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Master Machine Learning with Scikit-Learn Library & Python
by Piyush Dave- 5
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10.5 hours on-demand video
Learn Machine Learning Algorithms like Linear & Logistic Regression, SVM, KNN, KMean, NB, Decision Tree & Random Forest The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies using Scikit Learn library....
$9.99
Probabilistic Graphical Models 3: Learning
by Daphne Koller- 4.6
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Approx. 66 hours to complete
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. Learning in Parametric Models Learning Undirected Models Maximum Likelihood for Log-Linear Models Maximum Likelihood for Conditional Random Fields...
Build Decision Trees, SVMs, and Artificial Neural Networks
by Stacey McBrine- 0.0
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Approx. 22 hours to complete
As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow. Build Decision Trees and Random Forests Module Introduction Random Forest Random Forest Hyperparameters Guidelines for Building a Random Forest Model Building Decision Trees and Random Forests Guidelines for Building SVM Models for Classification Guidelines for Building SVM Models for Regression...
Machine Learning Fundamentals
by Sanjoy Dasgupta- 0.0
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10 Weeks
Or exploit data to create simple predictive models of the world? Generative and discriminative models Linear models and extensions to nonlinearity using kernel methods Ensemble methods: boosting, bagging, random forests...
$350
Linear Regression, GLMs and GAMs with R
by Geoffrey Hubona, Ph.D.- 4.1
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8 hours on-demand video
How to extend linear regression to specify and estimate generalized linear models and additive models. Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. Generalized linear models (GLMs) relax this assumption of linearity. Generalized additive models (GAMs) are extensions of GLMs....
$12.99
The Essential Guide to Stata
by F. Buscha- 4.4
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14 hours on-demand video
Binary outcome models (Logit and Probit) Fractional response models (Fractional Logit and Beta Regression) Categorical choice models (Ordered Logit and Multinomial Logit) Count data models (Poisson and Negative Binomial Regression) Panel data analysis (Long Form Data, Lags and Leads, Random and Fixed Effects, Hausman Test and Non-Linear Panel Regression)...
$14.99
Fundamentals of Quantitative Modeling
by Richard Waterman- 4.6
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Approx. 8 hours to complete
3 How Models Are Used in Practice Module 1: Introduction to Models Quiz Module 2: Linear Models and Optimization 1 Introduction to Linear Models and Optimization Module 2: Linear Models and Optimization Quiz Module 3: Probabilistic Models 1 Introduction to Probabilistic Models 2 Examples of Probabilistic Models 3 Regression Models 6 Markov Chain Models...
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AI for Medical Prognosis
by Pranav Rajpurkar , Bora Uyumazturk , Eddy Shyu- 4.7
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Approx. 30 hours to complete
Linear prognostic models Evaluating Prognostic Models Prognosis with Tree-based models Missing completely at random Missing at random Missing not at random Survival Models and Time Survival models Build a risk model using linear and tree-based models Apply tree-based models to estimate patient survival rates...
24h Pro data science in R
by Francisco Juretig- 3.6
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18.5 hours on-demand video
Using R's statistical functions for drawing random numbers, calculating densities, histograms, etc....
$12.99
Applied Data Science for Data Analysts
by Kevin Coyle , Mark Roepke , Emma Freeman- 4.2
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Approx. 16 hours to complete
Applied Tree-based Models Applied Random Forest Weighting Classes in Random Forest Random Forest Algorithm Applied Random Forest Lab Applied Random Forest Lab Results Hyperparameters in Tree-based Models Grid-search for Random Forests K-fold Cross-Validation with Random Forest Hyperparameters in Tree-based Models...
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