Build AI/ML Expertise from Scratch
A beginner-friendly guide with the non-technical learners in mind.
Artificial Intelligence (AI) is no longer a concept of the future—it’s here, reshaping industries, enhancing everyday experiences, and becoming a fundamental part of our lives. Whether it’s a customer service chatbot resolving your queries, a virtual assistant managing your schedule, or a recommendation system suggesting your next favorite show, AI is everywhere, often unnoticed but undeniably impactful.
This rapid integration of AI into daily life has sparked a growing demand for AI-related skills, especially in the tech industry. In May 2024, 12% of all tech job postings required AI skills—the highest percentage in six years (source). From my own job search, it feels like nearly 90% of postings now require some familiarity with AI, Machine Learning (ML), or data-related skills.
At the same time, there is a concerning trend of companies prioritizing hiring people with existing AI expertise rather than investing in upskilling their current workforce (source). For example:
In 2023, Dropbox laid off 500 employees, citing the need to hire talent with AI expertise.
In 2024, Intuit announced plans to lay off 1,800 employees, refocusing on hiring AI talent and reducing its executive workforce by 10%.
This shift in the workforce landscape underscores the growing importance of AI/ML knowledge for job security. But you don’t have to wait for your company to upskill you—you can take control of your own learning, just as I did!
Demystifying AI and ML
What is AI and ML?
Artificial Intelligence (AI), per ChatGPT, is the simulation of human intelligence by machines, enabling them to perform tasks such as learning, reasoning, problem-solving, and decision-making. Researchers use data and refine algorithms over time to improve AI. Machine Learning (ML) is a subset of AI. Machine Learning, according to the Microsoft (MS) Fundamentals of ML course, is the use of data from past observations to predict unknown outcomes or values; it is the intersection of data science and software engineering. Deep Learning is the newest hot topic, a term growing in usage with the popularity of generative AI, is sometimes used interchangeably with artificial neural networks and is an advanced form of ML that tries to emulate the way the human brain learns (also from the Microsoft course).
Common Misconceptions
AI is only for coders: Many courses cater to non-technical learners and business professionals.
AI is too complex to learn: Foundational courses simplify concepts into engaging, consumable formats.
Building a Foundation: Starting from the beginning and learning the fundamentals
If you’re new to AI/ML, like I was, and have little to no understanding of the concepts, don’t worry. Starting with foundational courses is the best way to build a strong knowledge base. I researched and tried a variety of course and the following are my recommendations, ranked by my experience:
-
Cost: ~$50
Instructor: Andrew Ng, a renowned AI expert.
Duration: 6 hours (guided to be spread over 4 weeks but you can finish as quick as you like).
What You Learn:
Key AI terminology and concepts.
Practical applications for AI in your organization
How to navigate ethical and societal considerations in AI.
My Experience: It was created for the non-technical I LOVED this course. Andrew Ng was engaging and sprinkled a ton of fun anecdotes from his years in AI throughout. The course was video-based but has AI highlighting where you are in a transcript. This was perfect for me as I learn to keep my late-diagnosed ADHD mind engaged with content.
Badge/Certification: Upon completion you receive a sharable badge that can be added to your resume or LinkedIn profile.
-
1. Cost: Free
Instructor: N/A
Duration: 3 hours
What You Learn:
Key AI terminology and concepts.
MS Azure services overview for creating AI solutions.
My Experience:
This course primarily consists of written text, with short videos and animated graphics sprinkled in. It delves into the mathematical foundations of the concepts, which may feel challenging or disengaging for some learners. Despite this, it was my second favorite course. MS did an excellent job distinguishing core AI concepts from their platform-specific tools and have made their tools accessible for less technical users by incorporating no-code and low-code solutions to apply ML and AI concepts effectively.Badge/Certification: Upon completion you receive a sharable badge that can be added to your resume or LinkedIn profile. This course is in the learning path for AI-900, MS Certified: Azure AI Fundamentals.
-
Cost: $150 (varies by University)
Instructor: Diego Alvarado, Instructional Assistant Professor
Duration: 3 hours
What You Learn:
Key AI terminology and concepts.
Practical applications of ML models.
