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Volatility Trading Analysis with Python
by Diego Fernandez- 3.3
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6 hours on-demand video
Calculate forecasted volatility through seasonal random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models. After that, you’ll use these estimations to forecast volatility through seasonal random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models....
$9.99
Modeling Risk and Realities
by Sergei Savin , Senthil Veeraraghavan- 4.6
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Approx. 7 hours to complete
Useful quantitative models help you to make informed decisions both in situations in which the factors affecting your decision are clear, as well as in situations in which some important factors are not clear at all. 2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values...
Data Science Projects with Python
by Packt Publishing- 4.2
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6 hours on-demand video
You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package....
$9.99
I/O-efficient algorithms
by Mark de Berg- 4.6
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Approx. 10 hours to complete
- Basic probability theory: events, probability distributions, random variables, expected values etc....
All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]
by Rishi Bansal- 0.0
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17.5 hours on-demand video
- Saving and Loading ML Models - Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method - Bagging - Random Forest (Regression and Classification)...
$12.99
Decision Trees, Random Forests, AdaBoost & XGBoost in Python
by Start-Tech Academy- 4.2
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7 hours on-demand video
How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right? Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning....
$11.99
Species Distribution Models with GIS & Machine Learning in R
by Minerva Singh- 4.2
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3.5 hours on-demand video
Do you want to implement practical machine learning models in R? In this course, actual spatial data from Peninsular Malaysia will be used to give a practical hands-on experience of working with real life spatial data for mapping habitat suitability in conjunction with classical SDM models like MaxEnt and machine learning alternatives such as Random Forests....
$12.99
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Regression Analysis for Statistics & Machine Learning in R
by Minerva Singh- 4.2
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7.5 hours on-demand video
This course is based on my years of regression modelling experience and implementing different regression models on real life data. Evaluate regression model accuracyImplement generalized linear models (GLMs) such as logistic regression and Poisson regression. Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data....
$12.99
Supervised Learning: Classification
by Mark J Grover , Miguel Maldonado- 4.9
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Approx. 11 hours to complete
You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models Implementing Support Vector Machines Kernel Models Random Forest Modeling Approaches: Random and Synthetic Oversampling...
Supervised Machine Learning: Classification
by Mark J Grover , Miguel Maldonado- 4.9
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Approx. 11 hours to complete
You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models Implementing Support Vector Machines Kernel Models Random Forest Modeling Approaches: Random and Synthetic Oversampling...
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