The AI Academy

We build a work-ready AI workforce that can be integrated with your existing teams.

Aligned to the Data Science & AI industry, our Academy adapts to meet the rapidly evolving tech landscape, connecting education to the working world. Our training pathways are created by our team of in-house experienced Data Science & AI leaders. Everyone has at least a decade’s experience in the field.

4 months



 Hours per week




 Case Studies

4-Month Training Program

Data Science & AI Aligned Training Pathway

17 Week Course Final Design Blue

Data Science Primer: Analytics Essentials & Testing

  • Analytics Landscape 
  • Descriptive Analytics – Scales of measurement, measures of central tendency
  • Probability & Conditional Probability
  • Sampling & Estimation
  • Hypothesis Testing – Parametric Tests, Non-Parametric Tests, ANOVA
  • A/B Testing Hands-On
  • Hypothesis Testing – Case Exercise
  • Correlations
  • Decision Trees
  • Mini Project (Using Python)

Data Prep: Quality Enhancement & Cleansing

  • Data Quality Checks 
  • Data Cleaning  
  • Data Imputation 
  • Hands-On Exercise 

Discovering Insights through Effective Data Storytelling

  • Data Storytelling 
  • Visual perception, Gestalt principles, Visual Encoding, Edward Tufte Design principles 
  • Hands-on: Explore and Build data visualizations in Tableau
  • Hands-on: Building Interactivity and Animations
  • Hand Boarding and Storyboarding
  • PowerBI, components and architecture
  • Powerbi Desktop – Exploring Tool, creating report and dashboard
  • Hands-on: Advance PowerBI Visualization 

Capstone Project (Using Python, Tableau, PowerBI)

ML Techniques for Descriptive & Predictive Analytics

Supervised Learning: 

  • Simple Linear Regression: Case Exercise 
  • Multiple Linear Regression: Case Exercise 
  • Logistic Regression: Case Exercise 

Capstone Project (Using Python) 

Unsupervised Learning: 

  • Cluster Analysis using Python 
  • Conjoint Analysis  
  • Factor Analysis 
  • Principle Component Analysis (PCA)
  • Regression: Simultaneous Equations 

Mini Project

Version Control & Cloud Services

  • Git, GitHub, GitHub Copilot 
  • Cloud Computing Fundamentals
  • AWS Cloud Services: Hands-On
  • Azure Cloud Services: Hands-On 

From Data Modeling to Engineering Decision Intelligence

  • Data Modeling & Analysis
  • Statistical Analysis for Engineering Data
  • Decision Intelligence Framework
  • Optimization & Simulation Techniques
  • Case Exercises & Real-world Applications 

Mini Project 

Temporal Analytics: Mastering Time Series Data

  • Introduction to Time Series Data 
  • Decomposition of Time Series Data 
  • Simple Forecasting Techniques – Smoothing Techniques 
  • Case Assignment + Group Exercise
  • ARMA & ARIMA Models: Case Exercise 

Mini Project 

Advanced ML Techniques at the core & edge of Business

  • Reinforcement Learning 
  • Markov Chains with Absorbing States: Case Exercise
  • Customer Lifetime Value (CLV): Case Exercise
  • Ensemble Methods Hands-On
  • Loss Function & Gradient Descent Optimization
  • Regularization: Lasso & Ridge Regression
  • Dealing with Data Imbalance
  • SVM, Random Forest Hands-On
  • Boosting & Stacking Hands-On
  • Advanced Feature Engineering and Feature Selection Techniques
  • Recommender Systems: Association Rules, Collaborative Filtering Matrix Factorization 

Capstone Project 

Big Data Analytics

  • Big Data Overview & Ecosystem 
  • Spark Architecture Overview 
  • Spark APIs 
  • SQL
  • Spark Advanced Analytical Functions
  • Spark Streaming
  • Spark & ML
  • Big Data & AWS Cloud 

Mini Project 

AI & Deep Learning

  • AI & Deep Learning Fundamentals 
  • Concept of Representational Learning 
  • Artificial Neural Networks: Multi-layer Perceptron; Back Propagation 
  • Hyper-Parameter Tuning (Learning Rate Scheduling, Batch Size, Epoch, Hidden Layers and neurons)
  • Training Deep Neural Networks - Faster Optimizers, Avoiding Overfitting - (Dropout, Batch Normalization)
  • Autoencoders - Transfer Learning, Unsupervised Pre-training, Anomaly Detection
  • Convolutional Neural Networks - Concept of Convolution and filters/kernels Grey-scale and color images
  • Image Classification using CNNs and concept of receptive fields
  • Explaining Convolution Layers, understanding CNN Architectures
  • Data augmentation strategies - overcoming over-fitting and handling data imbalance
  • Transfer Learning - Feature Extraction and Fine Tuning 

Capstone Project (Using Python, PyTorch) 

Generative AI

  • Introduction to Generative AI and LLMs (Foundation Models)
  • Training Large Language Models
  • In-context Learning/Prompt Engineering - Enhancing Model Outputs
  • Cost Optimization Strategies for LLM Training & Aligning to Human values
  • LangChain: Simplifying Development with Language Models
  • LLM Powered Applications
  • Generative Image Models
  • Use Cases of Generative AI
  • Responsible AI - Ethical Considerations in Generative AI 

Capstone Project 

Machine Learning Operations (MLOPs)

  • MLOps Overview 
  • Dealing with Data Labelling Challenges 
  • Building ML Pipelines and Model Versioning 
  • Model Experiments and Experiment Tracking 
  • Case Assignments
  • Model Persistence and AutoML
  • Model Serving using Containers
  • Model Monitoring
  • Azure ML Deployment 

Model Deployment Exercise 

Business Essentials & Soft Skills

  • Design Thinking Practitioner 
  • Product Success Leader 
  • UI/UX Expert 
  • Next-Gen AI Marketer
  • Reporting & Communication 

Upskilling Programs for Corporate Success

  • Intelligent Process Automation 
  • Digital Leadership 

Mindful Data Mastery: Navigating Stress and Insight in the Digital Age

  • Mindfulness & Benefits
  • Mindful Breathing & Grounding Techniques
  • Cultivating Present-Moment Awareness
  • Managing Workload & Prioritization
  • Mindful Communication & Collaboration
  • Stress Resilience & Self-Care
  • Sustaining Mindfulness in the long run


Tech Stack Foundation

Laying the Groundwork for Future AI Specialists

Website Tech Stack Section

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