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.
Target Audience:
Duration: 00:41
Description:
This course explores how natural language processing (NLP) is used for machine learning, and examines the benefits and challenges of NLP when creating an application that can essentially understand human language. In its 13 videos, learners will be shown the essential components of NLP, including parsers, corpus, and corpus linguistic, as well as how to implement regular expressions. This course goes on to examine tokenization, a way to separate a piece of text into smaller units, and then illustrates different tokenization use cases with NLTK (Natural Language Toolkit). You will learn to use stop words using libraries and the NLTK. This course demonstrates how to implement regular expressions in Python to build NLP-powered applications. Learners will examine the list of Python NLP libraries along with their essential capabilities, including NLTK, Gensim, CoreNLP, spaCy and PyNLPl. You will learn to set up and configure an NLTK environment to illustrate how to process raw text. Finally, this course demonstrates the use of filtering stopwords in a tokenized sentence using NLTK.
Target Audience:
Duration: 01:03