Machine Learning Topics

Error functions, how to minimize errors (gradient descent)

What is alpha
Gradient Descent
Gradient descent keeps changing the Parameters to reduce the cost function gradually. With each iteration we shall come closer to a minimum. With each iteration the parameters must be adapted simultaneously! The size of a “step”/iteration is determined by the parameter alpha (the learning rate).
https://towardsdatascience.com/machine-learning-basics-part-1-concept-of-regression-31982e8d8ced
Partial Derivative Function


How to tune algorithms
Add parameters - time series, lags


Regularization - ridge, lasso
minimization of coeffeiences

When to use what
Process, steps, examples of data prep
https://scikit-learn.org/stable/modules/preprocessing.html

Scaling, one-hot encoding, outliers

One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction


Error functions, how to minimize errors (gradient descent)
What is alpha
How to tune algorithms
Regularization - ridge, lasso
When to use what
Process, steps, examples of data prep
Scaling, one-hot encoding, outliers


Demantra | Oracle Products - to predict demand using Time series modeling (Lags, Dummy Variables, Time Series)
Gradient descent - Derivation, partial differentiation
PCA analysis - Derivation
supply chain management machine learning
ARIMA Model for Time Series Forecasting in Python

Exploratory data analysis with Spark

Cholesky transform
https://en.wikipedia.org/wiki/Cholesky_decomposition
Lower diagnol matrix

We are going to calculate a matrix that summarizes how our variables all relate to one another.
We’ll then break this matrix down into two separate components: direction and magnitude.

https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c

https://www.kaggle.com/nishantbigdata/exploratory-data-analysis-with-spark