There's a free open access book out now that covers advanced learning analytics methods. It's a solid find if you want to see how data science actually applies to educational tech.
The content gets into the weeds with machine learning and predictive analytics. It also explores natural language processing and how large language models fit into the picture.
You'll find specific sections on explainable AI and BERT. It looks like a useful reference for anyone building tools that need to provide personalized feedback or analyze student performance data.
xitter.cf/gp_pulipaka/status/209…
#MachineLearning #DataScience #NLP #LLM
Advanced Learning Analytics Methods — Free Open Access Book!
• Learning Analytics
• Artificial Intelligence
• Machine Learning
• Explainable AI
• Individualized Explainable AI
• Predictive Analytics
• Natural Language Processing
• Large Language Model
• BERT Language Models
• Complex Dynamic Systems
• Psychological Networks
• Recurrence Quantification Analysis
• Transition Network Analysis
• Precision Education
A comprehensive guide to advanced learning analytics combining AI, explainable machine learning, NLP, LLMs, complex systems and precision education, with step-by-step tutorial, code and datasets for applying modern analytical methods.
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
Free Complete Book — Springer:
geni.us/Advanced-Learning
Amazon Book:
geni.us/Advanced-Methods
Setting up a heavy orchestrator like Airflow is often overkill for simple automation tasks. If you just need to wrap your scripts and SSH commands in a proper workflow, dagu is a solid alternative. It is written in Go and comes as a single binary with a built-in Web UI.
The tool requires no external database or message broker to function. You define your workflows using declarative YAML files. It handles cron scheduling, retries, and parallel execution without forcing you to rewrite your business logic. It runs on Linux, macOS, and Windows.
You can trigger shell commands, Docker containers, or Kubernetes Jobs directly. The project even includes an MCP server for inspecting runs and applying changes via AI tools. It is a great fit for teams that want production grade scheduling on limited hardware resources.
github.com/dagucloud/dagu
#DevOps #Golang #SelfHosted #Workflow
There is a free open access book covering the intersection of data engineering and data science. It is a solid resource if you need to understand how these two fields actually fit together in a real production environment.
The text goes deep into the full lifecycle. You will find chapters on data integration and fusion plus specific sections on data governance. It covers the stuff that usually breaks when you try to move a model from a notebook to a server.
It also gets into the hardware and architecture side of things. There is detailed info on CPU and GPU setups along with MLOps pipelines and federated learning. It even digs into time series data and feature selection methods.
This is a great reference for anyone building ML pipelines or managing complex data flows. Having all these topics in one open source volume saves a lot of time spent hunting through documentation.
xitter.cf/gp_pulipaka/status/209…
#DataEngineering #DataScience #MLOps #MachineLearning
Data Engineering for Data Science — Free Open Access Book!
• Data Engineering
• Data Science Lifecycle
• Data Governance
• Data Integration
• Data Fusion
• Feature Selection
• Time-Series Data
• MLOps & ML Pipelines
• CPU–GPU Architectures
• Federated Learning
• Explainable Machine Learning
• Data Lakes
• Adversarial Learning
• Fraud Detection
• Approximate Inference
• Traffic & Trajectory Analytics
A comprehensive guide to the modern data engineering stack from governance, integration and scalable processing to MLOps, federated learning, explainable AI, data lakes and advanced analytics.
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
Free Complete Book — Springer:
geni.us/DataEngg-DSci
Mobile testing usually involves fighting with flaky drivers and complex setups. Maestro changes that by using a single binary that works across Android, iOS, and web. It drives apps through the accessibility layer and uses simple YAML files to define every test flow.
The project includes a built-in MCP server so you can connect it to coding agents like Claude or Cursor. This allows your agent to control a live device, inspect the screen, and verify its own work. It handles the taps and scrolls while you focus on the logic.
