Complete Machine Learning Bootcamp [Updated]

Build a simple web frontend for a machine learning application using Flask and Python, rendering an index page, processing input text with NLP, translation, and sentiment analysis.

Build a Flask backend for an ML app, implementing spam detection, sentiment analysis, part-of-speech tagging, and named entity recognition, with translation support for chatbots.

Create and test a translate function that takes English input, uses the user-selected target language, and returns the translated text structured as json for the app.

Learn to build a translation machine learning app demo that translates text between languages using a machine learning service, calling the translation function with input text and selected language.

Create a sentiment analysis app using AWS Comprehend to analyze text and extract positive, negative, neutral, or mixed sentiments, then apply insights to product reviews and marketing decisions.

Explore a sentiment analysis ml app demo that uses Amazon Comprehend to classify text into positive, neutral, negative, and a fourth category, with custom ml model tuning.

Learn to build a Python-based computer vision workflow that analyzes images, identifies landmarks and objects, extracts text, generates thumbnails, and retrieves domain data.

Learn to build a computer vision service that analyzes images and text, generates tags and thumbnails, and recognizes celebrities and landmarks using local images with Python.

Explore content moderator's text and image moderation capabilities, detecting profanity across 100+ languages and scoring adult content in videos, with human review and team-based workflows.

Learn image and video moderation workflows, review uploads for content or racy content, evaluate per-frame video segments, add frame notes, and navigate the moderation dashboard with credentials.

Explore the content moderator dashboard, attach APIs through connectors, and configure workflow settings to test moderation with images. Invite team members and create subteams to collaborate.

Create a content moderation instance to handle text, image, and video moderation; configure subscription, location, resource group; validate and deploy, then prepare Python integration with keys and endpoints.

Create a new custom vision project, upload images, train with the dataset, and evaluate predictions, selecting project type (classification or object detection) and configuring a resource group.

upload images to custom vision, provide minimal text for each image, and assign tags like apple and food; then save and repeat for orange images to expand tagging.

Explore Custom Vision by training a model on labeled images and predicting unlabeled images, using local file uploads and viewing probability-based results to implement the model in your project.

Learn how to delete uploaded images in a Custom Vision project by selecting all items and clicking delete, with items moved to deleted.

Explore how to build a text analytics service with sentiment analysis, key extraction, and language detection, using endpoints, access keys, and quick-start docs.

Analyze sentiment with the API by applying a script to text, where scores near 1 indicate positive and scores near 0 indicate negative, across languages.

Master entity recognition in text analytics by identifying entities like Microsoft as an organization, linking Wikipedia pages, and using Python or Ruby; explore language detection and sentiments in future projects.

Learn to set up a translation service by navigating the machine learning category, selecting the cognitive service marketplace, choosing translate text, and creating a translator instance with free pricing.

Explore AWS machine learning services, including SageMaker, comprehend, rekognition, lex, polly, transcribe, and translate, to train, deploy, and scale ML models with hands-on Python projects.

Explore Amazon Comprehend, a natural language processing service that uses machine learning to detect languages, extract key phrases, entities, and sentiment, organize topics for customer analytics and search.

Learn how to use Python and Comprehend to detect entities in text, prepare input, run the detect entities method, and interpret the resulting entity types from text and images.

Learn how to use Amazon Comprehend to detect key phrases and key entities from input text with a Python script, and view the results including metadata.

Learn to use python boto3 and comprehend to detect sentiments, extract key phrases and entities, and analyze syntax and parts of speech, including pronouns, auxiliary verbs, and prepositions.