Artificial Intelligence / Machine Learning Training, Port Harcourt, Rivers State

Artificial Intelligence / Machine Learning Training Course

This course offers a hands-on, project-based approach to mastering Artificial Intelligence and Machine Learning. You’ll move beyond theory to build practical skills by working on real-world projects, gaining a deep understanding of AI/ML concepts, and developing a portfolio of applied knowledge.

Course I. Foundations of Artificial Intelligence

Module 1: Introduction to Artificial Intelligence (AI) – Understanding the Landscape

This module lays the groundwork for understanding Artificial Intelligence, its historical context, and its diverse applications. You’ll gain a foundational vocabulary and appreciate the scope of this transformative field.

Learning Objectives:

• Define Artificial Intelligence and its Core Principles: Articulate what AI is, differentiating between strong AI, weak AI, and general AI, and discuss the core principles driving AI development (e.g., problem-solving, reasoning, perception, learning).
• Identify the Various Subfields of AI: Categorize and describe key AI subfields such as Machine Learning, Natural Language Processing (NLP), Computer Vision, Robotics, Expert Systems, and Planning.
• Understand the Turing Test and its Limitations: Explain the concept of the Turing Test as a measure of machine intelligence and critically analyze its effectiveness and inherent limitations in defining true intelligence.

Hands-on/Project Focus:

• Interactive Discussion & Brainstorming:
  • Activity: “AI in My Daily Life” – you share examples of AI they encounter daily (e.g., recommendation systems, voice assistants, spam filters).
  • Discussion: Explore the societal impact of these AI applications, both positive and negative.
• Turing Test Simulation (Conceptual):
  • Activity: Teams brainstorm scenarios to design a simple “Turing Test” for a hypothetical AI (e.g., a chatbot). Discuss what questions or interactions would be most effective and what challenges they might face.
  • Reflection: Analyze why passing the Turing Test doesn’t necessarily equate to human-level intelligence.

Module 2: Applications of AI – Real-World Impact

This module delves into the diverse real-world applications of AI, showcasing its profound impact across various industries. You’ll explore successful implementations and begin to envision how AI can solve complex problems.

Learning Objectives:

• Recognize the Impact of AI on Various Aspects of Society: Analyze how AI is transforming sectors like healthcare (diagnostics, drug discovery), finance (fraud detection, algorithmic trading), transportation (self-driving cars, logistics optimization), education, entertainment, and manufacturing.
• Analyze Case Studies Showcasing Successful AI Implementations: Examine detailed examples of AI projects that have achieved significant results, understanding the problem they addressed, the AI techniques used, and the outcomes.

Hands-on/Project Focus:

• Case Study Deep Dive & Presentation:
  • Activity: Trainees, in small groups, choose a specific industry (e.g., healthcare, finance, retail). They research and present a detailed case study of a successful AI implementation in that industry. Presentations should cover:
    • The problem the AI solved.
    • The specific AI technologies employed.
    • The measurable impact and benefits.
    • Challenges faced and lessons learned.
• Project Idea Generation: “AI for Good” Brainstorm:
  • Project: Begin brainstorming potential AI projects that could address a real-world problem. Focus on problems that AI could genuinely impact. This is the initial ideation phase for your capstone project.
  • Task: Each participant or group proposes 2-3 initial project ideas, outlining the problem, potential AI solution, and expected impact.

 

Module 3: The Ethics of AI – Responsibility and Fairness

Understanding the ethical implications of AI is crucial for responsible development and deployment. This module explores bias, fairness, transparency, and accountability in AI systems.

Learning Objectives:

• Identify Potential Biases Present in AI Systems: Recognize how data bias, algorithmic bias, and human bias can lead to discriminatory or unfair AI outcomes. Discuss examples from various domains.
• Explain the Importance of Fairness and Transparency in AI: Articulate why fairness (e.g., equal outcomes, non-discrimination) and transparency (e.g., explainability, interpretability) are critical for building trustworthy AI.
• Explore Ethical Frameworks for Responsible AI Development: Learn about existing ethical guidelines and frameworks (e.g., principles of beneficence, non-maleficence, autonomy, justice) and discuss their application in AI design and deployment.

