Hands-on NLP with NLTK and Scikit-learn
A complete Python guide to Natural Language Processing to build spam filters, topic classifiers, and sentiment analyzers
Ratings 3.65 / 5.00
What You Will Learn!
- Build end-to-end Natural Language Processing solutions, ranging from getting data for your model to presenting its results.
- Core NLP concepts such as tokenization, stemming, and stop word removal.
- Use open source libraries such as NLTK, scikit-learn, and spaCy to perform routine NLP tasks.
- Classify emails as spam or not-spam using basic NLP techniques and simple machine learning models.
- Put documents in their relevant topics using techniques such as TF-IDF, SVMs, and LDAs.
- Common text data processing steps to increase the performance of your machine learning models.
Description
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.
This course uses Python 3.6, TensorFlow 1.4, NLTK 2, and scikit-learn 0.19, while not the latest version available, it provides relevant and informative content for legacy users of NLP with NLTK and Scikit-learn.
About the Author
Colibri Ltd is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.
Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feedback into how our AI generates content.
Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first-hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with High Dimension. IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.
In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which to learn deeply about reinforcement learning and supervised learning topics in a commercial setting.
Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.
Who Should Attend!
- This course is for developers, data scientists, and programmers who want to learn about practical Natural Language Processing with Python in a hands-on way. Developers who have an upcoming project that needs NLP, or a pile of unstructured text data on their hands, and don't know what to do with it, will find this course useful.
