Scaling to Massive Data: Spark Fundamentals

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Related lectures (52)

Supervised Learning in Asset Pricing Log in to Mediaspace to watch this video

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Linear Models: Continued Log in to Mediaspace to watch this video

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Linear and Logistic Regression Log in to Mediaspace to watch this video

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

Linear Classification: Logistic Regression Log in to Mediaspace to watch this video

Covers linear classification using logistic regression, regularization, and multiclass classification.

Logistic Regression: Cost Functions & Optimization Log in to Mediaspace to watch this video

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Linear Regression: Basics and Gradient Descent Log in to Mediaspace to watch this video

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Linear Models: Classification Basics Log in to Mediaspace to watch this video

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Logistic Regression: Probability Modeling Log in to Mediaspace to watch this video

Covers logistic regression for binary classification using probability modeling and optimization methods.

Feature Engineering: Missing Data and Standardization Log in to Mediaspace to watch this video

Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.

Support Vector Machines: Basics and Applications Log in to Mediaspace to watch this video

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Untitled Log in to Mediaspace to watch this video

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Untitled Log in to Mediaspace to watch this video

Generalized Linear Models: A Brief Review Log in to Mediaspace to watch this video

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation Log in to Mediaspace to watch this video

Explores Maximum Likelihood Estimation, linear models, logistic regression, and Support Vector Machines.

Machine Learning: Supervised and Unsupervised Learning Techniques Log in to Mediaspace to watch this video

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Boltzmann Machine Log in to Mediaspace to watch this video

Covers the Boltzmann Machine, a type of stochastic recurrent neural network.

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