James Briggs Courses

OpenAI CLIP Explained - Multi-modal ML

Explore OpenAI's CLIP model, bridging text and image understanding. Learn about multi-modal AI, CLIP's functionality, and its applications in encoding, classification, and object detection.

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

Bag of Visual Words for Image Classification and Retrieval

Learn to build image embeddings using the Bag of Visual Words technique for image retrieval, object detection, and classification in computer vision applications.

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

How to Build a Q&A Reader Model in Python - Open-Domain QA

Learn to build an open-domain question-answering system in Python, covering data preprocessing and model fine-tuning for efficient information retrieval from large datasets.

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

Making the Most of Data - Augmented SBERT

Explore data augmentation in NLP, focusing on AugSBERT for low-resource domains. Learn techniques to generate and label data automatically, enhancing ML model performance without manual annotation.

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

Building an API with Flask in Python

Learn to build and test a Flask-based API in Python, covering GET/POST/PUT/PATCH/DELETE requests and endpoints for efficient software communication.

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

Building MLM Training Input Pipeline - Transformers From Scratch

Learn to create a complex input pipeline for Masked Language Modeling, transforming raw OSCAR data into a DataLoader ready for training transformer models from scratch.

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

Vector-Based Methods for Similarity Search - TF-IDF, BM25, SBERT

Explore three vector-based approaches for language comparison and document similarity: TF-IDF, BM25, and Sentence-BERT. Learn key techniques for building effective search engines in AI and machine learning.

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

Traditional Methods for Similarity Search - Jaccard, W-Shingling, Levenshtein

Explore three traditional similarity search methods: Jaccard Similarity, w-shingling, and Levenshtein distance. Learn their applications in language comparison and document matching for effective search engines.

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

Training BERT - Train With Next Sentence Prediction

Learn to pre-train BERT models using Next Sentence Prediction (NSP) for improved language understanding in specific domains. Practical implementation with unstructured text data.

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

How to Build Python Packages for Pip

Learn to create and publish your own Python packages on PyPI, enabling easy installation via pip. Discover the process of packaging code, structuring projects, and contributing to the Python ecosystem.

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

Sentiment Analysis on Any Length of Text With Transformers - Python

Learn to perform sentiment analysis on long texts using transformers in Python, overcoming token limitations through chunking and processing techniques for NLP tasks.

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

How to Build Custom Q&A Transformer Models in Python

Learn to fine-tune pre-trained transformer models for question-answering using HuggingFace and PyTorch. Gain practical skills in NLP and unlock the power of transformers for custom Q&A applications.

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

Building a Plotly GME Chart in Python

Learn to create an interactive stock chart for GameStop using Python, PlotLy, and AlphaVantage API, exploring the coding process from concept to implementation.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Novice to Advanced RegEx in Less Than 30 Minutes + Python

Master regular expressions and their Python implementation in this comprehensive 30-minute tutorial, covering essential concepts from basic syntax to advanced techniques for efficient text processing and analysis.

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

Build a Transformer for Language Classification in TensorFlow

Build a transformer-based sentiment classifier using HuggingFace's library in TensorFlow. Learn to download data, tokenize inputs, prepare datasets, define the model architecture, and train for multi-class sentiment analysis.

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