Overview/Description 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.

NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn

discover the key concepts covered in this course

demonstrate stemming and lemmatization scenarios in NLP using NLTK

extract synonyms and antonyms from NLTK WordNet using Python

demonstrate the steps involved in extracting topics using LDA

describe NER, its use cases, and the standard libraries that use NER

describe the concept of POS tagging, its importance in the context of NLP and the various implementations in NLTK

recognize the essential features provided by spaCy for NLP

analyze and process texts using spaCy

implement TF and TF-IDF text classification using Python, scikit-learn, and NLTK

implement sentiment analysis using Python and scikit-learn

recall the differences between stemming and lemmatization, list the prominent features of spaCy, and implement sentiment analysis using Python and scikit-learn