Hadoop & Data Science NLP (All in One Course).

Download and install virtualization tools like VirtualBox and PuTTY, then set up the Hortonworks sandbox to run Hadoop locally or in the cloud.

Install a sandbox using VirtualBox, configure RAM, import the sandbox appliance, note the IP address, map the hostname via the hosts file, and access the sandbox dashboard in a browser.

Access the Hadoop sandbox through putty, log in with the given credentials, explore the sandbox welcome (splash) page, manage services, and reset the admin password to restart the environment.

Explore basic Hadoop operations and practice commands for listing, copying, moving, creating directories, navigating, and configuring file permissions across local and distributed environments.

Explore Apache NiFi concepts, including flow files, flow file processors, ports and connections, and flow control, and learn how processors route, transform, and orchestrate data between systems.

Explore Apache NIFI's UI canvas components, including the top toolbar, status bar, operator palette, search, and global menu, plus bird's eye view, breadcrumbs, and templates for data flow navigation.

Discover the Apache NiFi architecture, including the flow controller as the operation brain and the pluggable flow file, content, provenance, and local repositories for managing flow states and data.

Download Apache NiFi by transferring files between local and remote environments, using drag-and-drop, unzip archives, and configuring within a virtual box for secure file transfers.

Configure Apache NiFi by editing the properties file to set port 9090 and starting the service, then use a tweet extraction template to transform data with processors while monitoring logs.

Explore Apache Solr, an open source, Java-based enterprise search platform built on Lucene. Enjoy full-text search, real-time indexing, dynamic clustering, and RESTful APIs for popular languages.

Explore the basics of search engines and their three core components: a database, crawlers, and a search interface. See how keyword queries retrieve relevant links, illustrated by Google.

Discover how search engines acquire content, build and analyze documents, index data, power the search user interface, construct and run queries, and display results.

Explore the core architecture of apache solr, including query and request handlers, spellchecking and highlighting features, the query parser, response writers, tokenizers, analyzers, and update handlers.

Explore Apache Solr basic concepts and configuration files, including core, schema, and memory management, and understand distributed Solr cloud terms like node, collection, shard, replica, and leader using ZooKeeper.

Spin up Apache Solr, deploy it, and configure it to receive tweets using custom configurations, restart affected services, and create a tweets collection for real-time analytics on a dashboard.

Create a Twitter app to pull tweets for a sentiment analysis project using natural language processing. Store and transform the extracted tweets in Hadoop as part of the workflow.

Discover the banana dashboard built on solar for real-time analytics of road tweets, with D3 Garches panels and bootstrap's 12-column grid.

Learn to extract tweets from Twitter, process them with a processor workflow, and store results in local and distributed file systems, visualizing real-time analytics on a solar-powered dashboard.

Explore Apache Hive, a sql-like query language on Hadoop, enabling table-based queries and large-scale data processing on HDFS with MapReduce, and integrated tools like Sqoop and Pig.

Explore the Apache Hive architecture, from the user interface and command line to the processing and execution engines and the distributed file system that stores tables and databases.

Learn how Hive processes a query end-to-end—from the command line driver and decompiler to the compiler, plan, and execution on the execution engine for map reduce job, then fetch results.

Create and drop databases, use a database, and list tables in hive. Create external and internal tables with row format delimited and fields terminated by pipe.

Explore how Hive partitions tables into static and dynamic partitions, and how to use a staging table to load data, create partitioned tables, and query specific partitions for faster results.

Explore Apache Hive operators and functions, including relational, arithmetic, and logical operators, plus complex constructors like map and struct; learn data loading, table design, and aggregate queries.

Learn how hive views simplify queries by exposing subsets of a table without storing data, and how hive indexes on a column optimize performance, including engine requirements for index creation.

Set up hive tables to receive JSON format data by following step-by-step installation, patch extraction, directory setup, and service restart processes described in the lecture.

Explore how to create Hive tables and views to store json format data, set up external tables, and perform sentiment analysis using a word dictionary without natural language processing algorithms.

Visualize tweet sentiment data with Apache Zeppelin, building bar and pie charts to explore language and country distributions after preprocessing in Hadoop and NLP workflows.

Explore tokenization in natural language processing, including word and sentence tokenizers, corpora, and lexicons, and how punctuation and context shape sentiment analysis.

Explore word stemming as a preprocessing step that reduces variants by trimming suffixes (e.g., shining to shine) using the Porter stemmer and tokenization before NLP analysis.

Learn part of speech tagging by labeling each word with its lexical category, such as noun or adjective, using tokenization and corpus-based POS tagging for training and testing.

Learn how named entity recognition identifies and classifies entities such as persons, organizations, and locations in text, enabling answers to who, where, and what in natural language processing (nlp).

Explore the concept of lemmatization, its limitations relative to stemming, and how it yields real words with the same meaning across different word forms, with practical examples.

Develop a text sentiment analysis classifier using natural language processing to label text as positive or negative, training on labeled corpora like movie reviews and testing with unseen data.

Explore sentiment analysis with the movie review dataset by turning words into features. Build a 4000-word feature set using word presence booleans and handle punctuation as tokens.

Explore how naive Bayes classifiers classify text documents by independent features, train on labeled data, and evaluate accuracy to distinguish categories like politics, philosophy, or technology.

Learn how to save and reuse a trained machine learning model with Python pickle, enabling fast loading and prediction of sentiment without retraining.

This lecture shows building a sentiment analysis model from labeled tweets (positive, neutral, negative) using a word dictionary and supervised learning, then advancing with feature extraction and a classifier.

Learn to build a real-time Twitter sentiment analysis pipeline that streams live tweets, classifies sentiment as positive or negative, and uses pickling to speed up repeated predictions.

Plot and monitor live Twitter sentiments in real time, updating a dynamic graph from a running stream with positive and negative sentiment data.