Text Mining and Analytics: Machine Learning for Natural Language Processing
Natural Language Processing
| Intermediate
- 13 Videos | 2h 2m 41s
- Includes Assessment
- Earns a Badge
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
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discover the key concepts covered in this courserecognize key concepts of NLP with MLdescribe end-to-end components for NLP problemsillustrate the use of one-hot encoding, bag-of-words, n-gram, and TFIDFrestate logistic regression, support vector machine (SVM), Naive Bayes, and boosting modelsdemonstrate data loading and a basic overview of columnsperform Exploratory Data Analysis (EDA) of data
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perform an exploration of linguistic features in datademonstrate feature engineering on datademonstrate simple model building and evaluation using the Decision Tree classifier, logistic regression, and SVMdemonstrate simple model building and evaluation using the Random Forest Classifier, Naïve Bayes, and KNN and compare the results of all the modelsperform model tuning for better results and evaluation using different search methodssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 55sUP NEXT
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2.NLP with Machine Learning (ML)7m 38s
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3.Machine Learning Pipeline for NLP6m 46s
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4.Feature Engineering for NLP5m 12s
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5.Common ML Models Used in NLP5m 17s
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6.Predicting Sarcasm in Text: Data Loading15m 54s
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7.Predicting Sarcasm in Text: Data Analysis13m 56s
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8.Predicting Sarcasm in Text: Linguistic Features5m 35s
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9.Predicting Sarcasm in Text: Feature Engineering17m
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10.Predicting Sarcasm in Text: Model Building Part 114m 39s
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11.Predicting Sarcasm in Text: Model Building Part 212m 20s
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12.Predicting Sarcasm in Text: Model Tuning15m 40s
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13.Course Summary50s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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