I’m not saying you need to be an expert in advanced calculus to do machine learning…
BUT, there is a big difference between someone that does vs someone that does NOT have a good foundation in stats when it comes to getting & explaining business results.
My thought process back in the day was to obtain a great foundation in stats and machine learning at the same time.
So here’s what helped me. I read a ton of books.
Here are the 3 books that helped me learn data science the most...
1. R for Data Science (Wickham & Grolemund) r4ds.had.co.nz
Skforecast is a Python library for time series forecasting using machine learning models.
Skforecast works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
skforecast shines with probabilistic forecasting.
When trying to anticipate future values, most forecasting models try to predict what will be the most likely value.
BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition in R
Let's explore:
1. What is BEAST?
BEAST stands for Bayesian Estimator of Abrupt change, Seasonality, and Trend.
But what does that mean?
According to the authors, BEAST is a fast, generic Bayesian model averaging algorithm to decompose time series or 1D sequential data into individual components, such as abrupt changes, trends, and periodic/seasonal variations