Participate in Kaggle.com and learn AI/ML on https://www.fast.ai/ (free).

Do excellent Analytics work by day for your company and spend your evenings pursuing your AI/ML dream. A resource like www.fast.ai has made it very easy for folks to learn AI/ML by learning to code first, making it easily accessible for those who may not want to start with theory.

Buy a book like Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems and read it every day and work on the exercises.

Spend your weekends learning from freeCodeCamp YouTube videos.

Learn Python and attempt to build simple ML models using Scikit-Learn.

Install a software like Dataiku and build ML models using their point-and-click interface so you can focus on learning about the various algorithms and approaches.

Read the articles that talk about the applications of ML and start familiarising yourself with the industry.

Read job descriptions of ML roles so you can imagine the kind of work you will be expected to do.

Reach out to ML professionals on LinkedIn/Twitter and connect with them and get on a quick call to ask them about their journey. Make notes of their conversation and with their permission post these notes online for the benefit of others just like yourself.

There is a lot you can do to get into ML without starting to write code. Learning to code will eventually follow but try to get quick wins by easing yourself into the ML industry.

The discussion shared above was part of many Q&A sessions Harsh Singhal conducted with Data teams at various companies and colleges.