Text Mining and Analytics: Machine Learning for Natural Language Processing

Natural Language Processing    |    Intermediate
  • 13 Videos | 2h 2m 41s
  • Includes Assessment
  • Earns a Badge
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Machine learning (ML) is one of the most important toolsets available in the enterprise world. It gives predictive powers to data that can be leveraged to investigate future behaviors and patterns. It can help companies proactively improve their business and help optimize their revenue. Learn how to leverage machine learning to make predictions with language data. Explore the ML pipelines and common models used for Natural Language Processing (NLP). Examine a real-world use case of identifying sarcasm in text and discover the machine learning techniques suitable for NLP problems. Learn different vectorization and feature engineering methods for text data, exploratory data analysis for text, model building, and evaluation for predicting from text data and how to tune those models to achieve better results. After completing this course, you'll be able to illustrate the use of machine learning to solve NLP problems and demonstrate the use of NLP feature engineering.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recognize key concepts of NLP with ML
    describe end-to-end components for NLP problems
    illustrate the use of one-hot encoding, bag-of-words, n-gram, and TFIDF
    restate logistic regression, support vector machine (SVM), Naive Bayes, and boosting models
    demonstrate data loading and a basic overview of columns
    perform Exploratory Data Analysis (EDA) of data
  • perform an exploration of linguistic features in data
    demonstrate feature engineering on data
    demonstrate simple model building and evaluation using the Decision Tree classifier, logistic regression, and SVM
    demonstrate simple model building and evaluation using the Random Forest Classifier, Naïve Bayes, and KNN and compare the results of all the models
    perform model tuning for better results and evaluation using different search methods
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 55s
    UP NEXT
  • Playable
    2. 
    NLP with Machine Learning (ML)
    7m 38s
  • Locked
    3. 
    Machine Learning Pipeline for NLP
    6m 46s
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    4. 
    Feature Engineering for NLP
    5m 12s
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    5. 
    Common ML Models Used in NLP
    5m 17s
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    6. 
    Predicting Sarcasm in Text: Data Loading
    15m 54s
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    7. 
    Predicting Sarcasm in Text: Data Analysis
    13m 56s
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    8. 
    Predicting Sarcasm in Text: Linguistic Features
    5m 35s
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    9. 
    Predicting Sarcasm in Text: Feature Engineering
    17m
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    10. 
    Predicting Sarcasm in Text: Model Building Part 1
    14m 39s
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    11. 
    Predicting Sarcasm in Text: Model Building Part 2
    12m 20s
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    12. 
    Predicting Sarcasm in Text: Model Tuning
    15m 40s
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    13. 
    Course Summary
    50s

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