which my current work with One Million by One Million is based.

Artificial Intelligence, Big Data and Machine Learning are going to be at the forefront of this immense burst of energy.

Let’s talk about the field of medicine. If you think about what a doctor needs to do to diagnose an illness, she needs to consider all the symptoms, take into account all the test results, consider all the treatment options, factor in all the side-effects of various medications and their interplay with other medications the patient is already taking.

This is, effectively, a multivariate optimization problem that a doctor has to do in her head. And, she needs to keep up with all the new research and advances in medical science, and factor those in as well. The field of medicine is full of incorrect diagnosis and mistreatment of illnesses. Now, if you replace this whole process with software, which IBM is trying to do with their Watson supercomputer, medical diagnosis becomes a truly scientific, deterministic process.

I can tell you, if I have the option of being diagnosed by software versus a human doctor, I would always prefer software. It would be far more accurate.

Let’s get started.

Who this course is for:

  • Tech entrepreneurs building or aspiring to build machine learning / artificial intelligence startups.