Hands-on/Project Focus:

• Ethical AI Case Study Analysis:
  • Project: You will be provided with several real-world scenarios or historical examples of AI systems exhibiting bias or ethical concerns (e.g., facial recognition bias, loan application algorithms, recidivism prediction tools).
  • Task: In groups, analyze one case study, identifying:
    • The ethical dilemma.
    • The source of bias (if applicable).
    • Potential solutions or mitigations.
    • Relevant ethical principles at play.
  • Output: Group presentations or written reports discussing their findings and proposed ethical considerations for future development.

Course II. Introduction to Machine Learning (ML)

Module 4: Fundamentals of Machine Learning – The Core Concepts

This module introduces the fundamental concepts of Machine Learning, laying the groundwork for understanding how machines learn from data.

Learning Objectives:

• Differentiate Between Supervised, Unsupervised, and Reinforcement Learning: Clearly define and provide examples for each primary type of machine learning, highlighting their distinct goals and applications.
• Explain the Role of Data in Machine Learning: Understand the paramount importance of data quality, quantity, and preparation. Discuss concepts like features, labels, datasets, and the train-test split.
• Identify Common Evaluation Metrics Used for ML Models: Learn about basic metrics for classification (accuracy, precision, recall, F1-score) and regression (Mean Squared Error, R-squared), and understand why different metrics are used for different problems.

Hands-on/Project Focus:

• “Predictive Power” Data Exploration Project:
  • Project: You will be provided with a small, clean dataset (e.g., housing prices, iris flower dataset, a simple customer churn dataset).
  • Task: Using a spreadsheet program or basic Python (if Module 5 is started early), explore the dataset to:
    • Identify potential features and target variables.
    • Discuss whether the problem would be supervised or unsupervised.
    • Brainstorm how they might evaluate a model built on this data.
  • Deliverable: A short report outlining their observations and initial thoughts on modeling approach.

Module 5: Machine Learning Workflow – From Data to Deployment

This module provides a practical understanding of the end-to-end Machine Learning workflow, from data collection to model deployment. This is where you begin to truly “get your hands dirty.”

Learning Objectives:

• Describe the Steps Involved in the Machine Learning Lifecycle: Detail the sequence of activities: problem definition, data collection, data preprocessing, feature engineering, model selection, model training, evaluation, tuning, and deployment.
• Explain the Importance of Data Pre-processing Techniques: Understand common techniques like handling missing values, outlier detection, data scaling (normalization, standardization), encoding categorical variables, and data cleaning.
• Understand the Model Training and Evaluation Process: Grasp the concepts of training a model on data, using validation sets, evaluating performance with chosen metrics, and the importance of preventing overfitting and underfitting.

Hands-on Session 1: Introduction to Python Programming for ML 

This foundational session equips beginners with the necessary Python skills for Machine Learning.

• Core Concepts: Variables, data types (integers, floats, strings, booleans), operators, control flow (if/else, loops), functions, basic data structures (lists, tuples, dictionaries, sets).
• Introduction to NumPy: Array creation, indexing, slicing, basic array operations.
• Introduction to Pandas: DataFrames, reading/writing CSVs, selecting data, filtering, basic data manipulation (group by, merge).
• Basic Data Visualization with Matplotlib/Seaborn: Creating scatter plots, bar charts, histograms.
• Practice Projects: Small coding challenges like data cleaning tasks, simple data analysis, and plotting insights from small datasets.

Hands-on/Project Focus: “Data Prep & First Model” Mini-Project

• Project: You will choose a relatively clean, small-to-medium dataset (e.g., Kaggle’s Titanic dataset, a simpler medical diagnosis dataset).
• Task: Using Python, Pandas, and Scikit-learn (basic functionalities), perform the following steps:
  1. Data Loading: Load the dataset into a Pandas DataFrame.
  2. Initial Exploration: Use .info(), .describe(), .isnull().sum(), and basic plots to understand the data.
  3. Basic Pre-processing: Handle a few obvious missing values (e.g., fill with mean/median or drop rows). Potentially perform simple encoding if categorical data is present.
  4. Train-Test Split: Split the data into training and testing sets.
  5. First Model: Train a very simple model (e.g., LogisticRegression or LinearRegression from Scikit-learn, even if not fully understood yet) on the training data.
  6. Basic Evaluation: Make predictions on the test set and calculate a primary evaluation metric (e.g., accuracy for classification, RMSE for regression).
• Deliverable: Jupyter Notebook with documented code, demonstrating each step of the workflow and the calculated metric.

