📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI-Assisted Circular Economy Pathways, from foundations to a certified capstone project.
Outline
Develop a comprehensive understanding of linear algebra and calculus for AI applications • Analyze the fundamentals of probability and statistics for machine learning • Configure computational frameworks for efficient AI model development
Outline
Design data pipelines for efficient data ingestion and processing • Implement data preprocessing techniques for handling missing values and outliers • Evaluate feature engineering methods for improving model performance
Outline
Implement convolutional neural networks for image classification tasks • Analyze the performance of recurrent neural networks for sequence prediction • Develop transfer learning techniques for adapting pre-trained models to new tasks
Outline
Configure hyperparameter tuning methods for optimal model performance • Evaluate model performance using metrics such as accuracy and F1-score • Develop strategies for handling overfitting and underfitting in AI models
Outline
Design containerization strategies for deploying AI models • Implement continuous integration and continuous deployment (CI/CD) pipelines • Develop monitoring and logging strategies for production AI workflows
Outline
Analyze the impact of bias in AI decision-making systems • Develop strategies for mitigating bias in AI models • Evaluate the importance of transparency and explainability in AI systems
Outline
Develop business cases for AI adoption in various industries • Implement AI solutions for real-world problems in industries such as healthcare and finance • Evaluate the return on investment (ROI) of AI solutions in different industries
Earn government-registered certification in AI-Assisted Circular Economy Pathways
e-Certificate and e-Marksheet issued on successful completion.
