Data Labeling Courses and Certifications

1littlecoder

Custom Named Entity Recognition with Open Source NER Annotator and spaCy - NLP Python Tutorial

Learn to create custom named entities using an open-source NER annotator and train a spaCy NLP model for entity recognition in Python, enhancing text analysis capabilities.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Nvidia

Deploy AI with AWS ML and IoT Services on Jetson Nano

Learn to deploy AI models on Jetson Nano using AWS ML and IoT services. Explore SageMaker, Greengrass, and real-time video processing for edge computing applications.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

DigitalSreeni

Annotate Images Like a Pro: Python Image Annotation Tool Walkthrough

Master image annotation with a Python-based tool. Learn installation, project creation, manual and semi-automatic annotation techniques, class organization, and exporting to various formats. Explore additional features for data management and augmentatio…

• YouTube
• 32 minutes
• Self-Paced
• Free Video

The Full Stack

Data Management - Full Stack Deep Learning - Spring 2021

Explore ML infrastructure for data management: ingestion, storage, processing, exploration, labeling, and versioning. Learn about tools and best practices for effective dataset handling in deep learning projects.

• YouTube
• 1 hour
• Self-Paced
• Free Video

The Full Stack

Jeremy Howard on Platform.ai and Fast.ai - Full Stack Deep Learning - March 2019

Explore machine learning innovations with Jeremy Howard, covering data labeling, deep learning successes, and insights from Fast.ai and Platform.ai for effective AI development.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

The Full Stack

Data Management - Full Stack Deep Learning - March 2019

Explore data management essentials: labeling, storage, versioning, and processing. Learn about data flywheels, annotation techniques, and efficient workflows for deep learning projects.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

The Full Stack

Lukas Biewald on Founding Weights & Biases and FigureEight - Full Stack Deep Learning - March 2019

Insights from AI industry pioneer on founding data labeling and ML tools companies, exploring challenges, breakthroughs, and the future of machine learning applications.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

PyCon US

Machine Learning on Encrypted Data with CrypTen

Learn to develop machine learning models on encrypted data using CrypTen, a PyTorch-based framework for secure computing. Explore feature aggregation, data labeling, dataset augmentation, and model hiding techniques.

• YouTube
• 26 minutes
• Self-Paced
• Conference Talk

MLOps.community

Designing Human-in-the-Loop Experiences for LLMs - Part 2

Explore techniques for integrating human feedback into LLM behavior at scale, addressing technical challenges and user experience considerations for collaborative AI learning.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

MLOps.community

Solving the Last Mile Problem of Foundation Models with Data-Centric AI

Explore data-centric AI techniques to enhance foundation models' accuracy for complex, high-value use cases. Learn to overcome hallucinations, biases, and misclassifications in real-world AI applications.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

MLOps.community

Foundational Models in Enterprise AI - Challenges and Opportunities

Explore the future of AI with foundation models, their enterprise applications, and challenges. Learn about Snorkel AI's approach to optimizing these models for cost, quality, and control.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Venelin Valkov

Testing Microsoft Phi-4 Model for Text Processing and Data Analysis

Explore Microsoft's Phi-4 language model through hands-on testing of summarization, data labeling, and text extraction capabilities using Ollama, with practical demonstrations and performance comparisons.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

Python Tutorials for Digital Humanities

How to Prepare Annotations in Prodigy for Training SpaCy SpanCat Models - Part 2

Master the process of preparing and using Prodigy annotations to train SpanCat models in spaCy, focusing on data preparation, annotation techniques, and model training workflows.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

Snorkel AI

Data-Centric AI Approach in Pharmaceutical Industry - AstraZeneca Case Study

Explore data-centric AI in pharma: personalized doctor education, patient treatment optimization, and real-world data utilization for predictive modeling at AstraZeneca.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Ground Truth: The Foundation of Accurate AI and Machine Learning Models

Discover how ground truth data ensures accurate AI predictions by powering supervised learning, addressing challenges like ambiguity, and improving data labeling for better AI performance.

• YouTube
• 10 minutes
• Self-Paced
• Free Video