 

Course III. Supervised Machine Learning Algorithms

Module 6: Linear Regression – Predicting Continuous Values

This module dives into linear regression, a foundational supervised learning algorithm used for predicting continuous target variables.

Learning Objectives:

• Explain the Concept of Linear Regression and its Applications: Understand the mathematical basis of simple and multiple linear regression (least squares), its assumptions, and common applications (e.g., predicting house prices, sales, temperatures).
• Interpret the Results of a Linear Regression Model: Understand coefficients, R-squared, p-values, and how to assess model fit and significance.

Hands-on/Project Focus:

• Project: House Price Prediction (Simple Linear Regression)
  • Dataset: A real-world dataset of house prices with features like square footage, number of bedrooms, location, etc. (e.g., a simplified version of the Boston Housing dataset or a similar Kaggle dataset).
  • Task:
    1. Data Loading & Initial Exploration: Load data, identify features and target (price).
    2. Feature Engineering (Basic): Create a simple new feature if appropriate (e.g., price_per_sqft).
    3. Data Cleaning: Handle missing values and outliers.
    4. Model Training: Train a LinearRegression model using Scikit-learn.
    5. Model Evaluation: Calculate Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared on the test set.
    6. Interpretation: Analyze the coefficients and discuss their implications.
    7. Visualization: Plot actual vs. predicted values to visualize model performance.
  • Deliverable: A Jupyter Notebook with the full workflow, including visualizations and a clear interpretation of the model’s findings.

 

Module 7: Classification Algorithms – Predicting Discrete Outcomes

This module explores common classification algorithms, crucial for predicting discrete outcomes. You’ll understand their nuances and apply them to practical problems.

Learning Objectives:

• Distinguish Between Different Classification Algorithms and their Strengths/Weaknesses: Understand the working principles, pros, and cons of Logistic Regression, Decision Trees, Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN).
• Apply Classification Algorithms to Solve Practical Problems: Implement and tune these algorithms for various classification tasks.

Hands-on Session 2: Supervised Learning in Python (Classification Project)

• Project: Customer Churn Prediction
  • Dataset: A telecommunications customer churn dataset (features like contract type, monthly charges, tenure, etc., and a binary ‘Churn’ target).
  • Task:
    1. Data Preprocessing: Handle categorical variables (one-hot encoding), scaling numerical features.
    2. Algorithm Implementation & Comparison:
       • Train and evaluate Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) models on the preprocessed data.
       • For each model:
         • Perform cross-validation.
         • Calculate and interpret classification metrics: Accuracy, Precision, Recall, F1-score, and Confusion Matrix.
         • Visualize the Decision Tree (if applicable).
    3. Hyperparameter Tuning (Basic): Experiment with a few key hyperparameters for at least one model (e.g., max_depth for Decision Tree, n_neighbors for KNN) and observe the impact on performance.
    4. Model Selection: Compare the performance of all models and justify which model is best for this specific problem and why (e.g., importance of Recall for churn prediction).
  • Deliverable: A comprehensive Jupyter Notebook comparing the performance of multiple classification models, including visualizations (confusion matrices, ROC curves if time permits), and a clear conclusion on the best model for the churn prediction task.

Course IV. Unsupervised Machine Learning Algorithms

Module 8: Clustering Algorithms – Grouping Data

This module introduces unsupervised learning techniques, focusing on clustering algorithms to discover inherent groupings within data.

Learning Objectives:

• Explain the Concept of Clustering and its Applications: Understand the goal of clustering (identifying natural groups without predefined labels) and its applications (customer segmentation, anomaly detection, document clustering).
• Implement K-Means Clustering and Interpret the Results: Understand the K-Means algorithm, how to determine the optimal number of clusters (e.g., elbow method), and how to interpret the resulting clusters.

Hands-on/Project Focus: Customer Segmentation with K-Means

• Dataset: A dataset containing customer purchasing behavior, demographics, or website activity (e.g., a mall customer dataset with annual income, spending score).
• Task:
  1. Data Preprocessing: Scale numerical features.
  2. Optimal K Determination: Use the Elbow Method to determine an appropriate number of clusters for the dataset.
  3. K-Means Implementation: Apply K-Means clustering to the data using the chosen ‘k’.
  4. Cluster Analysis: Analyze the characteristics of each cluster (e.g., average income, spending habits for each customer segment) to understand what differentiates them.
  5. Visualization: Plot the clusters in 2D or 3D (using PCA if necessary to reduce dimensions for visualization, tying into Module 9) to visually inspect the groupings.
  6. Application Discussion: Discuss potential business applications of these customer segments (e.g., targeted marketing campaigns).
• Deliverable: A Jupyter Notebook demonstrating the K-Means clustering process, including the elbow method, cluster analysis, and visualizations, with a discussion of the insights gained.

