Scaling up: Spark and Big Data

Related lectures (31)

Page 3 of 4

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

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.

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

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

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

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

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Covers the fundamentals of data science, the scientific method evolution, the role of a data scientist, and the significance of data as the new oil.

Covers the principles of data stream processing and its applications in real-time data analysis.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.