Why is now the time to learn AI?
Now is the perfect time to dive into the world of AI skills. With new AI achievements hitting the news almost daily, the demand for professionals versed in artificial intelligence has never been higher. From mundane applications like process automation and copywriting, to exciting frontiers like robotics and space exploration, AI is reshaping how we live and work, constantly expanding our sense of what’s possible.
At Udacity, we understand the urgency of staying ahead in this dynamic landscape, where those equipped with AI proficiency hold the keys to unlocking tomorrow’s possibilities. And we understand the difficulty professionals might have trying to identify how exactly to access a career in AI. That’s why we’ve put together this guide on the skills you should learn right now if you want to work in AI.
What types of roles are there in the field of AI?
There’s a variety of roles that await individuals eager to make their mark in the field of artificial intelligence. You have data scientists, who harness the power of algorithms to extract insights from datasets, and machine learning engineers who design and deploy intelligent systems capable of learning and adapting on their own. AI researchers explore new frontiers in artificial intelligence through groundbreaking research and experimentation, while data engineers build the infrastructure necessary to support AI applications. But the reality is that AI techniques and technologies will find their way into a much larger range of jobs; everyone from engineers to ethicists, product managers to graphic designers, doctors to lawyers.
If you’re interested in learning the core skills needed to get hands-on with the development and deployment of AI applications, then read on.
What are the Skills needed to engineer AI solutions?
AI is a complex, cutting edge field. Breaking into practitioner-level roles working on AI solutions requires a skill set that spans software engineering, mathematics, and data science. You don’t necessarily need to be an expert in all the following domains, but top candidates for roles in AI engineering and development are likely to check most of the below boxes. Alongside each, we’ve included some relevant Udacity courses that will help you fill any skills gaps you need.
- Programming Languages: Building scalable and maintainable AI systems requires solid software engineering skills and familiarity with software engineering principles like modular design, testing, and debugging. Python is the most widely used language in AI due to its extensive libraries like TensorFlow and PyTorch.
- Our Introduction to Programming Nanodegree program will orient you to programming fundamentals and best practices, and the Machine Learning DevOps Engineer Nanodegree program has a great course on Clean Code Principles.
- Udacity has a wide range of Python offerings, from beginner to advanced. The AI Programming with Python or Intermediate Python Nanodegree programs are great options, as is the free Intro to Python Course.
- Version Control: Proficiency in version control systems like Git is essential for collaboration and managing codebase changes efficiently.
- Check out this Version Control with Git Course (free) or our Introduction to Version Control Course, which includes guided project where you’ll post your work to GitHub.
- Machine Learning (ML) and Deep Learning (DL): Understanding the fundamentals of ML and DL is crucial, specifically learning different algorithms, optimization techniques, and neural network architectures.
- Check out our full Collection of ML Courses here, or browse some of these specific offerings…
- AWS Machine Learning Foundations Course (free)
- Intro to Machine Learning Nanodegree program with PyTorch or TensorFlow
- Machine Learning Engineer Nanodegree program with AWS or Azure
- Dive into our more advanced Deep Learning Nanodegree Program, or start with the more intermediate-level Introduction to Deep Learning Course
- For a bonus, check out the Machine Learning Interview Prep course
- Statistics and Probability: Having a strong foundation in statistics and probability theory is crucial for understanding and evaluating ML models.
- There’s a variety of resources out there for diving into the fundamentals of Statistics and Probability; some of the courses on the Udacity platform that we recommend include the immersive Statistics for Data Analysis Nanodegree program, or our free Intro to Statistics and Statistics courses.
- As you get more advanced, you may be interested in another free course, Fundamentals of Probabilistic Graphical Models
- Data Handling and Preprocessing: Knowing how to clean, preprocess, and manipulate data is essential. Familiarize yourself with libraries like Pandas and NumPy for efficient data handling.
- Check out Udacity’s Introduction to Data Analysis with Pandas and NumPy and
- Advanced Data Wrangling courses, or dive head-first into the Data Engineering Nanodegree program with AWS or Azure.
- Computer Vision (CV): If you’re interested in computer vision, learn about image processing techniques, convolutional neural networks (CNNs), and frameworks like OpenCV.
- Udacity offers an Intro to Computer Vision course, and a more exhaustive Computer Vision Nanodegree program, plus our Computer Vision and Generative AI course.
- Natural Language Processing (NLP): For NLP, explore text processing techniques, word embeddings, and sequence models like LSTM and Transformer.
- Similar to CV, Udaicty has a few offerings catered specifically to this skill: the Natural Language Processing Nanodegree program or the shorter Introduction to Natural Language Processing Course
- Reinforcement Learning (RL): Understanding RL algorithms and frameworks like OpenAI Gym can be valuable, especially if you’re interested in applications like robotics and game AI.
- The Deep Reinforcement Learning Nanodegree program is an advanced program, you’ll want solid Python skills plus a basic understanding of deep learning frameworks and neural networks before you dive in.
- Domain Knowledge: Depending on the industry you’re interested in (e.g., healthcare, finance, autonomous vehicles), gaining domain-specific knowledge can give you a competitive edge when applying for such roles.
- While the possible domains to cover are nearly limitless, Udaicty has offerings in a handful of the most popular: Generative AI, AI for Trading, AI for Healthcare, AI Product Manager, AI for Business Leaders and Generative AI for Business Leaders, Self Driving Car Engineer, Robotics Software Engineer.
If that all feels overwhelming, then start more simply with some high-level introductory material, and work your way forward from there:
- AI Fundamentals
- Discovering Artificial Intelligence and Machine Learning
- Discovering Ethical AI
- Introduction to AI/ML Fluency
- Generative AI Fluency
Remind me, why should I learn all this?
Investing your time and resources into learning AI skills offers both concrete and intangible benefits. Not only will you position yourself at the forefront of one of the fastest-growing and highest-paying industries, but you’ll also gain the ability to contribute meaningfully to groundbreaking innovations that shape the future. With AI proficiency, doors to diverse career opportunities will open, allowing you to pursue roles with top-tier companies, work on cutting-edge projects, and command competitive salaries. Beyond financial gains, mastering AI skills will empower you to take on complex and rewarding challenges, and guide the evolution of one of the most important technologies of our time.
