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Home » Technology » Big Data & Data Science » Haskell Data Analysis: Growth Hacking with NLP and Sentiment Analysis

Course Skill Level:

Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    HDAGHNL21E09

Who should attend & recommended skills:

Intermediate Python programmers with basic Python standard library, pandas, Jupyter Notebook, Colab, scikit-learn, statistics, data science, machine learning, & neural networks experience

Who should attend & recommended skills

  • This course is for intermediate Python programmers who want to step into the role of a Natural Language Processing Specialist working in the Growth Hacking Team of a new video game startup.
  • Skill-level: Foundation-level 3D Growth Hacking with NLP and Sentiment Analysis skills for Intermediate skilled team members. This is not a basic class.
  • Python standard library: Basic (1-2 years’ experience)
  • pandas: : Basic (1-2 years’ experience)
  • Jupyter Notebook: Basic (1-2 years’ experience)
  • Colab: Basic (1-2 years’ experience)
  • scikit-learn: Basic (1-2 years’ experience)
  • Statistics: Basic (1-2 years’ experience)
  • Data Science: Basic (1-2 years’ experience)
  • Machine Learning: Basic (1-2 years’ experience)
  • Neural Networks: Basic (1-2 years’ experience)
  • NLP and PyTorch: Not required but helpful

About this course

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.

Skills acquired & topics covered

  • 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
  • Creating your dataset
  • Creating a dictionary-based sentiment analyzer
  • Evaluating your dictionary-based sentiment analyzer
  • Creating neural network based sentiment analyzers
  • Finding key phrases and writing a report

Course breakdown / modules

  • Analyzing Tables Using Pandas
  • Running Random Simulations in NumPy
  • Introducing Annotation

  • Build Your Vocabulary (Word Tokenization)

  • Model evaluation and optimization

  • Introducing Deep Learning and the PyTorch Library
  • Model optimization through parameter tuning
  • Introducing NLP in practice: spam filtering
  • What is transfer learning?

  • Project Conclusions

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