Graduate Certificate in Applied Machine Learning for Software Engineering
-- ViewingNowThe Graduate Certificate in Applied Machine Learning for Software Engineering is a vital course designed to equip learners with essential skills in machine learning and artificial intelligence. In the current technology-driven world, there is an increasing demand for professionals who can apply machine learning algorithms to solve real-world problems.
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• Introduction to Machine Learning · Overview of machine learning principles, algorithms, and applications in software engineering.
• Data Preprocessing · Techniques for cleaning, transforming, and visualizing data for machine learning models.
• Supervised Learning · In-depth study of popular supervised learning algorithms, including linear regression, logistic regression, and support vector machines.
• Unsupervised Learning · Exploration of unsupervised learning techniques, such as clustering, dimensionality reduction, and autoencoders.
• Deep Learning · Introduction to deep learning models, including convolutional neural networks and recurrent neural networks.
• Natural Language Processing · Application of machine learning techniques to natural language processing tasks, such as text classification, sentiment analysis, and language translation.
• Evaluation Metrics · Methods for evaluating machine learning model performance, including accuracy, precision, recall, and F1 score.
• Ethics in Machine Learning · Examination of ethical considerations in machine learning, including fairness, accountability, transparency, and privacy.
• Machine Learning Tools · Hands-on experience with popular machine learning libraries and frameworks, such as TensorFlow, PyTorch, and Scikit-Learn.
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- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- ThreeFourHoursPerWeek
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