04/04/2026
I WILL DELIVER PAID HANDS-ON TUTORIALS ONLINE FOR 3 HOURS EVERY SATURDAY TO COVER A FULL ARTIFICIAL INTELLIGENCE (AI) COURSE (SEE OUTLINE BELOW) IN 25 WEEKS (6 MONTHS). THIS WILL BE ON ROLLING BASIS SUCH THAT AFTER COMPLETING ALL THE MODULES, I WILL ROLL BACK TO MODULE 1. YOU CAN PLAN TO ATTEND ALL SESSIONS OR YOU CAN CHOOSE WHICH ONES TO ATTEND.
TUTORIAL 1 : Saturday, April 18, 2026
TIME: 08:00-11:00 AST (14:00-17:00 Malawi Time)
DELIVERY PLATFORM: Microsoft Teams
PROGRAMMING LANGAUAGES: Python & R
PARTICIPATION FEES: Only $20 per 3-hour session every week ($500 for full AI course in 25 weeks)
FOR MALAWIANS: You can either pay USD20 using the PayPal link provided or else pay MWK40,000 to my Malawi bank account below.
Bank Name: Standard Bank
Bank Branch: Ginnery Corner
Account Name: Owen Mtambo
Account Type: Current Account
Account Number: 9100006182507
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Full Course Outline: Artificial Intelligence (AI)
Module 1: Introduction to AI
Definition and history of AI
Applications and impact of AI
AI vs Machine Learning vs Deep Learning
Overview of AI
Ethical considerations in AI
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Module 2: Mathematics & Statistics for AI
Linear Algebra (vectors, matrices, eigenvalues)
Probability and Statistics
Calculus (derivatives, gradients)
Optimization techniques
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Module 3: Programming for AI
Python & R fundamentals
Data structures and algorithms basics
Key libraries:
o NumPy
o Pandas
o Matplotlib
o Seaborn
o Tidyverse
o Ggplot2
o Shiny
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Module 4: Machine Learning Fundamentals
Supervised learning
Unsupervised learning
Reinforcement learning basics
Model evaluation (accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, MAE, RMSE, R-square, BLEU Score, ROUGE, Relevance, IoU)
Overfitting and underfitting
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Module 5: Classical Machine Learning Algorithms
Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines
k-Nearest Neighbors
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Module 6: Data Preprocessing & Feature Engineering
Data cleaning & wrangling
Handling missing data
Feature selection and extraction
Data normalization and scaling
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Module 7: Deep Learning
Introduction to neural networks
Activation functions
Backpropagation
Frameworks:
o TensorFlow
o PyTorch
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Module 8: Computer Vision
Image processing basics
Convolutional Neural Networks (CNNs)
Object detection and recognition
Applications (face recognition, medical imaging)
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Module 9: Natural Language Processing (NLP)
Text preprocessing
Tokenization and embeddings
Language models
Applications (chatbots, translation, sentiment analysis)
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Module 10: Reinforcement Learning
Agents and environments
Reward systems
Q-learning
Policy gradients
Applications (games, robotics)
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Module 11: AI Systems & Deployment
Model deployment
APIs and cloud AI services
Edge AI
Monitoring and maintenance
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Module 12: AI Ethics & Governance
Bias and fairness
Privacy and security
Responsible AI
Regulations and policies
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Module 13: Advanced Topics in AI
Generative AI
Transformers and large language models (LLMs)
Explainable AI (XAI)
Agentic AI
AI in research and innovation
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Module 14: Capstone Project
End-to-end AI project:
o Problem definition
o Data collection
o Model building
o Evaluation
o Deployment
Report writing and presentation
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Module 15: AI Applications
AI for Healthcare & Life Sciences
AI for Finance & Banking
AI for Manufacturing & Robotics
AI for Big Data Analytics
AI for Education
AI for Legal Services
AI for Cybersecurity
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DETAILED ARTIFICIAL INTELLIGENCE CONTENT WITH TIMELINES
15 Modules · 75 Hours · 25 weeks · 6 Months
From Foundations to Frontiers
Prerequisites Recommended
• Basic Python & R programming
• High school mathematics (algebra and statistics)
• Curiosity and willingness to experiment
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Table of Contents
Module 1: Introduction to AI
Module 2: Mathematics & Statistics for AI
Module 3: Programming for AI
Module 4: Machine Learning Fundamentals
Module 5: Classical Machine Learning Algorithms
Module 6: Data Preprocessing & Feature Engineering
Module 7: Deep Learning
Module 8: Computer Vision
Module 9: Natural Language Processing (NLP)
Module 10: Reinforcement Learning
Module 11: AI Systems & Deployment
Module 12: AI Ethics & Governance
Module 13: Advanced Topics in AI
Module 14: Capstone Project
Module 15: AI Applications
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MODULE 1: INTRODUCTION TO AI
Duration: 3 Hours (APR 18, 2026)
Objectives
By the end of this module, students will define AI, trace its history, distinguish AI from ML and DL, and discuss ethical implications.
