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Related lectures (32)

Page 3 of 4

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Predicting New Product Life Cycles: Machine Learning Approach

Explores machine learning for predicting new product life cycles and the challenges of limited historical data.

Handling Data: Data Models and Wrangling

Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.

NABEL Data Analysis

Covers the NABEL Data Analysis Assignment, emphasizing data analysis skills and report formatting for air pollution concentrations and meteorology.

Data Handling: Problems and Distributions

Covers common data problems and important distributions, along with correlation and dependencies analysis.

Data Characterization: PCA & Spike Sorting

Explores PCA for data simplification and Spike Sorting for shape identification.

Regulations: Figures of Regulations

Covers the analysis of ECG heart rate data and respiratory flow measurements using Excel.

Big Data Best Practices and Guidelines

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

Data Wrangling with Hadoop: Advanced Techniques

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

Observation Methods in Social Sciences

Covers observation methods in social sciences, emphasizing direct and indirect observation, participant observation, and multi-situated ethnography.