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Practical Time Series Analysis
by Tural Sadigov , William Thistleton- 4.6
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Approx. 26 hours to complete
We look at several mathematical models that might be used to describe the processes which generate these types of data. Random Walk Random Walk vs Purely Random Process Time plots, Stationarity, ACV, ACF, Random Walk and MA processes White Noise and Random Walks ARMA Models and a Little Theory ACF of SARIMA models...
Ensemble Machine Learning in Python: Random Forest, AdaBoost
by Lazy Programmer Team- 4.6
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5.5 hours on-demand video
We've already learned some classic machine learning models like k-nearest neighbor and decision tree. But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor? In particular, we will study the Random Forest and AdaBoost algorithms in detail. Simple machine learning models like linear regression and decision trees...
$29.99
Natural Language Processing - Basic to Advance using Python
by Shiv Onkar Deepak Kumar- 3.3
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7 hours on-demand video
Classifications using Random Forest, Naive Bayes and XgBoost How to know models are good enough Bias vs Variance Classifications using Random Forest, Naive Bayes and XgBoost How to know models are good enough Bias vs Variance...
$9.99
Supervised Machine Learning for beginners
by Ro Science- 4.7
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5 hours on-demand video
And last, we actually train and evaluate several models based on the most common machine learning algorithms for supervised learning such as K-nearest neighbors, logistic regression, decision trees and random forests....
$9.99
Mathematical Methods for Quantitative Finance
by Paul F. Mende , Egor Matveyev- 0.0
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12 Weeks
Time-series models: random walks, ARMA, and GARCH...
$450
Bayesian Statistics: Mixture Models
by Abel Rodriguez- 4.7
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Approx. 22 hours to complete
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. Definition of Mixture Models Likelihood function for mixture models Maximum likelihood estimation for Mixture Models Bayesian estimation for Mixture Models Applications of Mixture Models Density estimation using Mixture Models Mixture Models for Clustering Mixture Models and naive Bayes classifiers...
Unsupervised Machine Learning Hidden Markov Models in Python
by Lazy Programmer Team- 4.6
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9.5 hours on-demand video
This course follows directly from my first course in Unsupervised Machine Learning for Cluster Analysis, where you learned how to measure the probability distribution of a random variable. In this course, you’ll learn to measure the probability distribution of a sequence of random variables. Understand and enumerate the various applications of Markov Models and Hidden Markov Models...
$29.99
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Volatility Trading Analysis with R
by Diego Fernandez- 4.6
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6 hours on-demand video
Approximate options call and put prices through Black and Scholes, binomial trees models together with related option Greeks. 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
How to Win a Data Science Competition: Learn from Top Kagglers
by Dmitry Ulyanov , Alexander Guschin , Mikhail Trofimov , Dmitry Altukhov , Marios Michailidis- 4.7
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Approx. 54 hours to complete
- Master the art of combining different machine learning models and learn how to ensemble. - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM. - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks. Validation schemes for 2-nd level models...
AI Workflow: Machine Learning, Visual Recognition and NLP
by Mark J Grover , Ray Lopez, Ph.D.- 4.5
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Approx. 14 hours to complete
Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Employ evaluation metrics to select models for production use Linear Models Generalized Linear Models...
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