1.1 Definition and History of AI
Key Milestones in AI History
1.2 Applications and Impact of AI
Healthcare
Finance
Transportation
Education
Everyday Life
1.3 AI vs Machine Learning vs Deep Learning
1.4 Overview of AI Paradigms
Symbolic AI (Rule-Based)
Statistical / Connectionist AI
Hybrid AI
1.5 Ethical Considerations in AI
Core Ethical Issues
Frameworks for Ethical AI
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MODULE 2: MATHEMATICS & STATISTICS FOR AI
Duration: 3 Hours (APR 25, 2026)
Objectives
Students will apply linear algebra, probability, calculus, and optimization concepts that underpin all AI algorithms.
2.1 Linear Algebra
Scalars, Vectors, and Matrices
Key Operations
Eigenvalues and Eigenvectors
2.2 Probability and Statistics
Probability Fundamentals
Probability Distributions
Descriptive Statistics
Inferential Statistics
2.3 Calculus for AI
Derivatives
Partial Derivatives
Gradient Descent
2.4 Optimization Techniques
Variants of Gradient Descent
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MODULE 3: PROGRAMMING FOR AI
Duration: 9 Hours (Lab-Heavy) (MAY 2, MAY 9, MAY 16, 2026)
Objectives
Students will write Python and R code for data manipulation, analysis, and visualization using industry-standard libraries.
3.1 Python Fundamentals for AI
Why Python?
Core Python Concepts
3.2 R Fundamentals for AI
Why R?
Core R Concepts
3.3 Key Python Libraries
NumPy — Numerical Computing
Pandas — Data Manipulation
Matplotlib — Visualization
Seaborn — Statistical Visualization
3.4 Key R Libraries
Tidyverse — Data Science Ecosystem
ggplot2 — Grammar of Graphics
Shiny — Interactive Web Applications
3.5 Data Structures and Algorithms Basics
Essential Data Structures
Algorithm Complexity — Big O Notation
Lab Practical
· Lab 1: NumPy array operations — matrix math, broadcasting
· Lab 2: Pandas EDA — load a real dataset, clean and summarize it
· Lab 3: Visualization — recreate 5 chart types with Matplotlib and Seaborn
· Lab 4: R/Tidyverse — wrangle and visualize a dataset with ggplot2
· Lab 5: Shiny dashboard — build an interactive data explorer
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MODULE 4: MACHINE LEARNING FUNDAMENTALS
Duration: 6 Hours (MAY 23, MAY 30, 2026)
Objectives
Students will understand the three learning paradigms, apply model evaluation metrics, and diagnose overfitting/underfitting.
4.1 Supervised Learning
Regression
Classification
Workflow
4.2 Unsupervised Learning
Clustering
Dimensionality Reduction
Anomaly Detection
4.3 Reinforcement Learning Basics
4.4 Model Evaluation
Classification Metrics
Regression Metrics
NLP/Generation Metrics
Object Detection Metric
4.5 Overfitting and Underfitting
The Bias-Variance Tradeoff
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MODULE 5: CLASSICAL MACHINE LEARNING ALGORITHMS
Duration: 6 Hours (JUN 6, JUN 13, 2026)
Objectives
Students will implement and evaluate six core ML algorithms, understanding their assumptions, strengths, and limitations.
5.1 Linear Regression
5.2 Logistic Regression
5.3 Decision Trees
5.4 Random Forests
5.5 Support Vector Machines (SVM)
5.6 k-Nearest Neighbors (kNN)
Algorithm Comparison Table
Assessment
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MODULE 6: DATA PREPROCESSING & FEATURE ENGINEERING
Duration: 6 Hours (JUN 20, JUN 27, 2026)
Objectives
Students will clean messy datasets, engineer predictive features, and prepare data for model training.
6.1 Data Cleaning & Wrangling
Common Data Quality Issues
Data Wrangling Operations
6.2 Handling Missing Data
6.3 Feature Selection and Extraction
Feature Selection — Choosing the Best Features
Feature Extraction — Creating New Features
6.4 Data Normalization and Scaling
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MODULE 7: DEEP LEARNING
Duration: 6 Hours (JUL 4, JUL 11, 2026)
Objectives
Students will build and train neural networks using TensorFlow and PyTorch, understanding backpropagation and activation functions.
7.1 Introduction to Neural Networks
Architecture
7.2 Activation Functions
7.3 Backpropagation
Steps
7.4 Training Techniques
Regularization
Hyperparameters
7.5 TensorFlow Framework
7.6 PyTorch Framework
Lab Practical
· Lab 1: Build a neural network in Keras to classify handwritten digits (MNIST)
· Lab 2: Replicate the same model in PyTorch
· Lab 3: Experiment with regularization — compare accuracy with/without dropout
· Lab 4: Visualize training curves using TensorBoard
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MODULE 8: COMPUTER VISION
Duration: 6 Hours (JUL 18, JUL 25, 2026)
Objectives
Students will process images, build CNNs, and apply them to object detection and recognition tasks.