It is framework agnostic and works for React Native, Flutter, or native builds. You can run these flows on emulators, physical devices, or browsers. There is also a visual IDE called Maestro Studio for building tests without writing code manually.
github.com/mobile-dev-inc/Ma…
#MobileDev #SoftwareTesting #OpenSource #Kotlin
Expo just dropped a repo called expo/skills to help AI agents handle mobile dev workflows. It gives agents focused knowledge on Expo APIs and EAS constraints so they stop hallucinating mobile architecture.
You can use the tool to generate native-feeling Expo Router screens or set up EAS workflows for pull request previews. It also helps with SDK upgrades and monitoring EAS Update rollouts for health checks.
It works with Claude Code as a plugin or via a CLI for agents like Cursor and Windsurf. You just install the skills in your project root and your agent picks up the context.
The project is basically a bridge between the official docs and your LLM. It helps agents apply React Native and Android or iOS rules correctly without you having to copy-paste documentation constantly.
github.com/expo/skills
#Expo #ReactNative #AI #MobileDev
There's a massive batch of paid technical courses currently available for free to the first 4500 people who sign up. It covers the core stack most of us are trying to master right now.
You can find tracks for Python, data science, and big data. They also included materials for AWS certification and ethical hacking if you're looking to branch out into infrastructure or security.
The AI specific section is pretty deep. It covers prompt engineering, machine learning, and specific workflows for Claude, ChatGPT, and Grok. Grab these before the seat limit hits.
xitter.cf/Jerin_AI/status/209856…
#Python #MachineLearning #AWS #ArtificialIntelligence
All Paid Courses (Free for First 4500 People)
𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1)
1. Artificial Intelligence
2. Machine Learning
3. Prompt Engineering
4. Claude,Chatgpt,Grok
5. Data Analytics
6. AWS Certified
7. Data Science
8. BIG DATA
9. Python
10. Ethical Hacking
(72 Hours only )
Like + RT + comment ' Drive '
Must Follow me so I can DM you.
Managing IT infrastructure and service desks usually requires a pile of expensive licenses. GLPI is a free alternative that handles asset management, data center tracking, and ITIL service desk tasks in one place. It covers everything from software auditing to hardware lifecycles.
The tech stack is straightforward for most web devs. It runs on PHP and needs a standard web server like Nginx or Apache. For the database, you will need MariaDB 10.11 or MySQL 8.0 at a minimum. It relies on standard PHP extensions like bcmath for QR codes and curl for remote inventory agents.
It includes native dynamic inventory management and full support for incident, problem, and change management workflows. There is also a built-in knowledge base and contract tracking system to keep your documentation and vendor relationships organized.
github.com/glpi-project/glpi
#OpenSource #ITSM #SysAdmin #PHP
Building a game solo with AI often leads to messy code and zero structure. This new project turns a single Claude Code session into a full studio environment. It uses Shell scripts to organize your workflow just like a professional dev team.
The setup includes 49 specialized agents and 73 unique skills. It mimics a real studio hierarchy with directors to guard the vision and specialists for hands-on tasks. You get dedicated agents for design review and QA to catch mistakes early.
It relies on custom slash commands and a specific project structure to keep everything coordinated. The system adds quality gates so you don't end up with spaghetti code or broken logic. It’s a solid way to manage complex game projects without losing control.
github.com/Donchitos/Claude-…
#GameDev #ClaudeCode #AI #SoftwareArchitecture
Building a career as an AI engineer feels overwhelming because the field moves so fast. Most people get stuck trying to learn every single thing at once. The secret is following a structured roadmap that starts with basic programming instead of diving straight into complex models.
You need a solid foundation in Python and object oriented programming before you touch anything else. Managing your code with Git and platforms like GitHub is also non negotiable. It makes your workflow predictable and lets you collaborate on real projects.
Don't skip these fundamentals to chase the latest hype. Mastering the core tools first will make learning the actual AI concepts much easier later on. It is about building a path from total beginner to someone who can ship real software.
xitter.cf/AdarshChetan/status/20…
#AI #Python #SoftwareEngineering #CareerPath
AI Engineer Roadmap: From Beginner to Building Real-World AI
Want to become an AI Engineer but don’t know where to start?
