Training on AI, Generative Intelligence, and Autonomous Systems
Build intelligent models and deploy them for real-world applications. Master AI, ML, generative AI, and autonomous systems using Python and modern AI frameworks
Next intake
20 Jul 2026 · Nakuru
Duration
10 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Intermediate
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
This comprehensive course provides a concise yet thorough understanding of Artificial Intelligence and Machine Learning, covering foundational concepts, mathematical and programming essentials, data preparation, and classical as well as advanced machine learning techniques. It introduces deep learning, computer vision, and natural language processing, along with emerging areas such as generative AI and agent-based systems. Through hands-on practice using Python and modern AI frameworks, learners build, evaluate, and deploy intelligent models to solve real-world problems.
Target Audience:
- IT and engineering professionals
- Data and business analytics professionals
- Recent STEM graduates and academics
- Professionals seeking hands-on ML and AI training
- Anyone interested in building and deploying AI models
What you'll walk away with
- To understand AI, ML, and deep learning basics
- To trace the evolution of AI from rule-based systems to generative AI
- To differentiate learning types: supervised, unsupervised, reinforcement
- To explore generative AI and agent-based systems
- To understand AI project workflow and deployment basics
- To set up Python, Jupyter Notebook, and Google Colab
- To handle data using Python (NumPy, Pandas)
- To apply data visualization and analytics processes
- To perform data collection, cleaning, and feature engineering
- To build and evaluate predictive models on real datasets
What we cover, module by module
Module 1: AI, ML, and Deep Learning Basics
- Understanding the foundational concepts of AI and ML
- Differentiating between AI, ML, and deep learning
- Exploring the key drivers of AI: data, algorithms, compute
- Understanding the potential and limitations of AI
- Assessing the impact of AI on various industries
- Case Study: Analyzing the application of AI in a specific industry
Module 2: Evolution of AI from Rule-Based Systems to Generative AI
- Tracing the evolution of AI technologies
- Understanding rule-based systems and expert systems
- Exploring the rise of machine learning and deep learning
- Understanding generative AI and its transformative potential
- Assessing the future directions of AI development
- Case Study: Analyzing the evolution of a specific AI technology
Module 3: Learning Types: Supervised, Unsupervised, Reinforcement
- Understanding supervised learning: regression and classification
- Exploring unsupervised learning: clustering and dimensionality reduction
- Understanding reinforcement learning: agents and environments
- Comparing and contrasting different learning types
- Selecting the appropriate learning type for a problem
- Case Study: Applying different learning types to a problem
Module 4: Generative AI and Agent-Based Systems
- Understanding generative AI and its applications
- Exploring agent-based systems and their architecture
- Building intelligent agents for specific tasks
- Integrating generative AI with agent-based systems
- Assessing the potential of generative and agentic AI
- Case Study: Building a generative agent for a business task
Module 5: AI Project Workflow and Deployment Basics
- Understanding the end-to-end AI project lifecycle
- Defining project objectives and success criteria
- Planning AI projects and managing stakeholders
- Understanding the basics of AI deployment
- Preparing for AI project management challenges
- Case Study: Planning an AI project from start to finish
Module 6: Python Setup, Jupyter Notebook, and Google Colab
- Setting up Python for AI and ML development
- Getting started with Jupyter Notebooks
- Exploring Google Colab for cloud-based development
- Writing and executing Python code for AI
- Best practices for Python development in AI
- Case Study: Setting up a Python environment for AI development
Module 7: Data Handling Using Python (NumPy, Pandas)
- Understanding data structures and data types
- Manipulating data with NumPy arrays
- Processing data with Pandas DataFrames
- Cleaning and preparing data for AI models
- Performing data transformations and aggregations
- Case Study: Manipulating data with NumPy and Pandas
Module 8: Data Visualization and Analytics Process
- Understanding the importance of data visualization
- Creating visualizations with Matplotlib and Seaborn
- Exploring data patterns and relationships visually
- Communicating insights through data visualization
- Applying visualization techniques to AI projects
- Case Study: Visualizing data for an AI project
Module 9: Data Collection, Cleaning, and Feature Engineering
- Understanding data sources and collection methods
- Cleaning data: handling missing values, outliers, and errors
- Engineering features for AI models
- Transforming and scaling data for AI
- Preparing data for model training
- Case Study: Preparing data for an AI model
Module 10: Building and Evaluating Predictive Models on Real Datasets
- Selecting appropriate models for a problem
- Training and evaluating predictive models
- Tuning model hyperparameters for performance
- Interpreting model results and drawing insights
- Deploying models for real-world applications
- Case Study: Building and evaluating a predictive model
Where the change lands
Organizational Impacts:
- Enhanced capability to build and deploy AI models
- Improved problem-solving through intelligent systems
- Better integration of AI into real-world applications
- Strengthened technical capacity in AI and ML
Individual Impacts:
- Understanding of AI, ML, and deep learning basics
- Skills in using Python for data handling and visualization
- Ability to build, evaluate, and deploy intelligent models
- Knowledge of generative AI and agent-based systems
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
- NakuruNext
20 Jul → 31 Jul·In-Person
Book this intake - Kigali
20 Jul → 31 Jul·In-Person
Book this intake - Accra
20 Jul → 31 Jul·In-Person
Book this intake - Kisumu
27 Jul → 07 Aug·In-Person
Book this intake - Johannesburg
27 Jul → 07 Aug·In-Person
Book this intake - Dakar
27 Jul → 07 Aug·In-Person
Book this intake
Common questions.
Still not sure? Send us a note and a facilitator will get back to you within a business day.
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