#MachineLearning for #AlgorithmicTrading — Predictive models to extract signals from market and alternative data for systematic trading strategies with #Python and for *other* #TimeSeries prediction applications Explore http://
amzn.to/47Vd6s8 by @ml4trading via
DATA
-
Machine Learning for Algorithmic Trading and Time-Series Prediction
By
–
-
Moving Beyond Traditional Statistics in the Age of AI
By
–
If You’re Still Worshipping Pearson Correlation, You’re Not a Data Scientist — You’re Driving a Horse Cart in the Age of AI: https://
valeman.medium.com/if-youre-still
-worshipping-pearson-correlation-you-re-not-a-data-scientist-you-re-driving-a-831dc0590de6
… My summary: Don’t just plug your data into some formula to find the answers to the questions you started with. Real Data -
New Book on Quantum Machine Learning and Optimization in Finance
By
–
Be future-ready with this book >> "Quantum #MachineLearning and Optimization in #Finance" (494 pages; 2nd Edition): http://
amzn.to/4lNlBt5 v/ @PacktDataML -
Mathematical Methods in Data Science and Machine Learning
By
–
Mathematical Methods in Data Science — Bridging Theory and Applications with Python: http://
amzn.to/4b7ZYQ4
——————
#ML #MachineLearning #DataScientist #DataScience #Mathematics #AI #Algorithms -
Tensor Decompositions for Machine Learning and Data Science
By
–
Tensor Decompositions for Data Science [and Computational Science]: http://
amzn.to/4s2B5g1
——
#ML #MachineLearning #DataScientist #DataScience #Mathematics
——
Note: Extensive background materials in linear algebra, optimization, probability, and statistics are included as -
Handbook of Data Science and AI for Business
By
–
The Handbook of Data Science and AI for Business — Generate Value from Data with Machine Learning and Data Analytics: http://
amzn.to/3Wc1Iiz -
Cohere Compass for Complex Document Analysis
By
–
Searching through unstructured data, such as scans of handwritten and typed declassified documents, can be challenging. However, with Cohere Compass, it becomes possible because it is designed to process and retrieve information from even the most complex documents. This includes the Compass Visual feature.
-
AI Storage: No Trade-Offs Between Cost and Workflow
By
–
AI teams shouldn’t have to choose between costly object storage and cumbersome git workflows. @huggingface
Storage is designed for model weights, datasets, checkpoints, and artifacts:
– simple per-TB pricing
– built-in CDN
– Xet deduplication
– private by default when needed Store -
Effective scaling laws for time series foundation models
By
–
Are scaling laws finally effective for time series foundation models? Today, @datadoghq is releasing Toto 2.0 weights under Apache 2.0 on @huggingface. It's a family of open-weights TSFMs ranging from 4M to 2.5B parameters, where each size outperforms the previous one from a single hyperparameter configuration.
-
Databricks Unveils No-Code Lakeflow Designer
By
–
Lakeflow Designer is a visual, no-code, AI-native way to prepare and analyze data directly on Databricks.
— Databricks (@databricks) 14 mai 2026
Data stays in place, governed by Unity Catalog from the start, with lineage and permissions intact. Every transformation generates production-ready Python code under the… pic.twitter.com/WUv1xXU5gaLakeflow Designer is a visual, no-code, AI-native solution for preparing and analyzing data directly on Databricks. Data remains in place, governed by Unity Catalog from the outset, with lineage and permissions preserved. Every transformation automatically generates production-ready Python code underneath.