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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• 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.
• 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).
• 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.
• 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 Optimization

• Transforming Features: Encoding, scaling, normalization, and advanced transformations
• Dimensionality Reduction: Techniques like PCA and t-SNE for simplifying data
• Feature Importance Analysis: Techniques like SHAP and LIME for interpreting model behavior
• Automation Tools: Tools like FeatureTools for faster processing

Section 5: Model Training, Evaluation, and Optimisation

• Model Selection: Choosing algorithms for classification, regression, or clustering
• Cross-Validation: Techniques to ensure model robustness
• Hyperparameter Tuning: Grid Search, Random Search, and Bayesian Optimization
• Metrics and Validation: Key evaluation metrics (accuracy, precision, recall, F1-score)
• Addressing Overfitting: Techniques like regularization and dropout
• Benchmarking and Finalizing Models: Compare results for optimal performance

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