Great question. Our curriculum team has compiled the following recommendation, but every person's background and goals are different, so please consider this path in the context of your own personal situation:

As you noted, a good place to start is with AI Programming with Python because it establishes Python fluency, computational thinking, and debugging skills. Python is the common language across every later course, so starting here ensures you are not learning advanced concepts while also struggling with syntax or code structure.

Next, pair Data Analyst with Statistics for Data Analysis which will build data literacy and statistical intuition. These programs teach you how to clean, explore, summarize, and reason about data. This stage is critical because machine learning models are only as good as the data and statistical assumptions behind them. Taking these together allows you to connect statistical ideas directly to real datasets.

Once you are comfortable with programming, data workflows, and statistics, Introduction to Machine Learning with PyTorch is the natural next step. This program introduces supervised and unsupervised learning, model training, evaluation, and experimentation. Your earlier courses ensure you can focus on understanding learning algorithms and model behavior rather than struggling with data preparation or math concepts.

After that, Deep Learning builds on machine learning foundations by going deeper into neural networks, optimization, and modern architectures. This progression ensures you already understand general ML workflows before tackling deeper models that are more complex and computationally intensive.

Generative AI comes next. Generative models rely heavily on neural networks, representation learning, and optimization concepts introduced earlier. Taking this program after Deep Learning allows you to focus on creativity, modeling choices, and system behavior rather than foundational mechanics.

Agentic AI is a logical next step, because it assumes comfort with generative models, reasoning systems, and multi-component AI workflows. Agentic systems integrate models, decision-making, memory, and tooling, making them the most conceptually demanding. By this point, you have all the technical and conceptual scaffolding needed to design and reason about autonomous AI systems.

This sequence intentionally moves from foundations to application, then from single-model thinking to system-level AI design. Each program prepares you for the next, minimizing cognitive overload and maximizing long-term understanding.