- Explain the definition of an NLP.
- Describe the process of tokenizing.
- Identify the purpose of vectorizing.
- Recognize the outcomes of lemmatizing.
- Summarize the characteristics of TF-IDF.
- Define accuracy in terms of evaluation metrics.
- Recall three benefits of using ensemble methods.
Skill Level Intermediate
- [Derek] Welcome to Natural Language Processing with Python for Machine Learning Essential Training. I'm Derek Jedamski. I'm a senior data scientist with a passion for natural language processing. Have you ever wondered how your email filters out spam messages? Or maybe how autocorrect on your phone knows what you're trying to type? In this course, we'll cover some basics of natural language processing like reading in and creating structure in messy text data, and then cleaning and tokenizing that data. Then we'll cover some of the more advanced topics like lemmatizing, stemming, and vectorizing your data.
In other words, converting it from text into a numeric matrix. We'll do this with a focus on preparing our data to build a machine learning classifier on top of it. We'll learn how to build two different types of machine learning models, while thoroughly testing and evaluating different variations of those models. You'll have the tools to go from messy dataset to concise and accurate predictions from machine learning model, to deliver solutions to complex business problems. So let's start our learning journey.
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
1. NLP Basics
2. Supplemental Data Cleaning
3. Vectorizing Raw Data
4. Feature Engineering
5. Building Machine Learning Classifiers
Next steps1m 10s
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