Module 9: Dimensionality Reduction Techniques – Simplifying Data

This module explores dimensionality reduction techniques, particularly Principal Component Analysis (PCA), to reduce the number of features in a dataset while retaining important information.

Learning Objectives:

• Understand the Benefits of Dimensionality Reduction: Explain why reducing dimensions is important (e.g., curse of dimensionality, noise reduction, improved model performance, visualization).
• Apply PCA for Feature Selection and Model Improvement: Understand the working principle of PCA, how to compute principal components, and how to use them for data visualization or as input to other machine learning models.

Hands-on Session 3: Unsupervised Learning in Python (PCA Application)

• Project: Image Compression and Classification with PCA
  • Dataset: A simple image dataset (e.g., MNIST handwritten digits, or a subset of a larger image dataset).
  • Task:
    1. Image Loading & Flattening: Load images and flatten them into feature vectors.
    2. PCA for Visualization: Apply PCA to reduce the image data to 2 or 3 components for visualization. Plot the reduced data, observing if clusters (e.g., digits) are discernible.
    3. PCA for Compression/Feature Engineering: Apply PCA to reduce the dimensionality significantly (e.g., retaining 95% variance).
    4. Classification on Reduced Data: Train a simple classification model (e.g., Logistic Regression or SVM) on both the original high-dimensional data and the PCA-reduced data.
    5. Performance Comparison: Compare the training time and classification accuracy/performance of the model trained on the original data versus the PCA-reduced data. Discuss the trade-offs.
• Deliverable: A Jupyter Notebook illustrating the application of PCA for dimensionality reduction, including visualizations and a comparative analysis of model performance on original versus reduced data.

 

Course V. Deep Learning and Artificial Neural Networks (ANNs)

Module 10: Introduction to Deep Learning – Beyond Traditional ML

This module provides a comprehensive overview of Deep Learning, a powerful subfield of Machine Learning utilizing Artificial Neural Networks (ANNs) for complex pattern recognition.

Learning Objectives:

• Explain the Basic Structure and Function of Artificial Neural Networks: Understand the concepts of neurons, layers (input, hidden, output), activation functions (ReLU, Sigmoid, Softmax), weights, biases, and the forward/backward propagation process.
• Understand the Concept of Deep Learning Architectures: Define what makes a neural network “deep” and discuss the advantages of deep architectures in learning hierarchical representations.

Hands-on/Project Focus:

• “Neural Network from Scratch” (Conceptual/Simple Implementation):
  • Activity: you will conceptually walk through or even implement a very simple single-layer perceptron or a small multi-layer perceptron using pure NumPy (without TensorFlow/Keras yet).
  • Task:
    • Define inputs, weights, and bias.
    • Implement a simple activation function.
    • Perform a forward pass calculation.
    • (Optional, if time permits) Implement a rudimentary backward pass for weight updates.
  • Purpose: To demystify the underlying calculations of ANNs before moving to high-level frameworks.
• Deep Learning Environment Setup: Guides you through setting up their Deep Learning environment (e.g., Anaconda, installing TensorFlow/Keras, ensuring GPU drivers are configured if applicable).

Module 11: Deep Learning Architectures – CNNs and RNNs

This module explores popular Deep Learning architectures, focusing on Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for sequence data.

Learning Objectives:

• Identify Different Deep Learning Architectures and their Applications: Differentiate between Feedforward NNs, CNNs, and RNNs, and understand their specific use cases (e.g., image classification, natural language processing, time series prediction).
• Explain the Working Principles of CNNs and RNNs: Understand concepts like convolutional layers, pooling layers, recurrent connections, and memory cells (LSTMs, GRUs).

