Course Overview

Objective: This course aims to provide learners with practical experience in designing, implementing, and deploying AI systems. The projects are carefully curated to address real-world challenges and offer end-to-end solutions.

Target Audience: AI practitioners, data scientists, and developers with prior knowledge of machine learning, Python, and basic AI concepts.

Prerequisites:

• Python programming
• Experience with frameworks like TensorFlow, PyTorch, or scikit-learn
• Familiarity with data preprocessing and model evaluation techniques

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Course Outline

Section 1: Foundations for AI Development

• Introduction to AI Development Tools
  • Overview of essential tools and their roles in AI development.
• Environment Setup
  • Step-by-step guide to installing Python, Anaconda, and Jupyter Notebook.
  • Configuring GPU support with CUDA and cuDNN for performance optimisation.
  • Installing key AI libraries such as TensorFlow, PyTorch, Hugging Face, and OpenCV.
• Version Control and Collaboration
  • Setting up Git and GitHub for version control and collaboration.
  • Using tools like GitHub Actions for CI/CD workflows in AI projects.
• Best Practices for Development
  • Structuring AI projects for scalability and maintainability.
  • Introduction to virtual environments and dependency management.

Section 2: Problem Identification and Objective Setting

• Real-World Problem Exploration
  • Examples of use cases (e.g., fraud detection, medical diagnosis)
  • Problem-solving strategies
• Defining Goals and Success Metrics
  • Align objectives with measurable success metrics
  • Common metrics like F1-score, RMSE, and precision-recall
• Managing Constraints
  • Strategies to overcome challenges in data, resources, and time
• Structured Frameworks
  • Overview of frameworks like CRISP-DM and design thinking for problem-solving

Section 3: Data Collection and Preprocessing

Why is Data Preparation Crucial?

• The quality of your AI model depends on the quality of the data.
• Properly preprocessed data ensures accurate, efficient, and robust model training.

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1. Sourcing Data

• Public Datasets: Explore platforms like Kaggle, UCI Machine Learning Repository, and Google Dataset Search.
• APIs: Fetch data using APIs like Twitter API, OpenWeatherMap, or public APIs for domain-specific data.
• Web Scraping: Techniques to gather data from websites using tools like BeautifulSoup and Scrapy.
• Internal Databases: Leveraging organisational data for proprietary projects.

2. Data Cleaning

• Handling Missing Values:
  • Techniques: Mean/median imputation, forward-fill/backward-fill, or dropping null records.
• Addressing Outliers:
  • Methods: Z-score analysis, IQR-based filtering, or transformations.
• Balancing Data: Address class imbalance using oversampling, undersampling, or synthetic data generation (SMOTE).

3. Exploratory Data Analysis (EDA)

• Visual Tools: Libraries like Matplotlib, Seaborn, and Plotly for creating visualisations.
• Statistical Summaries: Generating descriptive statistics to understand data distributions.
• Correlation Analysis: Identifying relationships using correlation matrices and scatter plots.

4. Preprocessing Techniques

• Data Encoding: Convert categorical variables into numerical formats using one-hot encoding or label encoding.
• Scaling and Normalisation: Standardise features with Min-Max scaling or Z-score normalisation.
• Feature Transformation: Apply log transformations, polynomial expansions, or discretisation techniques.

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Section 4: Feature Engineering and Optimisation

This section focuses on refining datasets by transforming raw data into features that have the greatest impact on model performance. Through dimensionality reduction, feature importance analysis, and optimisation tools, learners will gain a solid understanding of how to extract the most relevant insights from their data.

1. Transforming Features

• Encoding: Techniques to convert categorical data into numerical formats (e.g., one-hot encoding, label encoding).
• Scaling and Normalization: Methods like Min-Max scaling and Z-score normalization for standardizing feature values.
• Advanced Transformations: Apply transformations such as log, square root, and polynomial expansions for better feature representation.

2. Dimensionality Reduction

• Principal Component Analysis (PCA): Reducing feature space while preserving variance.
• t-Distributed Stochastic Neighbor Embedding (t-SNE): Visualizing high-dimensional data in lower dimensions for exploratory purposes.
• Autoencoders: Neural network-based dimensionality reduction for large datasets.

3. Feature Importance Analysis

• SHAP (SHapley Additive exPlanations): Explaining model predictions and identifying the most impactful features.
• LIME (Local Interpretable Model-agnostic Explanations): Analyzing individual prediction explanations.
• Tree-Based Feature Importance: Using algorithms like Random Forest and Gradient Boosted Trees to rank feature importance.

