10 Weeks, 20 Lessons, Data Science for All!
This course is designed to guide beginners through the exciting world of Edge AI, covering fundamental concepts, popular models, inference techniques, device-specific applications, model optimization, and the development of intelligent Edge AI agents.
This project presents a comparative machine learning framework for heart attack risk prediction using Python. It includes data preprocessing, feature engineering, SMOTE for class balancing, cross validation, and performance evaluation of multiple ML models.
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Financial Sentiment Analysis with BERT
Aircraft design optimization made fast through computational graph transformations (e.g., automatic differentiation). Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
Microsoft Azure Traces
Repository for threat hunting and detection queries, etc. for Defender for Endpoint and Microsoft Sentinel in KQL(Kusto Query Language).
Samples and data for Microsoft Fabric Learn content
User friendly and accurate binder design pipeline
Segment Anything for Microscopy
NCBI Datasets is a new resource that lets you easily gather data from across NCBI databases.
A free, self-paced 24-week AI engineering course: Python, machine learning, LLMs, RAG, fine-tuning, agents and MCP, Azure and Vertex and Bedrock, and Databricks. 43 runnable notebooks, one continuous case study. MIT licensed, no signup. By Zorost Intelligence AI Lab.
LLM-powered multiagent persona simulation for imagination enhancement and business insights.
This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
βTextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.
Official Implementation of "KBLaM: Knowledge Base augmented Language Model"
ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
Dynamics 365 FastTrack Implementation guides
Hands-on Microsoft Fabric Data Science labs (FreshMart churn) with Thai study notes, PySpark/MLflow, and DP-604-aligned learning path.
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PromptForge scores LLM prompts across seven quality dimensions, then rewrites weak prompts into clear, intent-preserving instructions
One Power BI template for Microsoft Copilot credit consumption and cost - Cowork/Work IQ, Copilot Studio, GitHub Copilot and Azure AI Foundry.
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T