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Lecture
Introduction to Data Science
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Related lectures (29)
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.
Linear Regression: Fundamentals and Applications
Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.
Introduction to Machine Learning: Course Overview and Basics
Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Supervised Learning Fundamentals
Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Linear Regression and Logistic Regression
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Linear Regression: Statistical Inference and Regularization
Covers the probabilistic model for linear regression and the importance of regularization techniques.
Linear Regression: Basics and Estimation
Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Regression: High Dimensions
Explores linear regression in high dimensions and practical house price prediction from a dataset.
Introduction to Machine Learning: Regression Fundamentals
Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.
Probability and Statistics
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
Linear Regression: Foundations and Applications
Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.
Introduction to Machine Learning
Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.
Supervised Learning: Likelihood Maximization
Covers supervised learning through likelihood maximization to find optimal parameters.
Machine Learning Basics
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Linear Regression: Theory and Applications
Covers the theory and practical applications of linear regression.
Regression: Linear Models
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Statistics: Exploratory Data Analysis
Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.
Feature Engineering: Polynomial Regression
Covers fitting linear regression on features of the original predictors for flexible feature representation.
Linear Regression: Introduction and Training
Covers linear regression training to find the best line for given data points, essential for predicting house prices.
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