Machine Learning for Data Analysis is a comprehensive course designed to equip learners with the knowledge and skills to predict future outcomes using machine learning algorithms. This course, offered by Wesleyan University, builds upon fundamental concepts introduced in Course 3 and delves into advanced techniques and algorithms in machine learning.
The 350-word description covers the following topics:
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The course modules cover a wide range of topics, from decision trees and random forests to lasso regression and k-means cluster analysis. Learners will gain practical experience and a deep understanding of machine learning concepts.
This module introduces decision trees, covering the process of growing a decision tree, strengths and weaknesses, and building decision trees with SAS and Python. Additionally, learners will explore choosing between SAS and Python, working with codebooks and data sets, and running a classification tree.
The module focuses on random forests, explaining the process of growing a random forest, building random forests with SAS and Python, and validation and cross-validation. Learners will also gain hands-on experience with running a random forest and working with assignment examples.
This module delves into lasso regression, covering its definition, testing lasso regression with SAS and Python, data management, limitations, and running a lasso regression analysis. Learners will gain practical experience with SAS and Python code for lasso regression.
Exploring k-means cluster analysis, this module covers its definition, running the analysis in SAS and Python, limitations, and working with SAS and Python code. Learners will gain hands-on experience with running a k-means cluster analysis and working with assignment examples.
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