Hands-on NLP with NLTK and Scikit-learn

Hands-on NLP with NLTK and Scikit-learn, available at $19.99, has an average rating of 3.5, with 30 lectures, based on 18 reviews, and has 121 subscribers.

You will learn about 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. This course is ideal for individuals who are 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. It is particularly useful for 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.

Enroll now: Hands-on NLP with NLTK and Scikit-learn

Summary

Title: Hands-on NLP with NLTK and Scikit-learn

Price: $19.99

Average Rating: 3.5

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $109.99

Quality Status: approved

Status: Live

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.

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: Working with Natural Language Data

Lecture 1: The Course Overview

Lecture 2: Use Python, NLTK, spaCy, and Scikit-learn to Build Your NLP Toolset

Lecture 3: Reading a Simple Natural Language File into Memory

Lecture 4: Split the Text into Individual Words with Regular Expression

Lecture 5: Converting Words into Lists of Lower Case Tokens

Lecture 6: Removing Uncommon Words and Stop Words

Chapter 2: Spam Classification with an Email Dataset

Lecture 1: Use an Open Source Dataset, and What Is the Enron Dataset

Lecture 2: Loading the Enron Dataset into Memory

Lecture 3: Tokenization, Lemmatization, and Stop Word Removal

Lecture 4: Bag-of-Words Feature Extraction Process with Scikit-learn

Lecture 5: Basic Spam Classification with NLTK's Naive Bayes

Chapter 3: Sentiment Analysis with a Movie Review Dataset

Lecture 1: Understanding the Origin and Features of the Movie Review Dataset

Lecture 2: Loading and Cleaning the Review Data

Lecture 3: Preprocessing the Dataset to Remove Unwanted Words and Characters

Lecture 4: Creating TF-IDF Weighted Natural Language Features

Lecture 5: Basic Sentiment Analysis with Logistic Regression Model

Chapter 4: Boosting the Performance of Your Models with N-grams

Lecture 1: Deep Dive into Raw Tokens from the Movie Reviews

Lecture 2: Advanced Cleaning of Tokens Using Python String Functions and Regex

Lecture 3: Creating N-gram Features Using Scikit-learn

Lecture 4: Experimenting with Advanced Scikit-learn Models Using the NLTK Wrapper

Lecture 5: Building a Voting Model with Scikit-learn

Chapter 5: Document Classification with a Newsgroup Dataset

Lecture 1: Understanding the Origin and Features of the 20 Newsgroups Dataset

Lecture 2: Loading the Newsgroup Data and Extracting Features

Lecture 3: Building a Document Classification Pipeline

Lecture 4: Creating a Performance Report of the Model on the Test Set

Lecture 5: Finding Optimal Hyper-parameters Using Grid Search

Chapter 6: Advanced Topic Modelling with TF-IDF, LSA, and SVMs

Lecture 1: Building a Text Preprocessing Pipeline with NLTK

Lecture 2: Creating Hashing Based Features from Natural Language

Lecture 3: Classify Documents into 20 Topics with LSA

Lecture 4: Document Classification with TF-IDF and SVMs

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes
2 stars: 2 votes
3 stars: 4 votes
4 stars: 3 votes
5 stars: 8 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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