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

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 Apaches 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.