🔗 ML Libraries

🔗 5 Trending AI Jobs You Can’t Miss in 2025! 🤖

💻 Machine Learning Engineer
👉🏻 Average Salary: $114,000
👉🏻 What They Do: Design and implement ML algorithms while collaborating with data scientists and engineers. 📊

📊 Data Scientist
👉🏻 Average Salary: $120,000
👉🏻 What They Do: Analyze data, build predictive models, and drive data-backed decisions. 📈

🔬 AI Research Scientist
👉🏻 Average Salary: $126,000
👉🏻 What They Do: Explore the future of AI by testing algorithms and driving innovation. 🌟

🤝 AI Ethic
👉🏻 Average Salary: $135,000
👉🏻 What They Do: Promote ethical AI development, address biases, and ensure fairness. 🌐

📈 AI Product Manager
👉🏻 Average Salary: $140,000
👉🏻 What They Do: Manage AI products for success, focusing on innovation and ethical impact. 🛠

🔅 Hands-On PyTorch Machine Learning

🌐 Author: Helen Sun
🔰 Level: Intermediate

⏰ Duration: 56m

🌀 Discover the fundamentals of creating machine learning models with PyTorch, the open-source machine learning framework.

📗 Topics: PyTorch, Machine Learning

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📂 Full denoscription

Many of the worlds most exciting and innovative new tech projects leverage the power of machine learning. But if you want to set yourself apart as a data scientist or machine learning engineer, you need to stay up to date with the current tools and best practices for creating effective, predictable models.In this course, instructor Helen Sun shows you how to get up and running with PyTorch, the open-source machine learning framework known for its simplicity, performance, and APIs. Explore the basic concepts of PyTorch, including tensors, operators, and conversion to and from NumPy, as well as how to utilize autograd, which tracks the history of every computation recorded by the framework. By the end of this course, youll also be equipped with a new set of skills to get the most out of Torchvision, Torchaudio, and Torchtext.

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📱Hands-On PyTorch Machine Learning

🔗 Instill Core is a universal tool for working with unstructured data.

This open-source project offers a comprehensive solution for ETL processing, data preparation for AI, and deployment of LLM models. The platform combines document, image, and video processing into a single workflow, which is especially valuable for RAG scenarios and building AI pipelines.

Instill Core can be easily integrated into existing systems via the Python/TypeScript SDK or CLI. Local execution is possible via Docker, and ready-made recipes allow you to quickly deploy PDF parsing, web scraping, or image segmentation.

🔗 GitHub

🔗 AI Engineer Roadmap

🔗 What is an AI Agent?

An AI agent is a software program that can interact with its environment, gather data, and use that data to achieve predetermined goals. AI agents can choose the best actions to perform to meet those goals.

Key characteristics of AI agents are as follows:

An agent can perform autonomous actions without constant human intervention. Also, they can have a human in the loop to maintain control.

- Agents have a memory to store individual preferences and allow for personalization. It can also store knowledge. An LLM can undertake information processing and decision-making functions.

- Agents must be able to perceive and process the information available from their environment.

- Agents can also use tools such as accessing the internet, using code interpreters and making API calls.

- Agents can also collaborate with other agents or humans.

Multiple types of AI agents are available such as learning agents, simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.

A system with AI agents can be built with different architectural approaches.

1 - Single Agent: Agents can serve as personal assistants.
2 - Multi-Agent: Agents can interact with each other in collaborative or competitive ways.
3 - Human Machine: Agents can interact with humans to execute tasks more efficiently.

🔗 Machine learning Project ideas

🔅 Design to Code: Using AI to Build Faster

🌐 Author: Drew Falkman
🔰 Level: Intermediate

⏰ Duration: 1h 21m

🌀 Learn about the artificial intelligence tools that can improve and speed up your design process.

📗 Topics: Artificial Intelligence for Design, Software Development, Artificial Intelligence

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📂 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.

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📱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