NLP for Social Media Analytics

Text Classification Basics with Python

Learn text classification basics with Python to process and categorize social media data efficiently. Python, a versatile programming language, offers a wide array of libraries and frameworks to simplify text classification. This course covers the foundational steps of data preprocessing, tokenization, and feature extraction.

Participants will also explore libraries such as NLTK and spaCy, which are essential for preparing and analyzing text data for classification tasks.

Document Classification with Natural Language Processing (NLP)

Discover document classification with natural language processing (NLP) to handle social media data at scale. This course delves into techniques like bag-of-words, TF-IDF, and word embeddings for extracting meaningful features from text. Learners will explore real-world applications like spam detection, topic categorization, and content moderation.

With a focus on practical implementation, learners will understand how to leverage NLP to improve classification models’ accuracy and efficiency.

Building Classification Models with Scikit-learn

Learn building classification models with Scikit-learn to apply machine learning algorithms to social media analytics. Scikit-learn, a popular Python library, simplifies the implementation of classification models like logistic regression, decision trees, and support vector machines.

This course walks learners through the end-to-end pipeline of training, validating, and testing classification models using Scikit-learn, ensuring they are equipped to handle diverse datasets and challenges.

Deep Learning for Document Classification

Master deep learning for document classification to process complex social media text data. This course introduces neural networks and deep learning techniques for building powerful classification models. Topics include recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers for text classification.

By learning deep learning approaches, participants can create robust models that adapt to nuanced and context-dependent social media data.

Document Classification Using BERT and Transformers

Learn document classification using BERT and transformers, advanced NLP models that have revolutionized the field. This course focuses on leveraging BERT (Bidirectional Encoder Representations from Transformers) and other transformer-based models for handling large and complex datasets.

Learners will explore the fine-tuning process for pre-trained transformer models and their application to social media analytics, making this a must-have skill for professionals in this domain.

Multiclass Document Classification Techniques

Learn multiclass document classification techniques to tackle real-world challenges in social media analytics. This course equips learners to categorize social media content into multiple classes effectively. Topics include evaluating multiclass classification models, handling imbalanced datasets, and optimizing performance metrics.

Participants will gain the skills to implement scalable multiclass classification systems for diverse applications, from sentiment analysis to trend detection.

Text Preprocessing for Classification Tasks

Learn text preprocessing for classification tasks to prepare social media data for effective analysis. This course focuses on essential preprocessing steps such as cleaning data, tokenization, stemming, and lemmatization. These processes ensure the data is structured and meaningful for building accurate classification models.

This foundational knowledge is crucial for learners looking to excel in social media analytics and NLP with Edcroma.