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Working in a hands-on learning environment, led by our Growth Hacking with NLP and Sentiment Analysis expert instructor, students will learn about and explore: Creating a data corpus from text reviews.Sampling from imbalanced data Finding sentiment value using NLTK and dictionary-based sentiment analysis tools.Data evaluation with scikit-learn Analyzing reviews using PyTorch and deep learning.Comparing classifier performance.Transformers-based language models.Visualizing findings and presenting a formal report

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Unlimited Duration

Last Updated

February 4, 2021

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Course Description

In this course, you’ll step into the role of a Natural Language Processing Specialist working in the Growth Hacking Team of a new video game startup. Your team wants to massively accelerate your company’s early growth by acquiring huge numbers of customers at the lowest possible cost. To help tailor marketing messages, your boss has asked you to map the market and find out how customers evaluate your competitors’ products. Your challenge is to create a sentiment analyzer that will give a deeper understanding of customer feedback and opinions. To do this, you’ll need to download and create a dataset from Amazon reviews, build an algorithm that will determine whether a review is positive or negative, evaluate your analyzer's performance against star ratings, and lay out your findings in a report for your manager.

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Course Curriculum

    • Creating your dataset 00:00:00
    • Analyzing Tables Using Pandas 00:00:00
    • Running Random Simulations in NumPy 00:00:00
    • Introducing Annotation 00:00:00
    • Creating a dictionary-based sentiment analyzer 00:00:00
    • Build Your Vocabulary (Word Tokenization) 00:00:00
    • Evaluating your dictionary-based sentiment analyzer 00:00:00
    • Model evaluation and optimization 00:00:00
    • Creating neural network-based sentiment analyzers 00:00:00
    • Introducing Deep Learning and the PyTorch Library 00:00:00
    • Model optimization through parameter tuning 00:00:00
    • Introducing NLP in practice: spam filtering 00:00:00
    • What is transfer learning? 00:00:00
    • Finding key phrases and writing a report 00:00:00
    • Project Conclusions 00:00:00

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