AI Engineering Study Plan (Updated 2025)
Phase 1 (Weeks 1–8): Data Science Foundations
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■ **Primary Course:** *The Data Science Course: Complete Data Science Bootcamp 2025*
(Udemy, by 365 Careers)
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■ **Goal:** Gain practical foundations in data analysis, statistics, and Python for data science.
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■ **Topics:** Python for data analysis (NumPy, pandas, matplotlib), statistics, probability, and
basic ML models.
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■ **Recommended focus:** Skip basic Python syntax if already comfortable. Prioritize machine
learning and data visualization sections.
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■ **Supplementary Book:** *Data Science from Scratch* by Joel Grus — for deep
understanding of algorithms and math fundamentals.
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■■ **Pace:** 5–6 hours per week, alternating between Udemy lessons and book exercises.
Phase 2 (Weeks 9–12): Cloud and Certification
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■ **Course:** *AWS Certified AI Practitioner* (Stephane Maarek or Andrew Brown
recommended).
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■ **Goal:** Build foundational knowledge in cloud-based AI and ML solutions for professional
certification.
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■■ **Focus Areas:** AWS SageMaker, Bedrock, Rekognition, and AI service integration.
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■ **Outcome:** Earn your AWS AI Practitioner certification — useful for career and HR
validation.
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■ **Study tip:** Dedicate 2–3 hours on weekdays and 4 hours on weekends for 4–5 weeks.
Phase 3 (Weeks 13–20): Applied AI Engineering
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■ **Main Course:** *The AI Engineer Course 2025: Complete AI Engineer Bootcamp* (Udemy,
by 365 Careers).
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■ **Goal:** Learn to build real-world AI applications with NLP, Transformers, LangChain, and
Hugging Face APIs.
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■ **Tools Covered:** Scikit-learn, TensorFlow, LangChain, Hugging Face, OpenAI API.
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■ **Focus Areas:** Transformers, embeddings, chatbot projects, and deployment pipelines.
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■ **Supplementary Reading:** *AI and Machine Learning for Coders* (Laurence Moroney) —
review TensorFlow chapters alongside projects.
Phase 4 (Weeks 21–28): Deep Dive into Machine Learning
Frameworks
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■ **Book:** *Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow* by Aurélien
Géron.
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■■ **Goal:** Master ML pipelines, neural networks, model tuning, and production workflows.
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■ **Focus:** Work on advanced ML projects and integrate them with cloud-based deployment
(AWS or GCP).
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■ **Outcome:** End-to-end ML and AI engineering competence suitable for portfolio
presentation or job applications.
■ Final Outcomes
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By the end of this learning roadmap, you will have:
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- Strong data science and ML fundamentals.
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- A recognized AWS certification in AI fundamentals.
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- Practical project experience with LangChain, Transformers, and Hugging Face APIs.
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- Confidence to design, build, and deploy end-to-end AI applications.