Machine Learning and Big Data Analytics with AWS

Build a Flask frontend for an ML app, creating a web interface to process text with NLP tasks such as translation, sentiment analysis, and POS tagging using AWS.

Build a Flask-based backend for an ML application by adding NLP features such as spam detection, sentiment analysis, part of speech tagging, named entity recognition, and translation.

Install dependencies in a virtual environment, configure the translation service with an access token, and use the translate function to convert English to Spanish, producing JSON for app integration.

Demonstrates building a web translation app using a machine learning service for translation, translating text between languages, and handling input text, language codes, and translation results.

Create a sentiment analysis function using AWS Comprehend to classify text as positive, negative, or neutral, and apply it to movie and product reviews for marketing insights.

Build a sentiment analysis web application using Amazon Comprehend to obtain sentiment scores and categorize text as positive, neutral, or negative, and customize your machine learning model for review analysis.

Learn how Amazon Athena analyzes data stored in S3 without managing infrastructure, using Presto-based queries with schema definitions, and optimize cost through compression, partitioning, and data conversion.

Set up and manage AWS S3 buckets for Athena analysis, upload datasets, organize into folders, and configure permissions, policies, and encryption to enable scalable data analytics.

Create and name a new database in Amazon Athena, connect datasets from S3, specify the bucket and folder paths, choose data formats, and add columns for big data analytics.

Extract attributes from the dataset, define data types for location, time, age group, and gender, then create a table with virtual columns and partitions in Athena to run queries.

Learn to create and manage database tables with SQL, including create table if not exists, drop table, bulk add columns, read properties, and save queries with history.

Leverage AWS CloudSearch, a fully managed, scalable search service that simplifies configuring, indexing, and scaling for sites and apps. It supports full-text search, highlighting, autocomplete, and cost-effective regions worldwide.

Practice creating and configuring an AWS CloudSearch domain, setting up an index with attributes, applying access policies, and performing a test search once the domain is created.

Explore how to upload data, generate document batches with photos and documents, and use CloudSearch for custom search on your website, including movie and actor data.

Configure and optimize AWS CloudSearch domains by adjusting access policies, indexing and instance types, uploading data, and fine-tuning search expressions to improve domain search results.

Explore Elasticsearch on AWS: deploy, secure, and scale fast for real-time search across big data using a simple API and integrated security features.

Leverage AWS Kinesis to collect, process, and analyze real-time streaming data from video, logs, streams, and sensors, gaining rapid insights for applications like security and fraud detection.

Create and configure AWS Kinesis streams and Firehose, set shard throughput, enable enhanced fan-out, and ingest real-time data for analytics while learning about creating, querying, and deleting streams.

Learn practical AWS Kinesis using Python boto3 by creating and describing streams, managing permissions, and writing records with partition keys and sequence numbers.

Explore AWS QuickSight’s cloud data visualization, connect sources like Athena and Aurora, build dashboards accessible on any device, and pay only for what you use with no infrastructure to manage.

Explore Amazon's cloud-based machine learning services, including SageMaker, Comprehend, Transcribe, and Polly, and learn to train, deploy, and generate insights from text and speech.

Amazon Comprehend harnesses natural language processing and machine learning to extract key phrases, sentiment, entities, and topics from text, enabling semantic search and personalized content.

Explore Amazon Comprehend for natural language processing, extracting entities such as organizations, locations, and dates, key phrases, language, sentiment, and syntax insights from text.