AI engineering is the fastest-growing career in tech. This self-study roadmap takes you from Python basics to building and deploying ML models — personalized to your math and coding background.
Last updated: March 2026 · 6 Months plan
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AI engineers build, train, and deploy machine learning models that power everything from recommendation systems to autonomous vehicles. In 2026, the field has expanded beyond research to include AI application engineers who integrate LLMs and foundation models into products. AI engineer salaries range from $120,000 to $250,000+, reflecting intense demand. The role requires Python proficiency, understanding of ML algorithms, deep learning frameworks, and increasingly, experience with LLM APIs and prompt engineering.
Month 1: Python and math foundations — NumPy, pandas, linear algebra, statistics, and probability. Month 2: Data analysis and visualization — data cleaning, exploratory analysis with matplotlib and seaborn. Month 3: Classical ML — supervised learning (regression, classification, decision trees), unsupervised learning (clustering, PCA), model evaluation. Month 4: Deep learning — neural networks with PyTorch or TensorFlow, CNNs for vision, RNNs for sequences. Month 5: NLP and LLMs — transformers, fine-tuning, working with OpenAI/Anthropic APIs, RAG systems. Month 6: End-to-end project — build, evaluate, and deploy an ML application.
AI bootcamps cost $10,000-$20,000 and compress learning into 3 months. Self-study takes longer (6-9 months) but costs nothing. The content is identical — the same textbooks, courses, and papers are freely available. What self-study lacks is structure, which is exactly what Free Class AI provides: a personalized week-by-week roadmap that adapts to your math background, coding experience, and career goals, keeping you focused and on track.
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