📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Navigating AI Accountability and Algorithmic Bias, from foundations to a certified capstone project.
Outline
Analyze the mathematical foundations of AI and machine learning, including linear algebra, calculus, and probability theory • Develop a comprehensive understanding of AI fundamentals, including supervised and unsupervised learning, neural networks, and deep learning • Evaluate the importance of accountability and bias mitigation in AI systems, including the role of data quality, algorithmic design, and human oversight
Outline
Design and implement data pipelines for AI applications, including data ingestion, preprocessing, and feature engineering • Configure and optimize data storage solutions, including relational databases, NoSQL databases, and data warehouses • Develop and deploy data preprocessing workflows, including data cleaning, transformation, and feature extraction
Outline
Implement and evaluate various AI and machine learning algorithms, including linear regression, decision trees, random forests, and neural networks • Develop and deploy model architectures for AI applications, including computer vision, natural language processing, and recommender systems • Analyze and mitigate algorithmic bias in AI systems, including bias detection, bias correction, and fairness metrics
Outline
Configure and optimize hyperparameters for AI and machine learning models, including grid search, random search, and Bayesian optimization • Develop and deploy model training workflows, including data splitting, model selection, and model evaluation • Evaluate the performance of AI and machine learning models, including metrics, benchmarks, and model interpretability
Outline
Design and implement deployment strategies for AI and machine learning models, including model serving, monitoring, and maintenance • Develop and deploy MLOps workflows, including continuous integration, continuous deployment, and continuous monitoring • Configure and optimize production environments for AI applications, including cloud computing, containerization, and orchestration
Outline
Analyze and address ethical concerns in AI applications, including fairness, transparency, and accountability • Develop and implement bias mitigation strategies, including data curation, algorithmic auditing, and human oversight • Evaluate and promote responsible AI practices, including explainability, interpretability, and human-centered design
Outline
Develop and deploy AI solutions for industry-specific applications, including healthcare, finance, and retail • Analyze and evaluate the business value of AI applications, including return on investment, cost savings, and revenue growth • Implement and evaluate AI-powered business workflows, including automation, optimization, and decision support
Earn government-registered certification in Navigating AI Accountability and Algorithmic Bias
e-Certificate and e-Marksheet issued on successful completion.
