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Related lectures (32)

Page 3 of 4

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Fermented Foods: Microbial Transformations and Health Benefits

Explores the microbial transformations and health benefits of fermented foods, emphasizing their potential as delivery vehicles for probiotics to underserved communities.

Big Data: Processing and Dimensions

Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.

Data Science Fundamentals

Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.

Knowledge Representation: Semantics and Data Structures

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Digital Humanities: Defining Big Cultural Data Sets

Delves into Digital Humanities, focusing on the definition of Big Cultural Data Sets and the various dimensions of data bigness.

Logistic Regression: Vegetation Prediction

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

Digital Humanities: Big Cultural Data Sets

Explores the distinctions in Digital Humanities, focusing on Big Cultural Data Sets and the implications of data bigness.

Data Analysis to AI and ML, Social Media

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.