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Modeling Data in the Tidyverse
by Carrie Wright, PhD , Shannon Ellis, PhD , Stephanie Hicks, PhD , Roger D. Peng, PhD- 0.0
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Approx. 21 hours to complete
Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Random Sampling Random Forest...
Mastering Data Science and Machine Learning Fundamentals
by AI Sciences- 4.2
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2 hours on-demand video
Decision Tree and Random Forest Evaluating Machine Learning Models Performance...
$9.99
Regression Machine Learning with Python
by Diego Fernandez- 3.5
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6 hours on-demand video
Calculate generalized linear models such as linear regression or Ridge regression and select optimal linear regression coefficients regularization parameter through time series cross-validation. Calculate ensemble methods such as random forest or gradient boosting machine and select optimal maximum trees depth parameter through time series cross-validation. After that, you’ll define generalized linear models such as linear regression and Ridge regression....
$12.99
What are the Chances? Probability and Uncertainty in Statistics
by Jennifer Bachner, PhD- 0.0
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Approx. 11 hours to complete
Random Variables and Distributions Random Variables and Probability Distributions Random Variables Discrete Random Variables and Continuous Variables Random Variables and Probability Distributions Practice Problems Final Assessment on Random Variables and Distributions...
Artificial Intelligence Projects with Python-HandsOn: 2-in-1
by Packt Publishing- 3.6
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4 hours on-demand video
You will then build a classifier using random forests....
$9.99
Measurement – Turning Concepts into Data
by Jennifer Bachner, PhD- 4.8
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Approx. 11 hours to complete
Measurement Models Models of Measurement and Their Implications Final Quiz on Measurement Models Simple Random Sample Evaluating Measurement Models Final Quiz on Evaluating Measurement Models...
機器學習技法 (Machine Learning Techniques)
by 林軒田- 0.0
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Approx. 19 hours to complete
The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. Summary of Kernel Models Random Forest Algorithm Random Forest in Action Summary of Aggregation Models Summary of Extraction Models...
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Imagery, Automation, and Applications
by Nick Santos- 4.9
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Approx. 28 hours to complete
Week 3: In week three, we'll make and use digital elevation models using some new, specific tools such as the cut fill tool, hillshades, viewsheds and more. Feature Layers and Selections in Models Polishing Models for Reuse Advanced Models and Exporting Models to Python Digital Elevation Models and Common Algorithms Use products derived from digital elevation models...
Master Statistics & Probability 2020: Beginner-To-Advanced
by Kody Amour- 4.1
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13.5 hours on-demand video
Draw conclusions about random discrete variables based on their various probability distributions Coordinate multiple numerical variables with linear and exponential models...
$12.99
Machine Learning & Deep Learning in Python & R
by Start-Tech Academy- 4.4
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35 hours on-demand video
In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX....
$14.99
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