📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Machine Learning & AI Fundamentals Course, from foundations to a certified capstone project.
Outline
Develop a comprehensive understanding of the mathematical prerequisites for machine learning, including linear algebra and calculus • Analyze the fundamental concepts of artificial intelligence, including machine learning, deep learning, and neural networks • Design a basic machine learning model using a supervised learning approach, including data preprocessing and feature selection
Outline
Configure a data pipeline using Apache Beam, including data ingestion, processing, and storage • Implement data preprocessing techniques, including handling missing values, data normalization, and feature scaling • Evaluate the effectiveness of different feature engineering techniques, including feature extraction and selection
Outline
Design a convolutional neural network (CNN) architecture for image classification, including convolutional and pooling layers • Develop a recurrent neural network (RNN) model for natural language processing, including long short-term memory (LSTM) and gated recurrent units (GRU) • Analyze the performance of different machine learning algorithms, including support vector machines (SVM) and k-nearest neighbors (KNN)
Outline
Implement a grid search algorithm for hyperparameter tuning, including learning rate and batch size optimization • Evaluate the performance of a machine learning model using metrics, including accuracy, precision, and recall • Develop a strategy for handling overfitting and underfitting, including regularization and early stopping
Outline
Configure a machine learning model for deployment using Docker, including containerization and orchestration • Implement a continuous integration and continuous deployment (CI/CD) pipeline using Jenkins, including automated testing and deployment • Develop a monitoring and logging strategy for machine learning models in production, including metrics and alerts
Outline
Analyze the ethical implications of machine learning, including bias, fairness, and transparency • Develop a strategy for mitigating bias in machine learning models, including data preprocessing and feature selection • Evaluate the effectiveness of different techniques for ensuring fairness and accountability in AI systems
Outline
Develop a machine learning solution for a real-world business problem, including data collection and preprocessing • Implement a machine learning model for predictive maintenance, including sensor data analysis and anomaly detection • Evaluate the effectiveness of different machine learning algorithms for recommender systems, including collaborative filtering and content-based filtering
Earn government-registered certification in Machine Learning & AI Fundamentals Course
e-Certificate and e-Marksheet issued on successful completion.
