Professional Certificate in ML Design for Data Scientists
-- ViewingNowThe Professional Certificate in Machine Learning (ML) Design for Data Scientists is a crucial course that bridges the gap between data science and ML engineering. This program addresses the industry's rising demand for data scientists who can design, build, and deploy ML models.
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⢠Introduction to Machine Learning Design: Fundamentals of ML design, differentiating between ML models and algorithms, use cases and applications.
⢠Data Preparation for ML Design: Data preprocessing, feature engineering, data selection, data splitting, and data normalization techniques.
⢠Understanding ML Models: Types of ML models, strengths and weaknesses, and use cases for each model.
⢠Model Training and Evaluation: Model training methods, hyperparameter tuning, cross-validation, and model evaluation metrics.
⢠Model Deployment and Monitoring: Model deployment strategies, monitoring ML systems, and continuous improvement.
⢠Ethics and Fairness in ML Design: Understanding biases, ethical considerations, and ways to ensure fairness in ML design.
⢠Reinforcement Learning and Deep Learning: Basic principles, applications, and limitations of reinforcement learning and deep learning.
⢠Natural Language Processing (NLP) for ML Design: NLP fundamentals, text processing techniques, and NLP applications in ML.
⢠Computer Vision for ML Design: Computer vision basics, image processing techniques, and computer vision applications in ML.
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