📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI Project Management Course, from foundations to a certified capstone project.
Outline
Develop a comprehensive understanding of AI fundamentals, including machine learning, deep learning, and neural networks • Analyze mathematical concepts underlying AI, such as linear algebra, calculus, and probability theory • Design a framework for AI project management, incorporating agile methodologies and stakeholder engagement
Outline
Configure data pipelines using tools like Apache Beam, Apache Spark, and AWS Glue • Implement data preprocessing techniques, including data cleaning, feature scaling, and data transformation • Evaluate data quality and develop strategies for data validation, data normalization, and data augmentation
Outline
Design and implement model architectures using popular deep learning frameworks like TensorFlow, PyTorch, and Keras • Develop and evaluate algorithmic solutions for supervised, unsupervised, and reinforcement learning tasks • Apply AI project management methods, including project planning, risk management, and team collaboration
Outline
Train and fine-tune machine learning models using techniques like transfer learning, regularization, and early stopping • Implement hyperparameter optimization methods, including grid search, random search, and Bayesian optimization • Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and mean squared error
Outline
Deploy machine learning models using cloud platforms like AWS SageMaker, Google Cloud AI Platform, and Azure Machine Learning • Implement MLOps practices, including model monitoring, model serving, and model updating • Design and automate production workflows using tools like Docker, Kubernetes, and Apache Airflow
Outline
Analyze and address ethical concerns in AI, including bias, fairness, and transparency • Develop and implement strategies for bias mitigation, including data curation, feature engineering, and model regularization • Evaluate and ensure compliance with responsible AI practices, including explainability, accountability, and human oversight
Outline
Apply AI solutions to real-world business problems, including customer segmentation, demand forecasting, and recommender systems • Develop and evaluate AI-powered products and services, including chatbots, virtual assistants, and predictive maintenance • Analyze and discuss case studies of successful AI implementations in various industries, including healthcare, finance, and retail
Earn government-registered certification in AI Project Management Course
e-Certificate and e-Marksheet issued on successful completion.
