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Codecademy

Data Scientist: Natural Language Processing Specialist

via Codecademy Path

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Overview

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NLP Data Scientists find meaning in language, analyze text and speech, and create chatbots. They use Python, SQL, & NLP to answer questions. Includes **Python 3**, **SQL**, **pandas**, **scikit-learn**, **SpaCy**, **NLTK**, **Tensorflow**, **Matplotlib**, and more.

Syllabus

  • Welcome to the Data Scientist: Natural Language Processing Specialist Career Path
    • Discover what you will learn on your journey to becoming a Data Scientist: Natural Language Processing Specialist!
  • Principles of Data Literacy
    • Discover the world of data in this fully conceptual course where you will learn how to think about, visualize, and analyze data.
  • Learn SQL
    • Learn SQL — a popular language that’s used for communicating with databases and working with data.
  • Python Fundamentals for Data Science (Part I)
    • Build a foundation in programming with Python with a focus on Data Science!
  • Python Fundamentals for Data Science (Part II)
    • Continue building your Python Skills while applying them to real data science challenges including finding and working with real data.
  • Portfolio Project: U.S. Medical Insurance
    • Use your understanding of Python syntax to sort and analyze data about U.S. medical insurance costs!
  • Python Pandas for Data Science
    • Learn how to use the Python pandas library and lambda functions for Data Science.
  • Exploratory Data Analysis in Python
    • Learn about exploratory data analysis (EDA) techniques for Data Science
  • Statistics Fundamentals for Data Science
    • Learn how and when to use the essential statistical tools Data Scientists use to analyze data.
  • Data Visualization Fundamentals with Python
    • If a picture is worth a thousand words, then a visualization is worth more than a thousand data points. Learn how to make them here!
  • Portfolio Project: Data Visualization
    • Use your understanding of data visualization to analyze and plot data about GDP and life expectancy.
  • Data Wrangling, Cleaning, and Tidying
    • Clean, well-structured data is essential to data science but cleaning data requires both a keen eye and technical skills. Develop both here!
  • Communicating Data Science Findings
    • Communication is an important part of your work as a data scientist. Learn best practices for effectively explaining your analysis.
  • Data Science Foundations Portfolio Project
    • Use your knowledge of data analysis to interpret data about endangered animals for the National Park Service.
  • Next Step: The Natural Language Processing Speciality
    • Get ready for your data science specialization by reviewing how each role works to make meaning from data.
  • Python Fundamentals Part III
    • Expand your knowledge of Python with Classes and Modules.
  • Math for Machine Learning
    • Build your mathematics foundation in preparation for Machine Learning.
  • Machine Learning Fundamentals
    • Build a foundational understanding of machine learning and feature engineering.
  • Supervised Learning I : Regressors, Classifiers and Trees
    • Learn about Supervised Learning algorithms like Linear and Logistic Regression, KNN, and Decision Trees
  • Unsupervised Learning Algorithms I
    • Learn about Unsupervised Learning algorithms, their implementation and where they're used.
  • Supervised Learning II: SVM's, Random Forests, Naive Bayes
    • Continue building your machine learning knowledge with Support Vector Machines, Random Forests, and Naive Bayes Classifiers.
  • Machine Learning Portfolio Project
    • Use your knowledge of machine learning to build, train, and test predictions you draw about data from OKCupid.
  • Deep Learning and Neural Networks
    • Explore how deep learning and neural networks are leveraged for machine learning!
  • Getting Started with Natural Language Processing
    • Delve into the exciting world of Natural Language Processing (NLP) with this overview of major topics in the field.
  • Text Preprocessing
    • Find out how to prepare your text data for most NLP tasks.
  • Language Parsing
    • Apply regular expressions (regex) and other natural language parsing tactics to find meaning and insights in the texts you read every day.
  • Language Quantification
    • Learn different ways to represent language numerically, including bag-of-words, tf-idf, and word embeddings.
  • Text Generation
    • Learn about seq2seq and LSTM neural networks commonly used in NLP work and how to implement them for machine translation.
  • Build Chatbots
    • Build your very first chatbot with Python and say "hello" to your next cutting-edge skill!
  • NLP Portfolio Project
    • You've learned a lot and it's time to show off your skills. Analyze text message data any way you like in this portfolio project.
  • Data Scientist: NLP Specialist Final Review
    • Review what you learned in the Data Scientist: NLP Specialist Career Path and explore ways to deepen your knowledge!

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