Document Retrieval and Classification

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Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

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

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.