Hands-on Session 4: Deep Learning Frameworks (TensorFlow/Keras)

• Project: Image Classification with Convolutional Neural Networks (CNNs)
  • Dataset: A well-known image classification dataset (e.g., CIFAR-10, Fashion MNIST, or a simpler custom image dataset).
  • Task:
    1. Data Loading & Preprocessing: Load and prepare the image data (resizing, normalization).
    2. CNN Model Design: Design and build a simple CNN model using Keras/TensorFlow, incorporating:
       • Convolutional layers (Conv2D).
       • Pooling layers (MaxPooling2D).
       • Flatten layer.
       • Dense layers.
       • Appropriate activation functions (e.g., ReLU, Softmax).
    3. Model Training: Compile and train the CNN model on the dataset.
    4. Model Evaluation: Evaluate the model’s performance using accuracy, loss, and confusion matrix.
    5. Visualization: Plot training history (accuracy and loss curves).
    6. Prediction: Make predictions on new, unseen images.
• Project: Text Classification with Recurrent Neural Networks (RNNs)
  • Dataset: A text classification dataset (e.g., IMDB movie review sentiment analysis, spam detection).
  • Task:
    1. Text Preprocessing: Tokenization, padding sequences, creating word embeddings (simple Embedding layer in Keras).
    2. RNN Model Design: Build a simple RNN model using Keras/TensorFlow, incorporating:
       • Embedding layer.
       • SimpleRNN or LSTM or GRU layer.
       • Dense layers.
    3. Model Training & Evaluation: Train the RNN model and evaluate its performance.
    4. Prediction: Make sentiment predictions on new text inputs.
• Deliverable: Two separate Jupyter Notebooks (one for CNN, one for RNN) showcasing the full deep learning workflow, including model definition, training, evaluation, visualizations, and predictions.

Capstone Project: End-to-End AI/ML Solution

Duration:

This is the culminating project where you will apply all acquired knowledge to build a complete, end-to-end AI/ML solution to a real-world problem.

Project Goals:

• Problem Definition: Clearly define a problem that can be addressed by an AI/ML solution. This could be an extension of an earlier “AI for Good” idea or a new, more complex challenge.
• Data Acquisition & Preparation: Identify, collect (or find suitable public datasets), and meticulously preprocess the data. This will involve significant data cleaning, handling missing values, encoding, and feature engineering.
• Model Selection & Development: Choose appropriate ML/DL algorithms based on the problem type (supervised, unsupervised, deep learning) and develop the model. This may involve training multiple models and comparing their performance.
• Evaluation & Tuning: Rigorously evaluate the model using relevant metrics, perform hyperparameter tuning, and address issues like overfitting/underfitting.
• Deployment Strategy (Conceptual/Basic Implementation): Outline a strategy for deploying the model. Ideally, you will build a very basic Flask/Streamlit application to demonstrate model inference.
• Ethical Considerations: Document and address any ethical considerations related to the project (e.g., potential biases in data or model, fairness, transparency).

Capstone Project

• Predictive Maintenance: Predict equipment failure based on sensor data.
• Medical Image Analysis: Classify diseases from X-rays or MRI scans.
• Financial Fraud Detection: Identify fraudulent transactions.
• Personalized Recommendation System: Recommend products/movies/music.
• Natural Language Chatbot: Build a simple intent-based chatbot.
• Automated Content Moderation: Classify text/images for inappropriate content.
• Smart City Analytics: Predict traffic congestion or optimize resource allocation.

Deliverables:

• Project Proposal: A detailed document outlining the problem, data sources, proposed methodology, and expected outcomes.
• Clean and Documented Codebase: A well-structured GitHub repository containing all Python code (Jupyter Notebooks, scripts).
• Technical Report: A comprehensive report detailing the project, including:
  • Problem statement and motivation.
  • Data collection and preprocessing steps.
  • Exploratory Data Analysis (EDA).
  • Model architecture and training details.
  • Evaluation metrics and results.
  • Challenges faced and solutions implemented.
  • Ethical considerations.
  • Future work.
• Live Demonstration/Presentation: A presentation demonstrating the working model, its features, and the insights gained. If a basic deployment is achieved, a live demo of the application.

Tools and Libraries Used Throughout the Course:

• Programming Language: Python
• Core Libraries:
  • NumPy: Numerical computing
  • Pandas: Data manipulation and analysis
  • Matplotlib, Seaborn: Data visualization
• Machine Learning Frameworks:
  • Scikit-learn: Traditional ML algorithms
  • TensorFlow/Keras: Deep Learning
• Development Environment: Jupyter Notebooks, Google Colab
• Version Control: Git & GitHub (for capstone project)
• Deployment Tools: Flask, Streamlit