NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
Machine Learning
| Intermediate
- 11 Videos | 40m
- Includes Assessment
- Earns a Badge
This 11-video course explores NLP (natural language processing) by discussing differences between stemming, a process of reducing a word to its word stem, and lemmatization, or returning the base or dictionary form of a word. Key concepts covered here include how to extract synonyms, antonyms, and topic, and how to process and analyze texts for machine learning. You will learn to use Apache's Natural Language Toolkit (NLTK), spaCy, and Scikit-learn to implement text classification and sentiment analysis. This course demonstrates the use of advanced calculus and discrete optimization to implement robust and high-performance machine learning applications. You will learn to use R and Python to implement multivariate calculus for machine learning and data science, then examine the role of probability, variance, and random vectors in implementing machine learning processes and algorithms. Finally, you will examine the role of calculus in deep learning; watch a demonstration of how to apply calculus and differentiation using R and Python libraries; see how to implement calculus, derivatives, and integrals using Python; and learn uses of limits and series expansions in Python.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedemonstrate stemming and lemmatization scenarios in NLP using NLTKextract synonyms and antonyms from NLTK WordNet using Pythondemonstrate the steps involved in extracting topics using LDAdescribe NER, its use cases, and the standard libraries that use NERdescribe the concept of POS tagging, its importance in the context of NLP and the various implementations in NLTK
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recognize the essential features provided by spaCy for NLPanalyze and process texts using spaCyimplement TF and TF-IDF text classification using Python, scikit-learn, and NLTKimplement sentiment analysis using Python and scikit-learnrecall the differences between stemming and lemmatization, list the prominent features of spaCy, and implement sentiment analysis using Python and scikit-learn
IN THIS COURSE
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1.Course Overview1m 30sUP NEXT
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2.Stemming and Lemmatization3m 16s
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3.Synonyms and Antonyms with NLTK2m 57s
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4.Topic Extraction with LDA3m 1s
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5.NER and Standard Libraries4m 7s
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6.POS Tagging and NLTK Implementations4m 32s
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7.spaCy Framework2m 49s
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8.Analyzing and Processing Texts4m 18s
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9.Text Classification Using scikit-learn4m 53s
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10.Sentiment Analysis4m 15s
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11.Exercise: Sentiment Analysis with scikit-learn4m 23s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform
Digital badges are yours to keep, forever.