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AI Engineering - Professional

The course equips learners with  AI concepts, algorithm fundamentals, data workflows, and basic model development to begin creating AI-powered applications.
  • Length
2 Months
  • Capstone
1 Months
  • Certificate
Yes
  • Level
Professional
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About the course

AI Engineering-Professional is a comprehensive, hands-on program designed to equip learners with the practical skills needed to build, deploy, and manage intelligent AI solutions in real-world environments. The course begins with Python programming foundations and progresses into machine learning, deep learning, natural language processing, and generative AI concepts. Learners gain experience working with industry-standard tools and frameworks, building AI-powered applications, automating workflows, and understanding how modern AI systems are developed and integrated into businesses. The program culminates in a capstone project where students design and deploy an AI solution to solve a real business challenge—preparing them for practical AI engineering and automation roles.

Course Curriculum

MODULE 1 — Python Programming for AI (Foundations to Intermediate)
  • Introduction to Python for AI
  • Jupyter Notebook, VS Code Setup
  • Python data types, variables, operators
  • Control flow (if, for, while)
  • Functions and modules
  • Data structures (lists, tuples, dictionaries, sets)
  • File handling
  • Virtual environments
  • Basic OOP concepts
  • Practical Python exercises
    • Linear algebra for AI
      • Vectors, matrices, matrix operations
    • Calculus essentials
      • Derivatives & gradients
    • Probability for AI
      • Random variables, distributions
    • Optimization concepts
      • Gradient descent, learning rates
    • Applied mathematics mini-projects

(This is not overly academic — it is practical, like in industry-focused tracks.)

  • Real-world data preprocessing mini-project NumPy for matrix operations
  • Pandas for data manipulation
  • Feature engineering for AI
  • Handling missing/imbalanced data
  • Data splitting and pipelines
  • Building preprocessing scripts for ML/AI models
  •  
    • Supervised learning
      • Regression, classification
    • Unsupervised learning
      • Clustering, dimensionality reduction
    • Train-test split, cross-validation
    • Evaluation metrics
    • Model tuning basics
    • ML workflow for AI projects
    • Mini-project: ML baseline system

     

  • What is deep learning?
  • Neural networks (perceptron, activation functions)
  • Forward & backward propagation
  • Loss functions
  • TensorFlow/Keras fundamentals
  • Building your first neural network
  • Hands-on mini DL project
  •  

Computer Vision:

  • Image fundamentals: pixels, channels
  • CNNs (conceptual introduction)
  • Image classification basics
  • Pretrained models overview

NLP:

  • Text preprocessing
  • Tokenization
  • Word embeddings
  • Sentiment analysis basics

Simple AI mini-projects:

  • Image classifier OR text sentiment classifier

  • Problem identification
  • Data sourcing & modelling
  • Model training & evaluation
  • Presentation of AI solution
  • Deployment overview
  •  

Course Structure

  • Saturday Live Classes for 2 months
  • 1-Month Capstone
  • CV Review
  • LinkedIn Optimization
  • Interview Preparation
  • Post-Training Support
  • Mentorship Sessions
  • Career Pathway Guidance

Emmanuel Chisom Ani

AI Engineering facilitator

Emmanuel Ani is an AI Engineer with a strong passion for building intelligent systems that solve real-world problems. He specializes in leveraging data, machine learning, and modern technologies to develop scalable and efficient solutions.

With a growing expertise in artificial intelligence and data-driven systems, Emmanuel is committed to continuous learning and innovation. He is focused on using AI to drive impact, improve decision-making, and contribute to the advancement of technology across industries.