ML Models in Science

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While addressing real-world issues and utilizing scientific datasets, develop a comprehensive understanding of machine learning techniques and tools.

Key AI Functions:random forest,artificial neural network,python programming,machine learning,pca,ai & machine learning

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Description for ML Models in Science

Comprehensive Data Preparation: Acquire the knowledge of fundamental preprocessing techniques, including Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), to effectively clear and transform data.
Fundamental Algorithms: Acquire practical experience with fundamental AI techniques, such as K-means clustering and Support Vector Machines (SVMs), to address classification and clustering issues.
Advanced Model Development: Investigate sophisticated techniques such as random forests and neural networks to address intricate machine learning tasks.
Practical Applications and Final Project: Utilize real-world medical and astronomical datasets to develop a Python-based project that compares and evaluates machine learning models.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 21

Offered by: On Coursera provided by LearnQuest

Duration: 3 weeks at 3 hours a week

Schedule: Flexible

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