Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Deep learning, machine learning and big data are classified in data science context.
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 guide shows how to obtain crypto data via CoinGecko API and calculate the coefficient of variation that is a statistical measure of the dispersion of data points.
Read MoreOverview of causal inference and RCT in a nutshell. Starting from RCT and various techniques that have been developed in causal inference. Introduced EconML.
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 MoreTalib library doesn’t support to draw trendlines. This is the article how to compute trendlines from DataFrame and draw it in matplotlib.
Read MoreTrend indicators are usable and handy for beginners. Here is to introduce how to draw chars and bars for Moving Averages, MACD, RSI and OBV in Python.
Read MoreTutorials how to craw a candlestick from DataFrame object with three different libraries mplfinance, plotly and bokeh.
Read More