Big Data and NLP with Python- 2-in-1

Big Data and NLP with Python: 2-in-1, available at $19.99, has an average rating of 4.38, with 43 lectures, 2 quizzes, based on 8 reviews, and has 94 subscribers.

You will learn about Learn how to efficiently ingest, query, and analyze data using MongoDB and Spark Learn practical NLP techniques and methods to analyze your text data Write MongoDB queries using operators and chain these together into aggregation pipelines Get to grips with powerful new libraries such as Gensim, Spacy, and Keras Perform different techniques to categorize text data Extract meaning and insights from text data such as vector space models This course is ideal for individuals who are This Learning Path is for data engineers, data scientists, researchers, and developers who wish to know how to efficiently ingest, query, and analyze data using MongoDB and Spark. It is particularly useful for This Learning Path is for data engineers, data scientists, researchers, and developers who wish to know how to efficiently ingest, query, and analyze data using MongoDB and Spark.

Enroll now: Big Data and NLP with Python: 2-in-1

Summary

Title: Big Data and NLP with Python: 2-in-1

Price: $19.99

Average Rating: 4.38

Number of Lectures: 43

Number of Quizzes: 2

Number of Published Lectures: 43

Number of Published Quizzes: 2

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to efficiently ingest, query, and analyze data using MongoDB and Spark

Learn practical NLP techniques and methods to analyze your text data

Write MongoDB queries using operators and chain these together into aggregation pipelines

Get to grips with powerful new libraries such as Gensim, Spacy, and Keras

Perform different techniques to categorize text data

Extract meaning and insights from text data such as vector space models

Who Should Attend

This Learning Path is for data engineers, data scientists, researchers, and developers who wish to know how to efficiently ingest, query, and analyze data using MongoDB and Spark.

Target Audiences

This Learning Path is for data engineers, data scientists, researchers, and developers who wish to know how to efficiently ingest, query, and analyze data using MongoDB and Spark.

Natural language processing and Big Data are the most interesting subfields of data science. You will learn to use the most popular programming language, Python with the latest Big Data technology, Apache Spark. If you’re a data science professional who is familiar with Python and wants to take first steps in the world of data science by acquiring NLP and Big Data skills, then this learning path is for you.

This comprehensive 2-in-1 course teaches you how to efficiently ingest, query, and analyze data using MongoDB and Spark. You will also learn practical NLP techniques and methods to analyze your text data. It’s a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to build on your understanding of the different Big Data and NLP techniques with every section.

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Working with Big Data in Python, starts off with explaining the use of MongoDB, how it differs from SQL and structured data, and setting up your first database and query. You will then learn how to make use of MongoDB and Python such as including the pyMongo library, retrieving results from MongoDB cursors, and building up complex aggregation pipelines using operators. You will also work on an example which builds a data pipeline using PyMongo. Next, you will be introduced to Spark as the main software framework for working with large datasets across distributed computing resources. Finally, you will explore another live example of a data science workflow using MongoDB and Spark which includes the analysis of Reddit comments and machine learning task to predict comment popularity.

The second course, Next Generation Natural Language Processing with Python, begins with explaining how NLP can help you extract useful information from large collections of text data, and how you can use the latest Python libraries for NLP. You will then learn how to solve a practical problem using NLP by building a spam SMS detector. You will also learn to convert words into numbers that can be analyzed. Next, you will learn how to accurately label new documents to get an accuracy score and cluster your data together. You will be glanced through more advanced analysis wherein you will learn to model text by using vector space models and semantic parsing to break down the components of a sentence. Finally, you will work with neural networks and learn how to write believable text.

By the end of this Learning Path, you’ll be able to use the latest libraries of Big Data and NLP in Python for your day-to-day data science tasks.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

Alexis Rutherford is a Research Scientist at MIT Media Lab. He has a PhD in Physics and nearly 10 years of experience of using Python for data analysis and modeling gained at the United Nations, Facebook, and elsewhere. He has tackled many problems using data analysis including epidemiology, ethnic violence, vaccine hesitancy, and constitutional change and has built pipelines for social media data, legal documents, and news articles among others. He blogs and tweets regularly on data science and data privacy.

Course Curriculum

Chapter 1: Working with Big Data in Python

Lecture 1: The Course Overview

Lecture 2: What Is MongoDB and Why Should I Use It?

Lecture 3: From Tabular Data to JSON Documents

Lecture 4: MongoDB Indices and Datatypes

Lecture 5: Setting Up MongoDB and Running Our First MongoDB Query

Lecture 6: Setting Up pyMongo

Lecture 7: Using pyMongo Cursors

Lecture 8: Inserting and Finding Documents

Lecture 9: Return Codes and Exceptions

Lecture 10: Using Operators, Updates, and Aggregations

Lecture 11: Grabbing Weather Data via OpenWeather API

Lecture 12: Ingesting Weather Data into MongoDB

Lecture 13: Querying Weather Data from MongoDB

Lecture 14: What Is Spark and When Do We Need It?

Lecture 15: Data Structures in Spark

Lecture 16: Data Structures in Spark (Continued)

Lecture 17: Connecting to MongoDB with PySpark

Lecture 18: Making Reddit Data Available to PySpark

Lecture 19: Loading Data from MongoDB in Spark, Transform into Pandas DF

Lecture 20: Preparing Data for Prediction Task Using spark.ml

Lecture 21: Predicting Up Votes Using pyspark.ml

Chapter 2: Next Generation Natural Language Processing with Python

Lecture 1: The Course Overview

Lecture 2: NLP and Its Uses

Lecture 3: Statistical Analysis of Language – Counting Versus Understanding

Lecture 4: Exploring Different Types of Text Data

Lecture 5: NLP Libraries in Python and Installation

Lecture 6: Finding and Loading Spam SMS Data

Lecture 7: Preparing SMS Data for Analysis and Training a Classifier

Lecture 8: Classifying Messages, Evaluating, and Testing

Lecture 9: Understanding Text as Noisy Data

Lecture 10: Splitting Documents into Parts

Lecture 11: Turning Words into Numbers

Lecture 12: Supervised Learning Refresher

Lecture 13: Supervised Learning Refresher (Continued)

Lecture 14: Building a Pipeline in scikit-learn to Categorize News Articles

Lecture 15: Optimizing a Classifier Using GridSearchCV

Lecture 16: Deploying a Trained Model in Production

Lecture 17: Finding Structure in a Text Corpus

Lecture 18: Understanding Gensim for Efficient Topic Modelling

Lecture 19: Creating a Corpus and Extracting Topics

Lecture 20: Evaluation of Topic Models

Lecture 21: Working with Vector Space Models

Lecture 22: Implementing Semantic Parsing

Instructors

Packt Publishing
Tech Knowledge in Motion

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4 stars: 1 votes

5 stars: 5 votes

Frequently Asked Questions

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