Word Embeddings: Context and Representation

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Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

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Word Embedding Models: Optimization and Applications

Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.

Word Embeddings: Modeling Word Context and Similarity

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Word Embeddings: Models and Learning

Explores word embeddings, context importance, and learning algorithms for creating new representations.

Gradient Descent and Linear Regression

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Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

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Logistic Regression: Classification

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