ML Fundamentals
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
algorithms,unsupervised learning,data science,predictive modeling,machine learning,jupyter,python,forecasting,ai & machine learning
Description for ML Fundamentals
Machine Learning Fundamentals: Examine classification, regression, and conditional probability estimation to comprehend critical machine learning tasks.
Differences Between Generative and Discriminative Models: Discover the practical applications of generative and discriminative models.
Advanced Learning Techniques: Discover ensemble methods, including random forests, bagging, and boosting, as well as nonlinearity through kernel methods. Explore linear models.
Representation Learning: Explore the concepts of autoencoders, deep neural networks, clustering, and dimensionality reduction to acquire a deeper understanding of feature extraction and data representation.
Level: Advanced
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On edX provided by UCSanDiegoX
Duration: 8�10 hours per week approx 10 weeks
Schedule: Flexible
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