Professional Certificate in Machine Learning for Creative Media
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⢠Introduction to Machine Learning: Principles, history, and applications of machine learning. Supervised, unsupervised, and reinforcement learning. Regression, classification, and clustering algorithms.
⢠Data Preprocessing: Data cleaning, wrangling, and visualization. Dimensionality reduction techniques. Feature engineering and selection. Handling missing and categorical data.
⢠Neural Networks: Artificial neural networks, deep learning, and backpropagation. Convolutional and recurrent neural networks. Training and optimization techniques.
⢠Computer Vision: Image processing, feature extraction, and object detection. Convolutional neural networks and deep learning for image classification and segmentation. Applications in creative media.
⢠Natural Language Processing (NLP): Text preprocessing, feature extraction, and classification. Word embeddings, recurrent neural networks, and transformers. Applications in creative media: text generation, sentiment analysis, and language translation.
⢠Recommender Systems: Collaborative and content-based filtering. Matrix factorization and deep learning for recommendation. Applications in creative media: music, movie, and product recommendations.
⢠Ethics and Bias in Machine Learning: Bias and fairness in machine learning. Ethical considerations in creative media. Privacy and data protection.
⢠Machine Learning Tools and Libraries: Scikit-learn, TensorFlow, Keras, and PyTorch. Cloud-based machine learning platforms. Integrating machine learning with creative media software.
⢠Machine Learning Project Management: Planning, executing, and monitoring machine learning projects. Collaborating with interdisciplinary teams. Communicating machine learning results to stakeholders.
⢠Machine Learning for Creative Media Applications: Interactive installations, art, and design. Virtual and augmented reality. Generative art and music.
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