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

Machine Learning    |    Intermediate
  • 11 videos | 40m
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 47 users Rating 4.3 of 47 users (47)
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

  • 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

IN THIS COURSE

  • 1m 30s
  • 3m 16s
    Learn about stemming and lemmatization scenarios in NLP using NLTK. FREE ACCESS
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    3.  Synonyms and Antonyms with NLTK
    2m 57s
    Learn how to extract synonyms and antonyms from NLTK WordNet using Python. FREE ACCESS
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    4.  Topic Extraction with LDA
    3m 1s
    To apply the steps involved in extracting topics using LDA, please consult the following resource. FREE ACCESS
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    5.  NER and Standard Libraries
    4m 7s
    After completing this video, you will be able to describe NER, its use cases, and the standard libraries that use NER. FREE ACCESS
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    6.  POS Tagging and NLTK Implementations
    4m 32s
    Upon completion of this video, you will be able to describe the concept of POS tagging, its importance in the context of NLP, and the various implementations in NLTK. FREE ACCESS
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    7.  spaCy Framework
    2m 49s
    After completing this video, you will be able to recognize the essential features provided by spaCy for natural language processing. FREE ACCESS
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    8.  Analyzing and Processing Texts
    4m 18s
    In this video, you will learn how to analyze and process texts using spaCy. FREE ACCESS
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    9.  Text Classification Using scikit-learn
    4m 53s
    In this video, you will learn how to implement TF and TF-IDF text classification using Python, scikit-learn, and NLTK. FREE ACCESS
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    10.  Sentiment Analysis
    4m 15s
    During this video, you will learn how to implement sentiment analysis using Python and the scikit-learn library. FREE ACCESS
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    11.  Exercise: Sentiment Analysis with scikit-learn
    4m 23s
    After completing this video, you will be able to recall the differences between stemming and lemmatization, list the prominent features of spaCy, and implement sentiment analysis using Python and scikit-learn. FREE ACCESS

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