Need for Speed: Comparing Pandas 2.0 with Four Python Speed-Up Libs (with Code) https://towardsdatascience.com/need-for-speed-comparing-pandas-2-0-with-four-python-speed-up-libs-with-code-b287e0d9b836
From TF-IDF to Transformers: How Machines Learned to Search by Meaning Rule-based retrieval, classical ML, embeddings, and fine-tuned transformers in Python /https://medium.com/@theomitsa/from-tf-idf-to-transformers-how-machines-learned-to-search-by-meaning-3b0c4c714db4/
From TF-IDF to Transformers: Implementing Four Generations of Semantic Search /https://towardsdatascience.com/from-tf-idf-to-transformers-implementing-four-generations-of-semantic-search/
From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers https://theomitsa.medium.com/from-classical-models-to-ai-forecasting-humidity-for-energy-and-water-efficiency-in-data-centers-02afd227558c?postPublishedType=repub
Unlocking Valuable Data and Model Insights with Python Packages Yellowbrick and PiML(with Code) View at Medium.com MEDIUM ARTICLE SHOWN ABOVE
Mastering the Versatility and Depth of Python’s Rich Plot Collection (with Code) View at Medium.com https://medium.com/towards-data-science/mastering-the-versatility-and-depth-of-pythons-rich-plot-collection-with-code-b136b584d143LINK
A Guide to 21 Feature Importance Methods and Packages in Machine Learning (with Code) https://towardsdatascience.com/a-guide-to-21-feature-importance-methods-and-packages-in-machine-learning-with-code-85a841f8b319
Five Ways to Remember the Past (Model State) in Python https://towardsdatascience.com/five-ways-to-remember-the-past-model-state-in-python-2c8430d29679
Python Factories for Scalable, Reusable, and Elegant Code https://towardsdatascience.com/python-factories-for-scalable-reusable-and-elegant-code-1358ea06936d#4798-fb4147753bfe
Discovering the Treasures of 22 R Exploratory Analysis Packages https://towardsdatascience.com/discovering-the-treasures-of-22-r-exploratory-analysis-packages-9bb1c5b4e6f8
