You’re interested in AI, but how can you advance your career in it?

Given the inter-disciplinary nature of this field plus keeping up with the latest & greatest developments, one can get easily overwhelmed. So let's accept that this is going to be a journey & probably a long one. So one must start by focussing first on building strong foundations in quantitative disciplines - statistics, mathematics coupled with programming & data engineering. Doing AI is not about calling a library or frameworks blindly to train a model. Or force fitting a problem to use AI when it can be solved elegantly & efficiently through other disciplines. The goal should be to create a deeper understanding of algorithms or networks, their architecture, so that one knows the math behind, can explain & know when to use them

Advance your AI career by acquiring relevant skills through online courses, such as those on Coursera or edX, focusing on machine learning, deep learning, and data science. Gain hands-on experience through personal projects or internships. Stay updated on industry trends and research by following conferences like NeurIPS or ICML. Network with professionals through LinkedIn or AI meetups for mentorship and job opportunities. Pursue advanced degrees or certifications like AWS Certified Machine Learning or Google AI Residency for specialized knowledge.

Given the vastness of AI, learning its intricacies requires time and effort. Starting with the basics is crucial to lay a solid foundation for comprehending more complex AI concepts. Without a solid foundation, it can be challenging to grasp advanced topics in ML , deep learning, or other AI subfields. Understanding the basics of mathematics, stats, and algorithms is essential for developing effective AI solutions. These skills are fundamental for designing, implementing, and optimizing AI algorithms. As, AI is a rapidly evolving field, with new technologies and techniques emerging regularly. A strong understanding of the basics allows professionals to adapt to these changes and incorporate new advancements into their work.

Choose a variety of projects that demonstrate your skills and interests in AI. For the projects clearly define the problem statement, methodology, and tools you used for each project. Highlight your problem-solving process, the challenges you faced, and how you overcame them. It is important to document your projects thoroughly, including code, datasets, models, and any other relevant materials. Provide clear explanations of your approach and results. Present your projects in a visually appealing and organized manner. Use visualizations, graphs, and charts to illustrate your results and make them easy to understand. It is useful to share the impact result from the projects.

User groups are a good way to meet people. They have existed for a long time and a lot have disappeared with the advance of the Internet and then Covid. Some user groups and communities are fully virtual now. I'd recommend AIDA User Group as they are free, trustworthy, and full of great people.

1. Find a passion. 2. Start practice your passion. 3. Be yourself. 4. Work hard. 5. Never give up. 6. Ask for help if you are stuck.