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A comprehensive playlist to step into and master the world of machine learning and data science!1๏ธโฃ
Data Science Principles:๐ Essential Mathematics for Machine Learning:
Link๐ Overview and commonly used terms:
Link๐ Current interview trends:
Link๐ Linear Regression Guide:
Link๐ Logistic Regression Playlist:
Link๐ Classification criteria:
Link๐ Simple Bayes Classifier:
Link๐ Types of variables:
Link๐ Dimension reduction:
Link๐ Entropy, mutual entropy, KL divergence:
link๐ Dynamic Pricing Overview:
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Building recommender systems:๐ Netflix Calibrated Recommendations:
Link๐ Netflix Integrated Recommendation Model:
Link๐ The Evolution of Recommender Systems:
Link๐ Embedding tutorial:
Link๐ Annoy library for approximate nearest neighbor:
link๐ Reducer product for ANN:
Link๐ Model-based account recommendations:
Link๐ PID controller for diversity:
link๐ Instagram Recommender System:
Link๐ LinkedIn CTR Modeling:
Link๐ Meituan's two-tower recommendation model:
Link๐ Scalable Two Tower Model Question-Item:
Link๐ Twitter Recommender Algorithm:
Link๐ eBay language model for recommender system:
link๐ Overcoming biases for recommender systems:
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Advanced Model Techniques and Applications:๐ Importance of Model Calibration:
Link๐ Detect and monitor data changes:
Link๐ Neural Networks Training:
Link๐ Analytics-based advertising with Pinterest:
Link๐ Using Pre-trained Bert:
Link๐ Model Compression with Knowledge Distillation:
Link๐ Multi-Armed Bandit Strategies:
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The world of large language models (LLMs):๐ Conversational AI:
Link๐ The dual nature of conversational language models:
link๐ Frontier Developments in LLM:
Link๐ Improving the performance of open source LLMs:
Link๐ Building artificial intelligence in Shah Rukh Khan style:
Link๐ Tags: #DataScience
#Python
#ML #AI #LLM #BIGDATA #Courses #Pandas
http://t.me/codeprogrammer โญ๏ธ
