Global Certificate in Machine Learning for Renewables
-- ViewingNowThe Global Certificate in Machine Learning for Renewables is a cutting-edge course that addresses the growing demand for expertise in harnessing machine learning to optimize renewable energy. This program equips learners with essential skills to advance their careers in this rapidly evolving field.
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⢠Fundamentals of Machine Learning: Introduction to machine learning, supervised and unsupervised learning, regression and classification algorithms.
⢠Data Analysis for Renewables: Data preprocessing, data cleaning, exploratory data analysis, statistical analysis for renewable energy data.
⢠Time Series Analysis and Forecasting: Time series analysis, decomposition methods, ARIMA, SARIMA, prophet, long short-term memory (LSTM) networks.
⢠Feature Engineering and Selection: Feature engineering techniques, feature selection methods, dimensionality reduction, recursive feature elimination.
⢠Deep Learning for Renewables: Introduction to deep learning, neural networks, convolutional neural networks, recurrent neural networks, long short-term memory (LSTM) networks.
⢠Reinforcement Learning for Renewables: Introduction to reinforcement learning, Q-learning, deep Q-learning, policy gradients, actor-critic methods.
⢠Machine Learning Applications in Renewables: Solar and wind energy forecasting, energy demand prediction, anomaly detection, fault diagnosis, predictive maintenance.
⢠Ethics and Bias in Machine Learning: Ethical considerations in machine learning, addressing bias in data and algorithms, fairness and accountability.
⢠Machine Learning Deployment and Maintenance: Cloud computing, containerization, model deployment, model monitoring, continuous integration and delivery (CI/CD).
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