Advanced Programming for AI Integration

This programme aims to equip participants with the knowledge and practical experience needed to leverage AI for improving productivity, optimising processes, and enhancing decision-making in their work environments.

Advanced Python for AI (20 Hours)

• Strengthen Python programming skills with a focus on AI-related tasks.
• Understand how to use Python libraries for data manipulation and analysis.
• Learn best practices for writing clean, efficient, and scalable code.

Advanced Python Techniques

• Functional programming
• Generators and iterators
• Decorators and context managers

Data Handling with Python

• Advanced Pandas for data manipulation
• NumPy for numerical operations
• Data cleaning and preprocessing

Optimizing Code for AI Applications

• Code optimization techniques
• Profiling and debugging complex code
• Parallel processing and concurrency

Practical Activities:

• Implementing advanced Python techniques in real-world scenarios.
• Manipulating large datasets and optimizing code for AI tasks.
• Code reviews and collaborative programming exercises.

Machine Learning for Professionals (20 Hours)

• Gain hands-on experience with machine learning algorithms.
• Understand how to select and implement appropriate models for various tasks.
• Learn to interpret and present machine learning results to non-technical stakeholders.

Supervised and Unsupervised Learning

• Advanced regression and classification techniques
• Clustering and dimensionality reduction
• Ensemble methods (Random Forest, Gradient Boosting)

Model Evaluation and Tuning

• Cross-validation and hyperparameter tuning
• Dealing with overfitting and underfitting
• Model interpretability and explainability (e.g., SHAP, LIME)

Practical Applications of Machine Learning

• Case studies in different industries (e.g., finance, healthcare, marketing)
• Deployment of machine learning models in production environments

Practical Activities:

• Implementing and tuning machine learning models.
• Working on real-world datasets to solve industry-specific problems.
• Presenting findings and recommendations based on model outputs.

AI Tools and Frameworks (20 Hours)

• Familiarise with popular AI tools and frameworks used in the industry.
• Learn how to integrate AI tools into existing software solutions.
• Understand the ethical implications and best practices for AI in the workplace.

Overview of AI Tools

• TensorFlow and PyTorch for deep learning
  
• Scikit-learn for classical machine learning
• Keras for high-level model building

Natural Language Processing (NLP)

• Text preprocessing and feature extraction
• Implementing NLP tasks (e.g., sentiment analysis, text classification)
• Using pre-trained models and transformers (e.g., BERT, GPT)

Ethics and AI Governance

• Understanding bias and fairness in AI models
• Data privacy and security considerations
• AI governance frameworks and compliance

Practical Activities:

• Developing AI models using TensorFlow or PyTorch.
• Implementing NLP tasks using pre-trained models.
• Conducting an ethical review of AI use cases in your work environment.

Automation and AI in the Workplace (20 Hours)

• Learn how to use AI for process automation and optimization.
• Understand how to implement AI-driven solutions to improve productivity.
• Explore the integration of AI with other technologies (e.g., RPA, IoT).

AI for Automation

• Process automation with AI (e.g., Robotic Process Automation, RPA)
• Predictive analytics and decision support systems
• AI for workflow optimization

Integration with Existing Technologies

• Combining AI with IoT for smart systems
• Using AI in cloud environments (e.g., Azure AI, AWS AI)
• Building AI-driven applications with APIs and microservices

Case Studies

• Successful AI integration examples in various industries
• Lessons learned and best practices

Practical Activities:

• Automating a business process using AI tools.
• Developing a small-scale AI-driven application.
• Case study analysis and presentation.

Capstone Project and Advanced Certification Preparation (20 Hours)

• Apply all learned concepts to a comprehensive capstone project.
• Prepare for advanced AI-related certifications.
• Develop a portfolio that showcases AI integration skills.

Capstone Project

• Define a real-world problem and develop an AI-driven solution.
• Focus on integration, automation, and optimization.
• Document and present the project.

Certification
Review and practice for certifications such as:

• Microsoft Certified: Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty