Text Classification with Natural Language Processing | CodeSignal Learn

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Text Classification with Natural Language Processing

Artificial Intelligence

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4 courses

110 practices

21 hours

This NLP learning path is tailored for mastering text classification, spanning from data preparation to model optimization. It includes practice in data collection, feature engineering, diverse modeling techniques, and advanced optimization strategies.

4.54

952 learners

Earn a shareable

Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Feature Engineering and Text Representation

ADVANCED

Machine Learning Modeling for NLP

INTERMEDIATE

Text Data Collection and Preparation

Tools you'll use

NLTK

Pandas

Python

Scikit-learn

TensorFlow

Course 1

Collecting and Preparing Textual Data for Classification

5 lessons

25 practices

Learn how to collect and prepare specific textual datasets essential for your text classification project. You'll delve into the practices of gathering and cleaning text data, and explore advanced textual processing techniques.

Course 2

Feature Engineering for Text Classification

5 lessons

25 practices

Dive deeper into the transformation of raw text data into features that machine learning models can understand. Through a practical, hands-on approach, you'll learn everything from tokenization, generating Bag-of-Words and TF-IDF representations, to handling sparse features and applying Dimensionality Reduction techniques.

Course 3

Introduction to Modeling Techniques for Text Classification

6 lessons

30 practices

This course paves the way for your journey in NLP modeling. Delve deep into the world of text classification algorithms starting with Naive Bayes, Support Vector Machines, Decision Trees, and Random Forests. But that's not all! You'll also get familiar with stratified cross-validation, a key tool for handling imbalanced classes in text data.

Course 4

Advanced Modeling for Text Classification

6 lessons

30 practices

Explore advanced text classification techniques, including ensemble methods and deep learning, to enhance model performance using Python and TensorFlow.

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