Welcome to SETScholars Data Science Recipes

This is an archive of end-to-end Applied Machine Learning and Data Science Recipes for beginners and business analysts. All codes are written in popular programming languages for Machine Learning such as Python & R using the mostly used ML frameworks e.g. scikit-learn, XGBoost, CatBoost, LightGBM, TensorFlow, Keras and TuriCreate.

Applied Machine Learning: Latest Data Science Recipes

Diabetes Classification using NB, KNN, SGD and DT classifiers: An approach to Grid Search and Random Search parameter tuning in Python
Applying Neural Networks to Pima Indian Diabetes Dataset: A Data Science Recipe for Parameter tuning
Extra-tree Bagging Algorithm to Pima Indian Diabetes Dataset: A Data Science Recipe for Parameter tuning

Applied Machine Learning: Latest Kickstarter Examples

How to select elements from Numpy array in Python
How to create a Sparse Matrix using scipy in Python
How to transpose a Vector or Matrix in Python

AML & DS Recipe Writer

Nilimesh Halder, PhD is a Data Scientist and Applied Machine Learning Engineer. He has 12+ years of experiences as AI - DevOps. He is also a Google trained GCP-Data Engineer and GCP-Data Scientist.

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