Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
📜 🎉 Automated reporting of objects in R
Horizontal Pod Autoscaler built with predictive abilities using statistical models
Hierarchical Time Series Forecasting with a familiar API
Time Series Analysis and Forecasting in Python
Nyoka is a Python library that helps to export ML models into PMML (PMML 4.4.1 Standard).
Input Output Hidden Markov Model (IOHMM) in Python
A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.
Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.
Here I go through the processing of prototyping a mean reversion trading strategy using statistical concepts, then test it in backtrader.
Time Series Decomposition techniques and random forest algorithm on sales data
Implemented an A/B Testing solution with the help of machine learning
Support financial data science workflow, manage large structured and unstructured data sets, and apply financial econometrics and machine learning
Practical financial data science examples applying statistics, time series analysis, graph analytics, backtesting, machine learning, natural language processing, neural networks and LLMs
Sharing the solved Exercises & Project of Statistics for Data Science using Python course on Coursera by Ankit Gupta
Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification
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