Dimensionality reduction techniques are used to extract informative features that could be used for later learning, etc. Tested PCA, MDS and t-SNE for the breast cancer dataset.

Read MoreWhat are differences between linear regression and polynomial regression? We must know these techniques well but it is still vague somewhat.

Read MoreStandarization and normalization are essential techniques to apply your data set before using classifiers in some cases. I tested both with iris data set.

Read MoreThis article is an overview why counterfactual explanations are required in recent ML projects and how to apply Microsoft research DiCE library for Titatic data set. There have been some foreseen and ongoing trends in AI/ML for 2020 such as AutoML, MLOps, AI ethic those are to democratize AI/ML in the industries more than ever. […]

Read MoreSupport and resistence lines are referred to analyze chart patterns, and a direction of assets for traders. This is a guide to draw those lines in matplotlib.

Read MoreOHLCV stands for Open, High, Low, Close and Volume (Volume is optional). It’s used for market data such as stock, forex, commodity and crypto and consists of a series of rows that represent 5 data points: the opening and closing price, the highest and lowest price during a certain period of time. Volume is the […]

Read MoreIt’s crucial to understand the distribution curve of data (asset returns) and if it conforms to the normal distribution. You can learn how to do this in Python.

Read MoreTo understand what historical volatility and sharpe ratio are in the markets. It is a how-to guide how to compute volatility and sharpe ratio in Python.

Read MoreTo explain how to use cumprod in Pandas library to calculate cumulative portfolio return and single asset return in an efficient code with Python.

Read MoreTo introduce Anaconda distribution and the official Docker image. You’ll know how to customize the image in Dockerfile and spin up your own environment.

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