📂 Full denoscription

It seems like new artificial intelligence tools are arriving every day, and even if youre interested in using AI in your work, figuring out where to start may seem like an overwhelming undertaking. In this course, Drew Falkman shows designers the ways that AI can help you to build designs faster, make your designs smarter and better, and even improve your dev handoff. Drew surveys the current tools like Figma, Magician, and Sprout, and details their strengths and weaknesses. He also looks at some full-featured design suites that can help you get to prototypes quickly, like Wondershare Mockitt, Visily, and Uizard. He explains how you can use these tools to go from paper sketch or screenshot to wireframe in seconds, or use a prompt to generate an entire prototype. Finally, he shows you how you can use AI to automate the process of turning your designs into code.

📱Artificial intelligence
📱Design to Code: Using AI to Build Faster

🔗 Creating Your AI For Business Roadmap: A Step-by-Step Guide

Identify Business Objectives: Understand how AI can help achieve your goals, whether it's through automation, predictive analytics, AI chatbots, or innovative product development.

Evaluate Data Infrastructure: AI needs quality data. Assess your data collection, storage, and cleanliness to ensure your AI initiatives can thrive.

Assemble a Skilled Team: Combine business insight, technical skills, and data science. Include business strategists, AI specialists, and IT professionals, or seek external expertise as necessary.

Choose Appropriate AI Technology: Select AI tools like ML, NLP, RPA, or Computer Vision, aligned with your business needs.

Prototype Development: Start small with a pilot project to address specific challenges, refining AI models based on performance.

Scale and Optimize: Expand successful prototypes, integrating them into broader business operations and continuously optimizing.

Implement Change Management: Develop strategies to assist your workforce in adapting to AI, including training and understanding AI benefits.

🔗 Life-cycle of Machine Learning Model

🔗 AI Agents

✅ An AI agent roadmap outlines the steps and skills needed to develop and deploy autonomous AI systems.

✅ This includes foundational skills in programming, AI/ML concepts, and data handling, progressing to more advanced topics like NLP, LLMs, and agentic frameworks.

✅ The roadmap also emphasizes practical experience through projects, community engagement, and potentially, internships or open-source contributions.

🔗 How to use Machine Learning to predict fraud

🔅 Introduction to Artificial Intelligence

🌐 Author: Doug Rose
🔰 Level: Beginner

⏰ Duration: 2h 26m

🌀 Get an overview of some of the latest tools and techniques in predictive and generative artificial intelligence (AI).

📗 Topics: Artificial Intelligence

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📂 Full denoscription

Computer scientists are just a small slice of people working in artificial intelligence (AI). Most people working with AI are just like you. Theyre professionals, teachers, and students who want to use AI to enhance their products, creativity, and career. AI has been around for over half a century. Despite huge advancements in predictive and generative AI, the core concepts of artificial intelligence are still accessible.This course is designed for project managers, product managers, directors, executives, and students starting a career in AI. First, learn what it means for a system to display “intelligence.” Then, explore the difference between classic predictive AI and newer generative AI. Next, youll get an overview of machine learning algorithms, artificial neural networks, foundation models, and deep learning. From the AI curious to the AI careerist, this course will help you get started with intelligent systems.This course is part of a Professional Certificate from Microsoft.This course is part of a Professional Certificate from Microsoft.

📱Artificial intelligence
📱Introduction to Artificial Intelligence

This repository contains a collection of everything needed to work with libraries related to AI and LLM.

More than 120 libraries, sorted by stages of LLM development:

→ Training, fine-tuning, and evaluation of LLM models
→ Integration and deployment of applications with LLM and RAG
→ Fast and scalable model launching
→ Working with data: extraction, structuring, and synthetic generation
→ Creating autonomous agents based on LLM
→ Prompt optimization and ensuring safe use in production

🔗 Link: https://github.com/Shubhamsaboo/awesome-llm-apps

🔗 How to use Machine Learning to predict fraud

1. Identify project objectives

Determine the key business objectives upon which the machine learning model will be built.
For instance, your goal may be like:

- Reduce false alerts
- Minimize estimated chargeback ratio
- Keep operating costs at a controlled level

2. Data preparation

To create fraudster profiles, machines need to study about previous fraudulent events from historical data. The more the data provided, the better the results of analyzation. The raw data garnered by the company must be cleaned and provided in a machine-understandable format.

3. Constructing a machine learning model

The machine learning model is the final product of the entire ML process.
Once the model receives data related to a new transaction, the model will deliver an output, highlighting whether the transaction is a fraud attempt or not.

4. Data scoring

Deploy the ML model and integrate it with the company’s infrastructure.

For instance, whenever a customer purchases a product from an e-store, the respective data transaction will be sent to the machine learning model. The model will then analyze the data to generate a recommendation, depending on which the e-store’s transaction system will make its decision, i.e., approve or block or mark the transaction for a manual review. This process is known as data scoring.

5. Upgrading the model

Just like how humans learn from their mistakes and experience, machine learning models should be tweaked regularly with the updated information, so that the models become increasingly sophisticated and detect fraud activities more accurately.

😎The AI that will 100% simplify your life!

🛠 Smithery AI – An AI platform for automating everyday tasks, compatible with various services.

🔰 The platform integrates 4,000 apps that will handle all your routine tasks:

🔹Connect the apps you want to give the AI assistant access to: code editors, GitHub, Slack
🔹 Ask the AI to automate any task
🔹 The Toolbox instantly directs the agent to the right tool, and voilà—task solved!

🔗 Links: https://smithery.ai

🔅 Building AI Applications with Amazon Bedrock

🌐 Author: Noah Gift
🔰 Level: Intermediate

⏰ Duration: 1h 7m

🌀 Learn how to build real-world AI applications using Amazon Bedrock.

📗 Topics: Amazon Bedrock, Artificial Intelligence, Application Development

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📱Artificial intelligence
📱Building AI Applications with Amazon Bedrock

🔗 Harbor — a local stack for working with LLM in one click.

This tool simplifies launching local language models and related services — from web interfaces to RAG and voice interaction. Everything runs in Docker and is configured with a couple of commands.

Harbor automatically integrates components, for example, SearXNG is immediately connected to Open WebUI for web search, and ComfyUI — for image generation. Suitable for those who want to quickly deploy a local environment for AI experiments.

🔗 GitHub