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Explore YOLOv8 for object detection in Python, from installation to implementation on images, webcams, and videos. Includes hands-on coding and practical demonstrations.
Explore Python face detection models for age, race, emotion, and keypoints. Compare results and implement various techniques in this hands-on coding session.
Learn to use Facebook's Prophet model for time series forecasting in Python, covering data preparation, model training, evaluation, and incorporating holiday effects.
Explore chess data analysis using Python, covering ELO differences, game upsets, PGN parsing, and Stockfish integration. Learn to visualize winning streaks and analyze professional games.
Live coding session demonstrating the creation of a flight delay dataset using Python and Pandas. Learn data acquisition, cleaning, exploration, and visualization techniques with real-world airline data.
Explore advanced time series forecasting with XGBoost, covering outlier removal, cross-validation, lag features, and future predictions. Enhance your skills in handling complex time-dependent data for improved forecasting accuracy.
Benchmark comparison of Pandas alternatives: Dask, Modin, Ray, and Vaex. Evaluates speed performance on large datasets, offering insights for data scientists seeking faster Python data processing options.
Explore Python coding to simulate and solve the 100 prisoner riddle, comparing random and loop strategies through data visualization and network analysis.
Comprehensive comparison of pytesseract, easyocr, and keras_ocr for extracting text from images using Python, with practical examples and performance analysis.
Learn to predict energy consumption using XGBoost and Python in this hands-on tutorial. Covers data preparation, feature engineering, model building, and forecasting techniques for time series analysis.
Learn to process video data using Python and OpenCV, covering reading, writing, metadata extraction, image manipulation, and annotation. Gain essential skills for machine learning applications in computer vision.
Learn to classify Amazon review sentiment using NLTK's VADER and Huggingface's RoBERTa models in Python. Compare results and gain practical NLP skills for language modeling and data science.
Learn to analyze economic data using Python and Pandas. Scrape, clean, and explore indicators with the FRED API. Create insightful visualizations and comparisons for unemployment and participation rates.
Comprehensive guide to cross-validation in machine learning, covering setup, datasets, common pitfalls, train-test splits, and practical applications using scikit-learn in Python.
Learn image processing in Python using matplotlib and OpenCV. Covers reading, manipulating, and saving images, with practical examples for computer vision and data science applications.
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