Randomly watching tutorials is the fastest way to get nowhere in AI.
You might feel productive, but without a structured path, you are just collecting disconnected facts rather than building a skill set. I recently found a brilliant roadmap from this LinkedIn creator that outlines exactly how to go from absolute zero to world-class status. The author breaks down the journey into 28 distinct levels, removing the guesswork that paralyzes so many beginners.
📌 The Hierarchy of Competence
The core philosophy behind this roadmap is linear progression. Many enthusiasts try to jump straight into building Large Language Models (LLMs) without understanding the underlying mathematics or coding principles. The creator argues that this is a mistake. By defining specific milestones, the expert ensures that you build a pyramid of knowledge where every new skill is supported by the one before it. The roadmap begins with basic awareness and literacy, moves through hard coding skills, touches on the necessary mathematics, and only then graduates to machine learning and deep learning. It is a disciplined approach designed to turn “magic” into engineering.
💡 Phase 1: The Non-Negotiable Foundations
The first four stages (Levels 0-3) identified by the original poster are often the ones people try to skip, yet they are the most critical. The author starts with AI Awareness and Digital Literacy, ensuring you know what AI actually is before you touch a keyboard. From there, the expert emphasizes Mastering Python and control structures. Crucially, Level 3 is devoted to Math for AI Foundations. The creator notes that you cannot effectively work with high-level AI without grasping linear algebra, probability, and calculus. If you skip this, you might run code, but you won’t understand why it works or how to fix it when it breaks.
💡 Phase 2: From Data to Machine Learning
Once the bedrock is laid, the roadmap shifts to the practical application of data (Levels 4-6). The expert highlights that real-world AI is mostly about data handling, including cleaning, preprocessing, and visualization. According to the post’s author, you must master libraries like scikit-learn and understand workflows before training models. This section bridges the gap between raw coding and actual intelligence. You learn supervised and unsupervised learning, but more importantly, you learn model evaluation. The innovator behind this list stresses that building a model is easy, but validating that it actually works requires a specific set of skills involving cross-validation and feature engineering.
💡 Phase 3: Deep Learning and the Path to LLMs
Only after Level 6 does the creator introduce Deep Learning (Levels 7-10). This is where the curriculum gets exciting. You start with the foundations of neural networks and backpropagation. The author guides you through building CNNs and RNNs using frameworks like PyTorch or TensorFlow. Finally, at Level 10, you reach the topic everyone is talking about: Large Language Models. The key takeaway from this professional’s structure is that LLMs are Level 10 for a reason. To understand attention mechanisms, pretraining, and fine-tuning, you need the context provided by the previous nine levels of Natural Language Processing and Deep Learning.
🧠 The Challenge of Consistency
While this roadmap provides clarity, the volume of information can be overwhelming. The nuance here is that this is not a weekend crash course; it is a long-term educational commitment. The original poster implies that success depends on investing time every single day to move through these levels. Rushing through the math or data cleaning stages to get to the “cool stuff” will likely result in a fragile understanding of the technology.
The list above covers the first 10 steps, but the expert mentioned there are 28 levels in total to reach the pinnacle of the industry.
Check out the full post to see the infographic for the remaining levels 👇