Natural Language Processing - Machine Learning Models in Python: Mastering Text Analytics: From Spam Detection to Topic Modeling [Video]

Name: Natural Language Processing - Machine Learning Models in Python [Video]
Brand: Packt
SKU: 9781836205579
Price: 2256.99 MXN
Availability: InStock

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Video Jun 2024 5 hours 11 minutes 1st Edition

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Key benefits

Spam detection, sentiment analysis, text summarization, topic modeling, and latent semantic analysis.
Hands-on coding sessions in Python to apply theoretical knowledge.
In-depth exploration of model evaluation metrics and advanced algorithms.

Description

Embark on a journey through the world of text analytics with our expertly crafted course. Starting with an introduction and outline, you'll discover the foundational concepts and receive a special offer to get you started. The initial sections guide you through the setup process, ensuring you have all the resources and tips needed for success. Delve into the core of text analytics with dedicated sections on spam detection, sentiment analysis, text summarization, topic modeling, and latent semantic analysis. Each section begins with a problem description, followed by intuitive explanations of algorithms like Naive Bayes, logistic regression, and Latent Dirichlet Allocation. You'll engage with practical exercises designed to reinforce your understanding, and apply these techniques using Python in a hands-on manner. The course culminates with advanced topics and comprehensive summaries, ensuring you grasp both the theoretical and practical aspects of text analytics. By the end, you'll have a robust understanding of various NLP techniques and the confidence to apply them in real-world scenarios. This course is an essential resource for technical professionals looking to excel in the rapidly evolving field of natural language processing.

What you will learn

Develop spam detection models using Naive Bayes in Python. Apply logistic regression for sentiment analysis of text data. Implement text summarization techniques, including TextRank and vector-based methods. Perform topic modeling with LDA and NMF in Python. Understand and apply latent semantic analysis using SVD. Evaluate and improve model performance using metrics like ROC, AUC, and F1 scores.