Lectures related to Scaling to Massive Data: Spark Fundamentals

Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

Explores the challenges of big data processing and introduces Spark as a solution.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Logistic Regression: Vegetation Prediction

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Linear Models for Classification: Multi-Class Extensions

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

Transformations of Input or Output

Covers handling missing data, feature engineering, and output transformations in machine learning.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Logistic Regression: Probabilistic Interpretation

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Feature Engineering: Polynomial Regression

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Supervised Learning Essentials

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Multiclass Classification

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.