Development

Full-Stack AI Engineer: Python, ML, Deep Learning & GenAI

Course Overview

  • Course Title: Full-Stack AI Engineer: Python, ML, Deep Learning & GenAI
  • Instructor: School of AI (AI Academy)
  • Target Audience:
    • Beginners with no prior AI/ML experience
    • Aspiring AI engineers, data scientists, and machine learning engineers
    • Developers transitioning into AI/ML and Generative AI
    • Professionals seeking end-to-end AI project deployment skills
  • Prerequisites:
    • No prior AI/ML experience required
    • Basic computer literacy
    • High school-level math/statistics (helpful but not mandatory)
    • Laptop/desktop (Windows/macOS/Linux) with 8GB+ RAM
    • Stable internet connection for cloud tools

Curriculum Highlights

  • Key Topics Covered:

    • Python for AI: Data types, control flow, functions, file handling
    • Data Science: NumPy, Pandas, Matplotlib, Seaborn, data cleaning, visualization
    • Machine Learning (ML):
      • Scikit-learn (regression, classification, ensemble methods)
      • Model evaluation, XGBoost, LightGBM, CatBoost
    • Deep Learning (DL):
      • TensorFlow & PyTorch (CNNs, RNNs, LSTMs, GRUs)
      • Neural network architectures, backpropagation, optimization
    • MLOps:
      • Git, DVC, Docker, MLflow, CI/CD pipelines
      • Cloud deployment (AWS, GCP, Azure)
      • FastAPI, Flask for model serving
    • Generative AI & LLMs:
      • OpenAI GPT, Claude, Gemini APIs
      • Prompt engineering, RAG pipelines, fine-tuning
      • LangChain, CrewAI, AI agent frameworks
  • Key Skills Learned:

    • Python programming for data science and AI
    • Data preprocessing, feature engineering, and statistical modeling
    • Building ML models (supervised/unsupervised learning)
    • Designing deep learning models for computer vision and NLP
    • MLOps workflows: Version control, deployment, scaling
    • Developing Generative AI applications with LLMs
    • End-to-end AI project deployment in production

Course Format

  • Duration:
    • 32 hours on-demand video
    • Self-paced (lifetime access)
  • Format:
    • Video lectures, hands-on coding exercises, real-world projects
    • Jupyter Notebooks (Google Colab/local setup)
  • Resources:
    • 2 downloadable resources (code templates, datasets)
    • 1 article (supplementary guide)
    • Certificate of completion
    • Mobile & TV access
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