This course, "Machine Learning Models in Science," offered by LearnQuest, equips learners with the knowledge and skills to apply machine learning techniques to scientific problems. Throughout the course, participants will delve into the complete machine learning pipeline, from data preprocessing to running basic and advanced machine learning algorithms. The curriculum covers essential topics, including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SVM), K-means clustering, decision trees, random forests, and neural networks.
Participants will gain hands-on experience using Python to implement and evaluate various machine learning models, with a focus on medical and astronomical datasets. The final project will provide an opportunity to compare and analyze different machine learning models in a real-world context.
By the end of the course, learners will be equipped with the necessary skills to work with scientific data, apply machine learning models, and evaluate their performance using Python.
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Machine Learning Models in Science covers a range of topics, including data preprocessing techniques, foundational AI algorithms such as SVMs and K-means clustering, and advanced methods like neural networks and decision trees, all implemented in Python.
This module introduces learners to the foundational concepts and techniques in data preprocessing, such as calculating eigenvalues and eigenvectors, principal component analysis (PCA), and linear discriminant analysis (LDA). Participants will gain a deep understanding of the mathematical and programming aspects essential for working with scientific data and machine learning models in Python.
Building on the foundational knowledge from the previous module, this section delves into the essential AI algorithms of K-means clustering and support vector machines (SVM). Participants will explore the differences between supervised and unsupervised learning techniques and gain practical experience in implementing and comparing these algorithms using Python.
This module focuses on advanced AI techniques, including decision trees, random forests, and neural networks. Participants will gain an understanding of these methods and apply their knowledge to implement and evaluate neural networks using Python. The module also includes a practical NN Playground for hands-on learning.
The final project module provides an opportunity for participants to apply their skills and knowledge gained throughout the course. Participants will compare different machine learning models using Python in a real-world context, allowing them to showcase their ability to work with scientific data and evaluate machine learning models effectively.
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