Admission Open

Natural Language Processing Course in Mianwali

Module 1: Introduction to NLP

Week 1: Overview of NLP

Definition and History of NLP
Applications of NLP
NLP vs. Computational Linguistics
Key Challenges in NLP
Week 2: Text Processing and Understanding

Text Preprocessing: Tokenization, Lemmatization, Stemming
Stop Words Removal
Text Normalization
Regular Expressions in NLP
Module 2: NLP Core Techniques

Week 3: Linguistic Fundamentals for NLP

Morphology: Analyzing Word Structures
Syntax: Sentence Structure Analysis
Semantics: Meaning Representation
Pragmatics: Contextual Language Understanding
Week 4: Feature Extraction

Bag-of-Words Model
TF-IDF (Term Frequency-Inverse Document Frequency)
Word Embeddings: Word2Vec, GloVe, FastText
Sentence Embeddings
Module 3: Machine Learning for NLP

Week 5: Classical Machine Learning Approaches

Supervised Learning: Naive Bayes, SVM, Decision Trees
Unsupervised Learning: K-Means, Hierarchical Clustering
Evaluation Metrics: Precision, Recall, F1-Score
Week 6: Advanced Machine Learning Techniques

Ensemble Methods: Random Forest, Gradient Boosting
Feature Selection and Dimensionality Reduction
Model Evaluation and Hyperparameter Tuning
Module 4: Deep Learning for NLP

Week 7: Introduction to Deep Learning in NLP

Basics of Neural Networks
Feedforward Neural Networks
Introduction to Recurrent Neural Networks (RNNs)
Week 8: Advanced Deep Learning Models

Long Short-Term Memory Networks (LSTMs)
Gated Recurrent Units (GRUs)
Convolutional Neural Networks (CNNs) in NLP
Week 9: Transformers and Attention Mechanisms

Attention Mechanisms
Transformers Architecture
BERT, GPT, and Other Transformer Models
Module 5: NLP Tasks and Applications

Week 10: Text Classification and Sentiment Analysis

Sentiment Analysis Techniques
Spam Detection
Topic Modeling: LDA, NMF
Week 11: Named Entity Recognition (NER) and Information Extraction

NER Techniques
Relationship Extraction
Knowledge Graphs
Week 12: Machine Translation and Summarization

Machine Translation Techniques
Summarization Methods
Applications and Case Studies

Admission Open for this course
Contact Number: 03307615544

Leave a Reply

Your email address will not be published. Required fields are marked *