Creating your own ML models. MS Azure services overview for creating AI solutions.
My Experience:
I completed the 4-hour survey course, which is hosted on Canvas with a non-guided transcript available. It brought back memories of college lectures, with the professor’s droning style making it difficult to stay engaged. While I learned a lot, it took me several days to fully absorb the content. The quizzes were challenging—I even struggled with help from ChatGPT! Despite this, I appreciate the University of Florida’s concept of micro-credentials and their focus on in-demand skills.Badge/Certification: Upon completion you receive a sharable badge that can be added to your resume or LinkedIn profile.
-
Cost: Free
Instructor: N/A
Duration: 30 minutes
What You Learn: Key AI terminology and concepts.
My Experience:
Amazon excels at delivering learning content in a consumable and engaging way. From my experience as an almost-former Amazonian, their training materials have always impressed me, and their AWS courses raise the bar even further for external audiences. This course felt like a sales pitch. This approach might work well if your company uses AWS services or if you're pursuing AWS certification—but that isn't my goal.Badge/Certification: No but there is a learning path that ends with a certification that is geared towards engineers.
-
Cost: Free
Instructor: N/A
Duration: 20 minutes
What You Learn: Key AI terminology and concepts.
My Experience:
Nothing stood out about the Google course, it was quick and to the point. Similar to the AWS course in content, but without the sales pitch. It is a good, vendor-neutral approach for a high-level overview of the concepts. They also offer a 12 module, 15-hr, free ML Crash Course, that I did not take, which takes you through deep-dives of the foundational concepts of AI and ML like Linear Regression, Classification, and Large Language Models.Badge/Certification: No.
You have a foundation, now what?
After you have completed the foundation or fundamental courses, what should you do next? That’s up to you!
Advanced Classes
There are multitudes! Each of the hosts I tried offered more courses on a variety of topics and even based upon your role. I’m excited to take the 4-hr Introduction to AI Ethics from University of Florida and continuing MS’s Azure AI Fundamentals learning path. I also plan on checking out more options from Coursera that are geared towards Product Managers. I might also try Amazon’s: 30-min Planning a ML Project and the 90–min ML Essentials for Business and Technical Decision Makers.
Hands on Projects
Try putting what you’ve learned into practice! MS offers a free starter pack and labs to try out their tools or there is paid lab course available at Amazon AWS and Coursera. There are also free platforms available like Kaggle, who offer datasets and community support.
Stay Up to Date
I’ve joined a few forums and communities and have found a couple of podcasts that I like, to stay in the know. Find what works for you, see if there are communities on the platforms where you already engage. Here are just a few options:
Reddit: /machinelearning, Discord, or GitHub
Community.deeplearning.ai
Podcasts: The TED AI Show or The AI Daily Brief
Incorporating AI in Everyday Life
As I wrote this blog, I leaned on AI tools to help organize my thoughts, refine ideas, and ensure clarity. Tools like ChatGPT not only made the writing process more efficient but also helped me explore topics more deeply by suggesting examples and highlighting areas I might have overlooked.
This experience highlights an important point: even if you're not pursuing AI for career progression, its applications in everyday life are invaluable. From simplifying tasks like writing, planning, or research to automating mundane processes, AI can enhance efficiency and creativity. Whether you’re brainstorming for a project, managing your schedule, or even crafting emails, AI tools can act as a powerful assistant.
By exploring AI, you’re not just gaining knowledge for work but also unlocking tools that can make daily life easier and more productive.
Final Thoughts
Artificial Intelligence is an exciting and rapidly evolving field, offering endless opportunities to learn and grow. Whether you’re looking to advance your career or satisfy a personal curiosity, the path to AI/ML expertise is accessible and flexible. Reflecting on my own journey, I’ve found that the key to success lies in choosing resources that align with your goals and preferred learning style.
There’s no single "right" way to learn AI—your journey may look completely different from mine. You might dive into other advanced certifications, experiment with hands-on projects, or stay informed through different podcasts and online communities. The important thing is to start. Take small steps, build a foundation, and keep exploring.
I hope this guide has given you a starting point to navigate the vast landscape of AI/ML learning. The future of AI is bright, and it’s never too late to begin your journey.
