7 Steps to Forging a New Career in AI

Imagine yourself as a trailblazer of the AI revolution. You crack open a terminal window, fingers poised over the keys like a virtuoso seated at a piano. Lines of elegant code pour forth like musical notes, giving rise to callback responses from an AI (artificial intelligence), designed to seamlessly augment your human capability.

You are not being replaced by Automation or A.I. but are in fact using the technology to bring your own potential and capacity to new heights. ”All in a day’s work,” you think to yourself, smiling. You feel the excitement and rush of adrenaline for being able to command an astronomical amount of data and detect deep patterns.

This is a vision of the not-so-far future, that entices many technologists, who are excited by the prospect of mastering this era-defining technology. Yet, transforming that desire into reality demands resilience, intuition, and dedication.

Today we’ll chart the journey required to reach those heights of A.I. glory.

Along the way, you will travel through worlds upon worlds sporting a battle-worn passport that contains hard-won “stamps” to exotic ports like Random Forest, Transformers, and Perceptrons.

#1 Start with the fundamentals

The trail to AI mastery begins with computer science fundamentals, a solid grasp of statistics/probability, and proficiency in a coding language like Python.

• The first steps are the hardest, but will be behind you before you know it
• Don’t skip past the basics in a rush to implement flashy algorithms.
• The basics you learn at the beginning are what you will build on continuously throughout your career, so make them stick
• Practice, Practice, Practice. The biggest mistake that people make in starting out with AI or Machine Learning or Programming concepts is to “stay in the theoretical.” Build projects, write code, and suffer through the pesky “bugs”. It is this painful yet enduring process that makes the whole journey worthwhile and rewarding!

#2 Absorb introductory materials across the whole field

Online courses and tutorials can offer an accessible starting point to survey the landscape. However, most of the time, online (FREE) content allows you to just tap into the tip of the iceberg and provides more of an introduction than real mastery. Here are some other things common among beginners in the A.I. field:

• Resist the urge to cherry-pick only areas that interest you initially, unless you have a decent grasp of the fundamentals already.
• Establish well-rounded roots by applying those theoretical “bits” to practical (hands-on) coding.
• Remember that a simpler (or more basic) solution is often better than a more convoluted solution. Don’t underestimate basics – seemingly simple models will remain potent weapons in your AI arsenal. Fundamentals > Hype
• Build mini-projects across some of the core basic ML techniques like regression, clustering, and classification.

#3 Learn from the Masters

Industry pioneers who achieved AI mastery often advise structured learning under expert guidance. Wisdom is built by experience and only those who have walked the path. Wisdom is what allows a veteran to point out and isolate a flaw on the order of seconds, which might take days for a new beginner to catch.

• A mentor is someone who you want on your side! Find empathetic mentors who can actually support you on the road to success through wisdom rather than just spitting out knowledge, links, or information.
• Let their hard-won experience guide you around pitfalls they once fell into.
• Ask them questions early and often. There are no stupid questions when you are starting out, but make sure to attempt it yourself before relying on others!
• Be a sponge and pay special attention to their mental frameworks and intuitions. Those can’t be learned from classes. They must be acquired through hard-fought experience.
• Apply their advice in your own learning and projects, and come back to thank them for providing specific insights, to build and maintain that connection (e.g. authentic networking).
• Offer to assist them or collaborate on projects, if they are open to it. Work alongside masters directly to absorb the greatest amount you can from them!

#4 Community propels you Further

“If you want to go fast, go alone. If you want to go far, go together” – African Proverb

Trailblazing is lonely. Reach out to fellow sojourners confronting similar challenges. Their diverse perspectives can re-energize you when progress stalls.

• Join forums, groups, and events to meet peers. Create a MasterMind of people who are passionate about the field, and you will have a never-ending source of information about the latest innovations and trends!
• If you are taking classes with other learners, try to form study groups to learn collaboratively and fill knowledge gaps.
• Attend webinars, conferences, and meetups to connect with the broader community. This is where you can find those future mentors, potential hiring managers, and soon-to-be colleagues!
• Embrace mentorship opportunities on both ends – teach others too as you walk down the path and learn. There is always someone who you can help provide value to!

#5 “Soft” Skills Open Doors

Beyond raw coding talent, soft skills like communication, empathy, and confidence make you truly invaluable. If you can’t explain concepts simply to someone, your mastery remains limited.

• Develop the vocabulary and metaphors to demystify AI for any audience, no matter their technical skill.
• Refine your written and verbal communication abilities by crafting a “voice” for your online and in-person communication. Are you into the “cutting-edge”, hard-hitting truth bombs, or cheesy Dad jokes? What makes your voice and communication style unique?
• Learn to visually communicate complex ideas simply and engagingly. Data Visuals will help, but the art of Data Storytelling will help even more!
• Practice explaining projects to non-experts. If they understand, you are on the right track. If you receive blank stares… back to the drawing board.
• The collaborative discussion will take you farther than solitary tinkering. Be willing to be wrong, be willing to be corrected, and be willing to receive help!

#6 Expand your Perspective

Don’t view AI (or any field) narrowly through the lens of your own industry or job role. Explore diverse real-world applications to expand your conceptual grasp.

• Look at other use cases in healthcare, finance, engineering, transportation etc. Especially in the A.I. field, this allows a deeper grasp of the different ways that an AI technology can be used. Keep an open mind!
• Stepping beyond your own bubble will help you spot new connections between analogous problems – track the synchronicities!
• Follow innovations in fields outside your own to get those creative juices flowing! Think outside your own box.
• Open yourself to collaborations and side projects that will pull you into new spaces heretofore unexplored. Bust out that compass and map to wander uncharted lands.
• Curiosity is an absolutely vital component of success on this path. Stay hungry, and keep learning.

#7 Build Breadth across Techniques

As your skills grow, avoid becoming a “one-trick pony.” Don’t use the same hammer to solve every single nail. It is essential to master a diverse toolkit when working with AI & ML.

• Learn various techniques from the 3 core branches of Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning.
• Study a wide variety of different model types from CNNs to RNNs, Random forests to XG-boosting. Learn the context where different model types can be useful, through practice!
• Identify and work through the potential tradeoffs between different approaches to a problem.
• Choose the right framework for each challenge and build a vast body of experience.
• Evaluating diverse options prevents blind spots.

The path to AI mastery is a lifelong journey of hands-on practice, learning from experts, and collaborating with a community of peers. Savor each step along the winding trail.

Fulfillment stems from overcoming hardships among fellow sojourners. In community, curiosity, and creativity, you find purpose.

TLDR;

• Starting with computer science fundamentals before advancing to complex algorithms
• Absorbing introductory materials broadly at first before specializing
• Learning from industry masters who can help you avoid pitfalls
• Embracing a community of fellow sojourners to propel each other farther
• Developing communication skills to explain complex topics simply
• Expanding perspective by exploring diverse real-world applications
• Building breadth across ML techniques instead of relying on singular approaches
• Architecting systems end-to-end rather than optimizing isolated components
• Maintaining an agile mindset as the field continues evolving rapidly

While self-guided learning can take you far, it can also be capped by how much can actually think to learn. A comprehensive and structured course is our recommendation to optimize the long road to mastery, through expert-level instruction in an interactive and hands-on setting. Experienced instructors can illuminate the trail ahead through hands-on projects and mentoring. Fellow peers who embark on the same journey as you will provide camaraderie and accountability.

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