Beginners & Enthusiasts

AI Engineering
Cohort

Become an AI Engineer in just 12 weeks. Learn to build, train, and deploy AI models, work with LLMs, and ship intelligent applications using Python, APIs, and modern ML tools. You’ll gain hands-on experience with real ML workflows and graduate with a portfolio of production-ready AI projects — ready for the job market.

AI Engineering Professional

AI Engineers

5.0

Start your AI Engineering
Bootcamp Journey TODAY!

What you'll learn in the brochure

About the AI Engineering Program
The modules for the mastery
Jekacode's distinguishing project

Register for the next cohort

💳 Flexible Payment Options

In-person at the hub — pay in Full, Half, or 3 installments.

Choose Training Mode

Price

₦500,000

At the hub — pay in full, half, or 3 installments


Course Overview

Here’s What You’ll Master

Module 1: Python & AI Foundations
  • Understanding the AI/ML landscape and career paths
  • Python for AI: NumPy, Pandas, and data handling
  • Jupyter workflows and version control for ML projects
  • Introduction to APIs and cloud AI services
    • Des manuels papiers Lelivrescolaire.fr
    Module 2: Machine Learning Essentials
  • Supervised vs unsupervised learning
  • Regression and classification with scikit-learn
  • Feature engineering and train/test splits
  • Model evaluation: accuracy, precision, recall, F1
    • Une personne consulte un manuel numérique Lelivrescolaire.fr sur son ordinateur portable
    Module 3: Deep Learning & Neural Networks
  • Neural network fundamentals and TensorFlow/Keras
  • Building and training feedforward networks
  • Activation functions, loss functions, and optimizers
  • Preventing overfitting with regularization
    • image here
    Module 4: LLMs & Prompt Engineering
  • How large language models work (high level)
  • Prompt design, few-shot learning, and chaining
  • Using OpenAI and similar APIs in applications
  • RAG basics: grounding models with your data
    • Deux personnes discutent ensemble
    Module 5: AI Application Development
  • Building AI-powered web apps and scripts
  • Integrating models and APIs into products
  • Streaming responses and error handling
  • Authentication, rate limits, and cost control
    • image here
    Module 6: Data for ML & Model Evaluation
    • Preparing datasets for training pipelines
    • Cross-validation and hyperparameter tuning
    • Bias, variance, and interpreting model output
    • A/B testing and monitoring model performance
    Deux personnes discutent ensemble
    Module 7: MLOps & Deployment
  • Packaging models for production
  • Docker basics and cloud deployment options
  • CI/CD for ML projects
  • Logging, versioning, and rollback strategies
    • Des couverture de la collection Les Classiques
    Module 8: Computer Vision & NLP Intro
  • Image classification fundamentals
  • Pre-trained vision models and transfer learning
  • Text preprocessing and embeddings overview
  • When to use off-the-shelf vs custom models
    • Module 9: AI Ethics & Responsible AI
  • Bias, fairness, and transparency in AI systems
  • Privacy, consent, and data governance
  • Hallucinations and human-in-the-loop design
  • Nigerian and global regulatory context
    • Des couverture de la collection Les Classiques
    Module 10: Capstone AI Project
  • End-to-end AI product from idea to deployment
  • Portfolio presentation and documentation
  • Code review and peer feedback
  • Career prep: CV, GitHub, and technical interviews

    • E JEKA CODE

    After Completion, You Will Be Able To:

    Build and train machine learning models with Python
    Integrate LLMs and AI APIs into real applications
    Deploy models and AI features to production environments
    Present a capstone AI project in a job-ready portfolio
    Work on real-world datasets and case studies from multiple industries
    Build a GitHub portfolio and prepare for AI engineer interviews
    Access to experienced AI mentors and a supportive builder community
    Apply now!
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