8.1 Image Processing Basics
8.2 Convolutional Neural Networks (CNNs)
Key Layers
Famous CNN Architectures
8.3 Object Detection and Recognition
Image Classification
Object Detection
Image Segmentation
8.4 Applications
Face Recognition
Medical Imaging
Autonomous Vehicles
Lab Practical
· Build a CNN image classifier using transfer learning (ResNet-50, pre-trained on ImageNet)
· Fine-tune on a custom dataset (e.g., plant disease, food classification)
· Evaluate with confusion matrix and class activation maps (Grad-CAM
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MODULE 9: NATURAL LANGUAGE PROCESSING (NLP)
Duration: 6 Hours (AUG 1, AUG 8, 2026)
Objectives
Students will process text data, build language models, and apply NLP to chatbots, translation, and sentiment analysis.
9.1 Text Preprocessing
9.2 Tokenization and Embeddings
Tokenization
Word Embeddings
9.3 Language Models
Statistical Language Models
Neural Language Models
Pre-trained Language Models
· BERT (Bidirectional Encoder)
· GPT (Generative Pre-trained Transformer)
· T5, BART
9.4 Applications
Chatbots and Conversational AI
Machine Translation
Sentiment Analysis
Information Extraction
Lab Practical
· Build a sentiment analysis pipeline using Hugging Face Transformers
· Fine-tune a pre-trained BERT model on movie review data
· Build a simple chatbot using OpenAI API or an open-source LLM
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MODULE 10: REINFORCEMENT LEARNING
Duration: 6 Hours (AUG 15, AUG 22, 2026)
Objectives
Students will implement Q-learning and policy gradient methods and apply them to game and robotics environments.
10.1 Agents and Environments
10.2 Reward Systems
10.3 Q-Learning
10.4 Policy Gradients
10.5 Applications
Games
Robotics
Other Applications
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MODULE 11: AI SYSTEMS & DEPLOYMENT
Duration: 6 Hours (AUG 29, SEP 5, 2026)
Objectives
Students will deploy trained models as REST APIs, integrate cloud AI services, and set up monitoring pipelines.
11.1 Model Deployment
Deployment Formats
Serving Architectures
11.2 APIs and Cloud AI Services
Building Model APIs with FastAPI
Cloud AI Platforms
11.3 Edge AI
Optimization Techniques
Platforms
11.4 Monitoring and Maintenance
Data & Model Drift
MLOps Pipeline
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MODULE 12: AI ETHICS & GOVERNANCE
Duration: 3 Hours (SEP 12, 2026)
Objectives
Students will identify bias in AI systems, propose fairness interventions, and understand global AI regulations.
12.1 Bias and Fairness
Types of Bias
Fairness Metrics
Debiasing Strategies
12.2 Privacy and Security
Privacy Threats
Privacy-Preserving ML
12.3 Responsible AI
Principles of Responsible AI
Explainability Methods
12.4 Regulations and Policies
Case Studies for Discussion
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MODULE 13: ADVANCED TOPICS IN AI
Duration: 6 Hours (SEP 19, SEP 26, 2026)
Objectives
Students will understand generative AI, Transformers, explainability, and the emerging field of agentic AI.
13.1 Generative AI
Generative AI creates new content — images, text, audio, video, code — that resembles training data.
Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Diffusion Models
13.2 Transformers and Large Language Models (LLMs)
The Transformer Architecture
Pre-training and Fine-tuning
Prominent LLMs
13.3 Explainable AI (XAI)
Why Explainability Matters
XAI Techniques
13.4 Agentic AI
Components of an AI Agent
Agentic Frameworks
Applications
13.5 AI in Research and Innovation
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MODULE 14: CAPSTONE PROJECT
Duration: 3 Hours (OCT 3, 2026)
Objectives
Students will independently design, build, evaluate, and deploy an end-to-end AI solution, demonstrating mastery of the full course curriculum.
14.1 Project Structure Overview
14.2 Problem Definition
Project Proposal must include:
14.3 Data Collection
14.4 Model Building
Baseline First
Iterative Improvement
14.5 Evaluation
14.6 Deployment
14.7 Report Writing and Presentation
Final Report Structure
Presentation
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MODULE 15: AI APPLICATIONS
Duration: 6 Hours (Guest Lectures + Case Studies) (OCT 10, OCT 17, 2026)
Objectives
Students will explore AI applications across seven industries, understanding domain-specific challenges, datasets, and success stories.
15.1 AI for Healthcare & Life Sciences
Key Applications
Challenges
Landmark Example
15.2 AI for Finance & Banking
Key Applications
Challenges
15.3 AI for Manufacturing & Robotics
Key Applications
Industry 4.0
15.4 AI for Big Data Analytics
Key Applications
Modern Data Stack
15.5 AI for Education
Key Applications
Challenges
15.6 AI for Legal Services
Key Applications
Challenges
15.7 AI for Cybersecurity
Defensive Applications
Offensive AI — The Threat Landscape
Challenges
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END OF COURSE DOCUMENT
