By Kian Katanforoosh
Want to become an AI practitioner? Here’s a program that will take you from beginner to job-ready. You may already have a head start, depending on your background. For a motivated person who starts with a solid high-school education, it may take around two years. There are just three steps: Learn coding basics, study machine learning, and focus on a role.
Learn coding basics. Fundamental programming skill is a prerequisite for building machine learning systems. You’ll need to be able to implement a simple computer program (function calls, for loops, conditional statements, basic mathematical operations) before you can start implementing simple machine learning algorithms. Almost any basic introductory programming class can get you there.
Don’t worry about prerequisites such as linear algebra, probability, and statistics. While knowing more math is better than knowing less math, it’s often most efficient to start in on machine learning and work backward as necessary.
You’ll deepen your knowledge of these important subjects as you learn (and sometimes, yes, struggle) to get machine learning algorithms to work.
Study machine learning. Stanford University’s Machine Learning course on Coursera remains the most popular introduction to the field. In this course, you can expect to learn:
- Machine learning models. These include important algorithms such as k-means, linear regression, logistic regression, neural networks, and recommender systems.
- Model implementation and training. This area encompasses a variety of methods to initialize, optimize, vectorize, regularize, and select machine learning models.
- Practical machine learning. This includes strategies for building high-accuracy systems such as how to split data into training and test sets, understanding bias and variance, carrying out error analysis, and systematic ways to improve a model’s performance.
For many learners, the next step is to dig deeper into the most exciting development in AI of the last decade, namely neural networks. The Deep Learning Specialization will give you the knowledge you need to build applications in areas like computer vision, natural language processing, and speech recognition. From there, you can deepen your knowledge in these and other areas through projects or additional coursework.
Focus on a role. Once you’ve learned the foundations of machine learning and deep learning, your next move depends on the role you have in mind:
- Data scientist. If you’re interested in becoming a data scientist, focus on business analytics to tie your analyses to business outcomes, data visualization to present your findings, applied statistics to analyze data, and databases and pipelines to access and prepare data.
- Machine learning engineer. If you aim to become a machine learning engineer, study data mining to develop your ability to build high-quality models and MLOps to deploy them in production. If you wish to extend your skills even further, develop your software engineering skills to help you implement scalable systems, train and/or deploy them in the cloud or at the edge, and ensure a reasonable level of security.
- AI +X. Are you already accomplished in a field like medicine, biology, or physics? Rather than abandoning your current career to become a data scientist or a machine learning engineer, consider developing AI skills to complement your existing expertise. This approach is a great way to apply AI to real-world problems. For instance, if your current job involves working with images, you might extend it by learning how to process them using AI.
A skills assessment can give you important information for charting this next step. Workera provides individualized assessments in foundational skills like computer science and mathematics; tools like Python; application areas like computer vision and natural language processing; and more.
Whatever path you choose, everyone who intends to pursue a career in AI should be familiar with the concept of responsible AI. AI is a powerful technology that’s still evolving. Its eventual impact isn’t always clear at the outset, so it’s critical to reach an understanding of how AI systems can be developed and deployed in ways that bring maximum benefit and minimal harm.
The path to mastery in AI isn't straight and narrow, but it is exciting and fulfilling, and there is truly room for everyone.
Kian Katanforoosh is the CEO and co-founder of Workera, an affiliate of DeepLearning.AI that provides skills assessments and personalized learning plans to close the AI skills gap in large organizations.