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Forecasting Models with R
by Diego Fernandez- 3.9
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5.5 hours on-demand video
Learn main forecasting models from basic to expert level through a practical course with R statistical software. Estimate non-seasonal autoregressive integrated moving average models such as random walk, random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, simple exponential smoothing with growth, Holt linear trend and Gardner additive damped trend models....
$12.99
Forecasting Models with Excel
by Diego Fernandez- 2.6
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8 hours on-demand video
Estimate non-seasonal autoregressive integrated moving average models such as random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, simple exponential smoothing with growth, Holt linear trend and Gardner additive damped trend models. Approximate seasonal autoregressive integrated moving average models such as seasonal random walk, seasonal random trend and seasonally differentiated first order autoregressive models....
$14.99
Forecasting Models with Python
by Diego Fernandez- 3.5
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5.5 hours on-demand video
Estimate non-seasonal autoregressive integrated moving average models such as random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, Holt linear trend and Gardner additive damped trend models. Approximate seasonal autoregressive integrated moving average models such as seasonal random walk with drift, seasonally differentiated first order autoregressive and Holt-Winters additive seasonality models....
$12.99
Probability: Distribution Models & Continuous Random Variables
by Mark D. Ward- 0.0
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6 Weeks
In this statistics and data analysis course, you will learn about continuous random variables and some of the most frequently used probability distribution models including, exponential distribution, Gamma distribution, Beta distribution, and most importantly, normal distribution. Some of the most widely used probability models with continuous random variables How distribution models we have encountered connect with Normal distribution...
$49
Random Models, Nested and Split-plot Designs
by Douglas C. Montgomery- 4.6
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Approx. 9 hours to complete
Unit 1: Experiments with Random Factors Design and analyze experiments where some of the factors are random...
Advanced Forecasting Models with Excel
by Diego Fernandez- 3.2
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8 hours on-demand video
Estimate autoregressive integrated moving average models with residuals or forecasting errors assumed as Gaussian or Student’s t distributed and with Bollerslev simple or Glosten-Jagannathan-Runkle threshold generalized autoregressive conditional heteroscedasticity effects such as random walk with drift and differentiated first order autoregressive. Estimate autoregressive integrated moving average models such as random walk with drift and differentiated first order autoregressive....
$12.99
Probability: Basic Concepts & Discrete Random Variables
by Mark D. Ward- 0.0
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6 Weeks
Then we will discuss a few important probability distribution models with discrete random variables, including Bernoulli and Binomial distributions, Geometric distribution, Negative Binomial distribution, Poisson distribution, Hypergeometric distribution and discrete uniform distribution. Some of the most widely used probability models with discrete random variables How probability models work in practical problems...
$49
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Machine Learning with Python: A Practical Introduction
by Saeed Aghabozorgi- 0.0
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5 Weeks
We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests....
$99
Practical Machine Learning on H2O
by Darren Cook- 4.5
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Approx. 24 hours to complete
Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. Types Of Models...
Social and Economic Networks: Models and Analysis
by Matthew O. Jackson- 4.8
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Approx. 30 hours to complete
We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. 8: Heterogeneity in Strategic Models 3: Diffusion on Random Networks...
