InsightLab / Data Science Cookbook

This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

brief introduction to Python for machine learning

Jupyter notebooks from the scikit-learn video series

Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Association Rule Mining from Spatial Data for Crime Analysis

The "Python Machine Learning (2nd edition)" book code repository and info resource

Compilation of R and Python programming codes on the Data Professor YouTube channel.

🚚 Agile Data Preparation Workflows made easy with dask, cudf, dask_cudf and pyspark

Distributed scikit-learn meta-estimators in PySpark

Zeek Analysis Tools (ZAT): Processing and analysis of Zeek network data with Pandas, scikit-learn, Kafka and Spark

Applying Data Science and Machine Learning to Solve Real World Business Problems

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

A library for debugging/inspecting machine learning classifiers and explaining their predictions

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

A collection of notebooks of my Machine Learning class written in python 3