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AI+ Developer (AT-310) Exam Prep: Applied AI Skills
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AI+ Developer (AT-310) Exam Prep: Applied AI Skills

Exam-focused prep for AI CERTs AT-310 covering Python, ML, deep learning, NLP, GenAI, RAG, and deployment.
Created byAseem Mankotia
Last updated 11/2026
English

What you'll learn

  • Navigate the AI/ML/DL/GenAI/NLP landscape as a developer
  • Use numpy and pandas for AI data workflows
  • Clean, encode, and visualize datasets with matplotlib and seaborn
  • Apply core linear algebra, probability, and statistics to ML
  • Train and evaluate scikit-learn models and diagnose overfitting
  • Use confusion matrices, cross-validation, and hyperparameter tuning
  • Build a simple neural network and apply CNN transfer learning in PyTorch
  • Tokenize text and run transformer inference with Hugging Face
  • Call LLM APIs for structured generative and multimodal output
  • Build a minimal RAG pipeline and deploy a model via FastAPI and Docker

Course content

13 sections • 12 lectures • 3h 0m total length
  • Domain 1: Foundations of Modern AI for Developers15:48

Requirements

  • Basic Python syntax (variables, functions, loops)
  • Basic AI/ML concepts (what a model and training are)
  • Comfort using a terminal/command line
  • A local Python environment (3.9+) with pip or conda

Description

This course contains the use of artificial intelligence.

AI+ Developer Practitioner (AT-310) from AI CERTs is the entry-level, vendor-neutral credential for developers who build applied AI features — and this course is independent, exam-focused preparation covering all twelve official exam domains in the published blueprint order and weighting. Taught by Aseem Mankotia, the course walks through the full applied-AI-developer workflow: Python for AI, data handling and visualization, practical math and statistics, machine learning fundamentals, model evaluation, deep learning and computer vision, NLP and transformers, generative AI application development, retrieval-augmented generation, simple agents and API deployment, and responsible AI and monitoring — closing with an end-to-end capstone and a full 90-minute, 50-question exam simulation.

Every domain pairs concept explanations with runnable, local labs — no paid cloud account required. You will clean and visualize real datasets with pandas, matplotlib, and seaborn; train and evaluate classifiers with scikit-learn; build a small neural network and apply transfer learning in PyTorch; tokenize text and run inference with Hugging Face transformers; call LLM APIs for structured generative output; build a minimal RAG pipeline with a FAISS/Chroma-style vector store; and wrap a model in a FastAPI endpoint, containerized with Docker. Each chapter maps directly to an AT-310 domain with its official weight, highlights heavily-tested concepts, flags common exam traps, and ends with scenario-style practice questions.

This course assumes basic Python and basic AI/ML concepts, matching the AI CERTs recommended background, and is designed as companion preparation that builds the underlying knowledge — it does not replace the official AI CERTs Authorized Training Partner program required for the credential bundle itself. AT-310 is also the recommended first rung into the AI+ Engineer Practitioner (AT-330), so the skills here carry forward directly. Confirm current AI CERTs pricing and exam logistics on the official AI CERTs store before enrolling. If you are a developer, data enthusiast, or IT specialist who wants a structured, hands-on review of applied AI development ahead of your AT-310 attempt, this course gives you a clear, domain-by-domain path.

AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official AT-310 exam guide.

Who this course is for:

  • Software developers, data enthusiasts, NLP and computer-vision learners, IT specialists and system architects, and students or recent graduates who want to prove hands-on applied-AI development skills. Assumes basic Python and basic AI/ML concepts. This is the entry rung of the AI CERTs developer track and a recommended feeder to the AI+ Engineer (AT-330), complementing role-specific developer certs with a broad, vendor-neutral 'AI Developer' credential.