Press, News, and Blogs

#TraderTerminology: ►Long or Short Biased◄believing the price of a stock will rise or fall over time.

#TraderTerminology: ►Training and Validation and Test Data◄ You should split your Machine Learning data into these three sets : 1) Train the model, 2) Confirm the training results, 3) Test (with no labels to see if it worked!)

#TraderTerminology: ►Big Data◄ is the gathering of large amounts of data relating to your model from both internal and external sources. Big Data and particularly Real Time Big Data is the key to the future of AI and ML.

#TraderTerminology: ►Feature Selection◄ is the process focussing the data to reduce Machine Learning processing time and improve accuracy by removing irrelevant or redundant data (ie the Names in Titanic) without affecting accuracy.

Alternative Data news covering "Quant Jobs No Longer Demanding Finance Knowledge", "14 of the 25 highest-paying U.S. jobs for 2019 are in tech" and "Quantzig Adds New Services to Their Big Data Analytics Solutions Portfolio"

#TraderTerminology: ►Feature Engineering◄"Feature engineering is the art part of data science." - Sergey Yurgenson. "Coming up with features is difficult, time-consuming, and requires expert knowledge." - Andrew Ng

#TraderTerminology: ►Feature Engineering◄ is any expansion of the original data, by fixing, combining or normalizing it utilizing knowledge of the data. It is the most challenging and valuable technique in data science/Machine Learning.

Innovation happens at Deep Nexus with their AI for Trading Technology. Are Black Swans corrupting your Data Analysis? Sentiment, Alternative, and Fundamental data are merging on your quant’s desktop. The AI Talent Squeeze.
#AI #MachineLearning #AltData

#TraderTerminology: ►Data Preparation◄ Cleaning of the data, analysis of missing or incomplete records, noise, poorly formatted data. One bad data point or format may skew a Machine Learning model. Overlaps with Feature Engineering.

#TraderTerminology: ►Data Summary◄ Normally in the form of a Histogram, Count of Occurance or Frequent Values Chart. Gives you an overview of the data contained in each feature. Ie Titanic Sex : 453 male, 259 female.

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