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

  • 1m 55s
  • 7m 38s
    After completing this video, you will be able to recognize key concepts of ML with NLP. FREE ACCESS
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    3.  Machine Learning Pipeline for NLP
    6m 46s
    After completing this video, you will be able to describe the end-to-end components for NLP problems. FREE ACCESS
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    4.  Feature Engineering for NLP
    5m 12s
    Upon completion of this video, you will be able to illustrate the use of one-hot encoding, bag-of-words, n-gram, and TFIDF. FREE ACCESS
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    5.  Common ML Models Used in NLP
    5m 17s
    During this video, you will learn how to restate logistic regression, support vector machine (SVM), Naive Bayes, and boosting models. FREE ACCESS
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    6.  Predicting Sarcasm in Text: Data Loading
    15m 54s
    In this video, you will learn how to load data and get a basic overview of the columns. FREE ACCESS
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    7.  Predicting Sarcasm in Text: Data Analysis
    13m 56s
    In this video, you will perform exploratory data analysis (EDA) on data. FREE ACCESS
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    8.  Predicting Sarcasm in Text: Linguistic Features
    5m 35s
    In this video, learn how to explore linguistic features in data. FREE ACCESS
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    9.  Predicting Sarcasm in Text: Feature Engineering
    17m
    In this video, you will learn how to perform feature engineering on data. FREE ACCESS
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    10.  Predicting Sarcasm in Text: Model Building Part 1
    14m 39s
    In this video, you will learn how to apply simple model building and evaluation using the Decision Tree classifier, logistic regression, and support vector machines. FREE ACCESS
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    11.  Predicting Sarcasm in Text: Model Building Part 2
    12m 20s
    In this video, find out how to apply simple model building and evaluation using the Random Forest Classifier, Naive Bayes, and KNN. Compare the results of all the models. FREE ACCESS
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    12.  Predicting Sarcasm in Text: Model Tuning
    15m 40s
    In this video, find out how to perform model tuning for better results and evaluation using different search methods. FREE ACCESS
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    13.  Course Summary
    50s

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