4. Automation Tools

• FeatureTools: A Python library for automated feature engineering.
• Sklearn Pipelines: Integrating preprocessing and feature engineering steps into reusable workflows.
• AutoML Frameworks: Tools like H2O.ai and Google AutoML for automated model training and feature optimization.

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Section 5: Model Training, Evaluation, and Optimisation

Why is Model Training Crucial?

Model training is at the heart of AI development. It involves teaching the model to learn patterns in data to make accurate predictions. This section covers essential techniques to train, evaluate, and optimize models to ensure robustness and scalability.

1. Model Selection

• Understanding Model Types:
  • Supervised learning (classification, regression).
  • Unsupervised learning (clustering, anomaly detection).
  • Reinforcement learning.
• Selecting the Right Algorithm:
  • Decision Trees, Random Forest, and XGBoost for structured data.
  • Convolutional Neural Networks (CNNs) for images.
  • Recurrent Neural Networks (RNNs) for time-series or sequential data.

2. Cross-Validation Techniques

• K-Fold Cross-Validation:
  • Splitting the dataset into k subsets to evaluate performance.
• Stratified K-Fold:
  • Preserving class distribution in classification problems.
• Leave-One-Out Cross-Validation (LOOCV):
  • Assessing model performance with maximum data utilization.

3. Hyperparameter Tuning

• Grid Search:
  • Exploring combinations of hyperparameters to find the best configuration.
• Random Search:
  • Randomly selecting hyperparameters for faster tuning.
• Bayesian Optimisation:
  • Using probabilistic models to optimize hyperparameters efficiently.
• Tools:
  • Libraries like Optuna and Hyperopt for automated tuning.

4. Metrics and Validation

• Classification Metrics:
  • Accuracy, Precision, Recall, F1-Score, AUC-ROC.
• Regression Metrics:
  • Mean Absolute Error (MAE), Root Mean Squared Error (RMSE).
• Clustering Metrics:
  • Silhouette Score, Davies-Bouldin Index.
• Confusion Matrix Analysis:
  • Visualizing performance for classification tasks.

5. Addressing Overfitting and Underfitting

• Overfitting:
  • Techniques: Regularization (L1, L2), dropout, data augmentation.
• Underfitting:
  • Techniques: Increasing model complexity, feature selection.

6. Benchmarking and Finalising Models

• Comparing Multiple Models:
  • Evaluate results based on performance metrics.
• Assessing Generalization
  • Test Dataset Performance: Evaluate the best models on a holdout test dataset.
  • Robustness Checks: Assess model performance under varying conditions (e.g., adding noise or reducing data).
• Selecting the Best Model:
  • Choose a model that balances accuracy, generalization, and scalability.
• Saving and Exporting Models:
  • Techniques: Pickle, joblib, or frameworks like TensorFlow SavedModel.

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Section 6: Deployment and Monitoring

• Basic Deployment Techniques
  • Methods: Flask, FastAPI, Docker
• Advanced Deployment Techniques
  • Cloud platforms: AWS, GCP, Azure
• Monitoring and Scaling
  • Tools: Grafana, Prometheus, and cloud-native solutions
  • Auto-scaling solutions for dynamic workloads
• Model Maintenance: Strategies to address concept drift
• Automated Retraining: Setting up CI/CD pipelines for continuous improvement
• Documentation and Testing: Importance of detailed documentation and rigorous testing

Section 7: Core Projects

• Sentiment Analysis Model (NLP)
  • Practical applications in customer feedback analysis
  • Guide: Preprocessing, model training, and visualization
• Chatbot Development (NLP)
  • Overview of the project
  • Step-by-step guide: Text preprocessing, training, and deployment
• Image Recognition System (Computer Vision)
  • Objective and real-world use cases
  • Steps: Training CNNs, dataset augmentation, and deployment
• Object Detection and Segmentation
  • Applications in live object tracking and video analysis
  • Guide: Training pre-trained models and real-time deployment
• GANs for Image Generation
  • Real-world applications of GANs
  • Steps to train and fine-tune GANs for creative outputs
• Reinforcement Learning Agent
  • Use cases in gaming and automation
  • Steps to implement Q-learning and OpenAI Gym environments

Section 8: Capstone Project

• Objective: Design and deploy a custom AI solution addressing a real-world problem
• Proposal Phase: Outline the project idea and receive feedback before implementation
• Steps:

1. Define the problem and collect relevant data
2. Perform data preprocessing and EDA
3. Build, train, and optimize a custom AI model
4. Deploy the solution on a web or cloud platform

• Examples:
  • Predictive analytics for healthcare
  • AI-powered recommendation system
• Outcome: A comprehensive AI solution ready for real-world use

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Course Features

• Detailed project walkthroughs
• Code templates and datasets
• Real-world problem-solving approach
• Deployment-ready solutions