Don’t try to learn everything at once. Follow a structured path 👇
1️⃣ Programming Fundamentals
Start with the foundation:
* Python basics & OOP
* Git & GitHub
* SQL
* Data Structures & Algorithms
2️⃣ Mathematics for AI
Build enough mathematical intuition to understand models:
* Linear Algebra
* Calculus
* Probability
* Statistics
* Optimization
3️⃣ Data Analysis
Learn how to work with real-world data:
* NumPy & Pandas
* Data Cleaning
* Data Visualization
* Exploratory Data Analysis (EDA)
4️⃣ Machine Learning
Understand how machines learn from data:
* Linear & Logistic Regression
* Decision Trees & Random Forest
* SVM
* K-Means & PCA
* Feature Engineering
* Model Evaluation
* Hyperparameter Tuning
5️⃣ Deep Learning
Move from traditional ML to neural networks:
* Neural Networks
* Activation & Loss Functions
* Forward & Backpropagation
* Optimizers
* TensorFlow / PyTorch
6️⃣ Computer Vision
Learn how AI understands images:
* OpenCV
* CNNs
* Image Classification
* Object Detection
* Segmentation
* Transfer Learning
7️⃣ NLP
Teach machines to work with language:
* Text Preprocessing
* Tokenization
* Embeddings
* RNNs & LSTMs
* Transformers
* BERT & GPT Models
8️⃣ Generative AI
This is where modern AI development gets exciting:
* LLM Fundamentals
* Prompt Engineering
* Embeddings
* RAG
* Vector Databases
* Fine-Tuning
* AI Agents
* Multi-Agent Systems
9️⃣ AI Frameworks & Tools
Turn concepts into applications:
* LangChain
* LangGraph
* LlamaIndex
* Hugging Face
* Ollama
* OpenAI APIs
* Gemini APIs
🔟 MLOps & Deployment
A model isn't useful if you can't ship it:
* FastAPI
* Streamlit
* MLflow
* Model Monitoring
* CI/CD
* AWS / GCP / Azure
1️⃣1️⃣ Build Projects
Projects are the most important part.
Build things like:
* AI Chatbot
* RAG Application
* AI Resume Analyzer
* Recommendation System
* Image Classification App
* AI Agent
* Multi-Agent Application
* End-to-End AI Product
1️⃣2️⃣ Advanced Topics
Once your fundamentals are strong:
* LLM Fine-Tuning
* LoRA & QLoRA
* Model Quantization
* Reinforcement Learning
* Distributed Training
* AI Safety
-🎯 The ultimate learning path
Python → Math → Data Analysis → Machine Learning → Deep Learning → NLP → Generative AI → AI Agents → MLOps → Deployment → Real-World AI Products
The goal isn't to collect certificates.
The goal is to build. Ship. Iterate. Repeat. 🚀
Save this roadmap and use it as your AI Engineering checklist.
Do Follow @Adarschetan on LinkedIn & X for more AI roadmaps, tools, and practical content.
NVIDIA just dropped Dynamo, a distributed inference serving framework written in Rust. It handles LLM serving across multiple GPUs or nodes at datacenter scale. The project focuses on efficiency by disaggregating prefill and decode steps so you can scale them independently.
One of the standout features is KV-aware routing. This helps you avoid redundant prefill computations by sending requests to the right nodes. It also includes a Kubernetes Inference Gateway plugin that handles this routing logic directly inside your standard K8s setup.
The framework aims to hit latency SLAs while keeping your total cost of ownership low. It supports fast cold-starts for new replicas and automatic scaling. If you need to coordinate complex inference workloads across a large cluster, this is worth a look.
github.com/ai-dynamo/dynamo
#Rust #LLM #Kubernetes #Inference
