Machine Learning
Showing 181–192 of 273 results
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Machine Learning with PySpark
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
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Machine Learning with Python
Dive into machine learning with Python. Learn to build models, preprocess data, and evaluate results using Python libraries like Scikit-learn, pandas, and numpy, to unlock powerful insights for a variety of business and data science applications.
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Machine Learning with R
Master machine learning with R, a popular language for data analysis and statistical modeling. Learn how to apply machine learning algorithms, preprocess data, and evaluate models using R’s powerful libraries and tools for data science.
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Machine Learning with Tree-Based Models in Python
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
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Machine Learning with Tree-Based Models in R
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
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Machine Learning: Artificial Intelligence Decision Making with Minimax
Teach computers how to make decisions and play games with the Minimax Algorithm!
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Machine Learning: Clustering with K-Means
Level up your machine learning skills by using unsupervised learning to find patterns hidden in data.
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Machine Learning: Introduction with Regression
Get started with machine learning and learn how to build, implement, and evaluate linear regression models.
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Machine Learning: K-Nearest Neighbors
Sharpen your machine learning skills by learning how to prepare, implement, and assess the K-Nearest Neighbors algorithm.
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Machine Learning: Logistic Regression
Predict the probability that a datapoint belongs to a given class with Logistic Regression.
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Machine Learning: Perceptrons
Level up your machine learning skills by learning how to build perceptrons: the foundations of neural networks.
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Machine Learning: Random Forests & Decision Trees
Learn how to build decision trees and then build those trees into random forests.