Duration:
100 hours
 
Project Overview:
Enhance your NLP skills by building a text classification model. This project involves processing textual data, extracting meaningful features, and training machine learning models to categorize text effectively.
 
Key Learning Outcomes:
Text Preprocessing: Clean and prepare text data using techniques like tokenization, lemmatization, and stopword removal.
Feature Engineering: Extract features using Bag of Words, TF-IDF, and word embeddings.
Machine Learning Models: Train classifiers such as Logistic Regression, SVM, and Neural Networks for text categorization.
Model Evaluation & Optimization: Use metrics like accuracy, F1 score, and precision to evaluate models and optimize hyperparameters.
Data Visualization: Visualize text data distributions and model performance for better insights.
 
Tools & Libraries:
Programming Language: Python
Libraries: pandas, NLTK, SpaCy, scikit-learn, TensorFlow, Keras, matplotlib, seaborn
 
Deliverables:
Data Preprocessing Notebook: Documentation of text cleaning and preparation steps.
Feature Engineering Notebook: Details of feature extraction methods applied.
Model Training Notebook: Code for training and evaluating text classification models.
Final Report: Summary of findings, model performances, and actionable insights.
 
Who Should Enroll:
Aspiring data scientists interested in natural language processing.
Individuals looking to build skills in text analysis and machine learning.
Professionals aiming to apply NLP techniques in industries like marketing, healthcare, and technology.
 
Why This Project? Text classification is fundamental in numerous applications, from sentiment analysis to spam detection. This project equips you with the skills to handle and categorize textual data effectively, making you proficient in one of the most sought-after areas in data science.