Packtpub – Hands-on NLP with NLTK and Scikit-learn

Packtpub – Hands-on NLP with NLTK and Scikit-learn
English | Size: 716.44 MB
Category: Programming | E-learning | others

There is an overflow of text data online nowadays. As a Python developer, you need to create a new solution using Natural Language Processing for your next project. Your colleagues depend on you to monetize gigabytes of unstructured text data. What do you do?
Hands-on NLP with NLTK and scikit-learn is the answer. This course puts you right on the spot, starting off with building a spam classifier in our first video. At the end of the course, you are going to walk away with three NLP applications: a spam filter, a topic classifier, and a sentiment analyzer. There is no need for fancy mathematical theory, just plain English explanations of core NLP concepts and how to apply those using Python libraries.Taking this course will help you to precisely create new applications with Python and NLP. You will be able to build actual solutions backed by machine learning and NLP processing models with ease. [Read more…]

Packt – Hands-on Scikit-learn for Machine Learning [Video]

Packt – Hands-on Scikit-learn for Machine Learning [Video]
English | Size: 1.59 GB
Category: CBTs

Scikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project’s life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia.
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Packt Publiching – Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Packt Publiching – Advanced Predictive Techniques with Scikit-Learn and TensorFlow
English | Size: 910.35 MB
Category: Tutorial

This course presents some of the most advanced Predictive Analytics tools, models, and techniques currently having a big impact on every industry. The main goal is to show the viewer how to improve the performance of predictive models-firstly, by showing how to build more complex models and secondly, by showing how to use related techniques that dramatically improve the quality of predictive models.
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