AI and Machine Learning

📂 Full description In this course, MLOps expert Noah Gift covers small language models, their advantages, and how to run them locally using the llamafile tool. Plus, get useful demos of the Phi llamafile and the Lava llamafile. This course was created by Noah Gift. We are pleased to host this training in our library.

🔅 Small Language Models and LlamaFile 🌐 Author: Noah Gift 🔰 Level: Intermediate ⏰ Duration: 11m

🌀 Explore small language models, their advantages, and how to run them locally.

📗 Topics: LLaMA, Large Language Models, Natural Language Processing 📤 Join Artificial intelligence for more courses

Two to three years until "AI systems are better than humans at almost everything... then eventually better than all humans at everything," says Anthropic CEO.

🔰 Create a Pencil Sketch Filter in Python ✏️ A quick guide to image processing with OpenCV (CV2). The Pipeline: Original Image → Grayscale → Inverted Image → Blurred Invert → Final Sketch

By blending the grayscale and blurred invert layers, we simulate the effect of a hand-drawn sketch. A simple yet powerful technique!

Ideal for beginners looking to dive into computer vision.

# Importing the Required Moduel
# pip install opencv-python
import cv2 as cv

# Reading the image
# Replace this image name to your image name
image = cv.imread("avatar.jpg")

# Converting the Image into gray_image
gray_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)

# Inverting the Imge
invert_image = cv.bitwise_not(gray_image)

# Blur Image
blur_image = cv.GaussianBlur(invert_image, (21,21), 0)

# Inverting the Blured Image
invert_blur = cv.bitwise_not(blur_image)

# Convert Image Into sketch
sketch = cv.divide(gray_image, invert_blur, scale=256.0)

# Generating the Sketch Image Named as Sketch.png
cv.imwrite("Sketch.png", sketch)

#Python #OpenCV #ComputerVision #Coding #AI

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💠 The Best Tool for Extracting Data from PDF Files! 👩🏻‍💻 Usually, PDF files like financial reports, scientific articles, or data analyses are full of tables, formulas, and complex texts. ⬅️ Most tools only extract texts and destroy the data structure, causing important information to be lost. ✅ But the tool Docling uses artificial intelligence to preserve all those structures (text, tables, formulas) exactly as they are in the file. Then it converts that data into a structured format. Meaning AI models can work on them. ⭕ The interesting point is that with just three lines of Python code, you can convert any PDF into searchable data! ┌ 🥵 Docling ├ 🔎 Article ├ 📄 Documentation └ 🐱 GitHub-Repos

🧠 Theirwork = AI helps to find your freelancer in seconds Tired of spending 3–10 hours picking freelancers — and 2 out of 3 don’t really fit? Or don’t even know where to start looking? We built Theirwork:  - A fully free, AI-first hiring assistant (not a job board) - No “HR” obstacles, no endless chats or vague profiles - AI does 95% of the heavy lifting: brief analysis, skill mapping, ranking 🔬 Benchmark: Manual search = 5–15 hours + 40% mismatch Theirwork = 2 minutes ⚡ 80–90% accuracy, totally automatic We are here to change the norms in freelance sourcing — and need your feedback to make it better. 👉 Give it a go (no signups, no fees): https://ubZirZ.short.gy/qm9h3B

Google published a 150-page report on Health AI Agents - 7,000 annotations, 1,100+ hours of expert work. But the main thing is not the metrics, but the new design philosophy. Instead of a monolithic *"Doctor-GPT"*, Google is creating a Personal Health Agent (PHA) - a system of three specialized agents: - Data Science Agent - analyzes wearable devices and lab data - Domain Expert Agent - verifies medical facts and knowledge - Health Coach Agent - conducts dialogue, sets goals, adds empathy 🧩 Everything is connected by an orchestrator with memory: user goals, barriers, insights. ⚡️ Results - Outperformed baseline models on 10 benchmarks - Users preferred PHA over regular LLMs (20 participants, 50 personas) - Experts rated answers 5.7–39% better on complex medical queries ⚙️ Design principles - Consider all user needs - Adaptively combine agents - Do not ask for data that can be inferred - Minimize latency and complexity 🧠 Tested scenarios - General health questions - Data interpretation (wearables, biomarkers) - Advice on sleep, nutrition, activity - Symptom assessment (without diagnosis) ⚠️ Limitations and future - Slower than single agents (244 s vs. 36 s) - Need bias audits, data protection, and regulatory compliance - Next step - adaptive communication style: empathy ↔️ responsibility 💡 Conclusion Google shows the way forward: not a "super doctor bot," but modular, specialized agent teams. Medicine is just the first test. Next: finance, law, education, science. Google 150 Health AI Agents: https://arxiv.org/pdf/2508.20148

📂 Full description In this course, Axel Sirota introduces Retrieval-Augmented Generation (RAG) as a powerful technique for enhancing the capabilities of Large Language Models (LLMs). Learn the foundational concepts and practical applications of RAG, focusing on creating chatbots and decision support systems across various domains. Using the MIMIC-III dataset to create a healthcare chatbot that can answer questions or suggest a diagnosis as an example, get hands-on experience in building RAG systems with TensorFlow, Keras, and HuggingFace. By the end of the course, you will be equipped to deploy RAG solutions that integrate robust retrieval mechanisms with generative models, applicable in fields like healthcare, legal, and customer service.

🔅 Building a RAG Solution from Scratch 🌐 Author: Axel Sirota 🔰 Level: Intermediate ⏰ Duration: 2h 53m

🌀 Learn to design, implement, and optimize RAG systems for chatbots and decision support, while exploring current research and ethical considerations.

📗 Topics: Retrieval-Augmented Generation, Generative AI, Artificial Intelligence 📤 Join Artificial intelligence for more courses

📺 12 comprehensive playlists to master ⬅️ machine learning, deep learning, and GenAI!

👨🏻‍💻 Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.

😉 Machine Learning Basics (39 videos) 😉 Python for ML (9 videos) 😉 Optimization for ML (5 videos) 😉 Machine Learning with Practical Exercises (37 videos) 😉 Building Decision Trees from Scratch (13 videos) 😉 Building Neural Networks from Scratch (35 videos) 😉 Graph Neural Networks (6 videos) 😉 Computer Vision from Scratch (19 videos) 😉 Building LLM from Scratch (43 videos) 😉 Reasoning in LLMs from Scratch (22 videos) 😉 Building DeepSeek from Scratch (29 videos) 😉 Machine Learning in Production Environment (6 videos)

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📚 AI Engineering: Building Applications with Foundation Models 1st Original Price: 57$

🧠 Examples and Guides for DeepMind Gemini Models

The repository contains small examples, code snippets, and guides demonstrating experiments with Google's DeepMind Gemini models. Here you will find useful samples for integrating and using various Gemini features, including working with the OpenAI SDK and Google Search.

📖 Highlights: - Examples of using Gemini with OpenAI and Google Search - Guides on functions and agents - Scripts for browser interaction and content generation - Integration with LangChain and PydanticAI 🔗 GitHub: https://github.com/philschmid/gemini-samples

🔗 Master AI in 2025

AI isn’t one big leap, it’s a series of steps - Python, ML, Deep Learning, NLP, and then the world of Generative AI.

This roadmap gives you the base.