Admissions Open for JANUARY Batch
Understand mathematical concepts behind language processing and embeddings.
Days : Tue & Thu
Duration : 3 Hours
Timings: 8 - 10 PM IST
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3 Hours
9 - 10 PM IST
Tue & Thu
Maths for
Natural Language Processing
Understand mathematical concepts behind language processing and embeddings.
Online Live Instructor-Led Learning
3 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Word embeddings and cosine similarity
Attention and context vectors
Get familier with
Probabilistic language models
Transformers for text representation
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners pursuing chatbots or text analytics. keen to master the mathematical foundations of natural language processing, covering topics such as vector semantics, probability in text, and sequence modeling for NLP-focused AI careers.
Prerequisites
Linear algebra and vector representation basics.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Linear algebra and vector representation basics.
Who Should Enroll?
This course is for learners pursuing chatbots or text analytics. keen to master the mathematical foundations of natural language processing, covering topics such as vector semantics, probability in text, and sequence modeling for NLP-focused AI careers.
By end of this course
Get Stronger in
- Word embeddings and cosine similarity
- Attention and context vectors
Get Familiar in
- Probabilistic language models
- Transformers for text representation
Course Contents
Topics
- Distributional semantics and vector space models
- Word2Vec skip gram and CBOW math
- Negative sampling derivation
Key Outcomes
Understand embeddings mathematically
Topics
- Attention score computation
- Scaled dot-product derivation
- Softmax temperature and scaling
- Multi-head attention formulation
Key Outcomes
Build and reason about Transformer attention
Topics
- RNN/LSTM forward and backward propagation math
- Gradient issues (vanishing/exploding)
- Positional encoding math
Key Outcomes
Understand the math behind sequence models
Topics
- Distributional semantics and vector space models
- Word2Vec skip gram and CBOW math
- Negative sampling derivation
Key Outcomes
Understand embeddings mathematically
Topics
- Attention score computation
- Scaled dot-product derivation
- Softmax temperature and scaling
- Multi-head attention formulation
Key Outcomes
Build and reason about Transformer attention
Topics
- RNN/LSTM forward and backward propagation math
- Gradient issues (vanishing/exploding)
- Positional encoding math
Key Outcomes
Understand the math behind sequence models