Introduction to Data Science

Related lectures (29)

Page 2 of 3

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Covers supervised learning through likelihood maximization to find optimal parameters.

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

Covers the theory and practical applications of linear regression.

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

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

Covers linear regression training to find the best line for given data points, essential for predicting house prices.