DAMA South Florida - (Forming Chapter)

AI in a Nutshell 1. Artificial General Intelligence (AGI) – Theoretical AI that mimics human intelligence, capable of reasoning, learning, and self-improvement. 2. Explainable AI (XAI) – Ensures AI decisions are interpretable, bridging the gap between machine predictions and human understanding. 3. Artificial Intelligence (AI) – Technologies that enable machines to think, learn, and act like humans in various applications. 4. Machine Learning (ML) – AI systems that improve through experience, utilizing supervised, unsupervised, and reinforcement learning techniques. 5. Neural Networks – Computational models inspired by biological brains, enabling pattern recognition and complex decision-making. 6. Deep Learning – Advanced ML using hierarchical networks to process vast data, powering AI applications like speech recognition and image analysis. 7. Generative AI – AI models that generate text, images, and other creative content, leveraging language models, transformers, and self-attention mechanisms. [Explore more in the post]

I’m 53. I've been doing analytics for 13+ years. Here are 8 no-BS steps I've learned to build DIY data science skills: 1) Crawl-Walk-Run Social media would lead you to believe that you must: Work with gigantic datasets. Use deep neural networks, LLMs, etc. Be an advanced mathematics wizard to do anything. It's simply not true.? Here's what you do... 2) Crawl - learn decision trees The dirty little secret of business analytics is that you usually use simple techniques. Prime example - decision tree machine learning: Works excellent with data tables. You can learn them using your intuition. When you're ready, you can learn the math. 3) Crawl - Use simple datasets When you first start, do yourself a favor and use simple datasets. While they get a lot of hate on social media, you want to concentrate on how decision trees learn from data. That's much easier when you use simple datasets. Worry about data wrangling later. 4) Walk - build ML fundamentals Once you build your intuitive understanding of decision trees, it's time for building skills with: Data profiling Feature engineering The bias-variance tradeoff Tuning decision tree models With these skills you're ready for... 5) Walk - the mighty random forest As the name suggests, random forests are collections of decision trees. In ML, this is known as an "ensemble." Ensembles of decision trees are state-of-the-art for real-world DIY data science. Next up, it's about the data. 6) Walk - data wrangling "Data wrangling" is a term for all the work needed to prepare data for DIY data science. Here's the thing, though. Building data wrangling skills is much easier when you know ML. This knowledge provides the context for what you need to do with the data. Moving on... 7) Run - apply it at work While I'm a big fan of using Kaggle to build initial skills for crawling and walking, Nothing beats applying what you've learned at work. Even if nobody ever sees it, the experience you will build is invaluable. P.S. - Don't do anything that will get you fired. 8) Run - expand With some work projects under your belt, it's time to expand your skills. Start with cluster analysis: K-means DBSCAN A powerful combination for DIY data science is cluster analysis + ML models for interpretation. Be sure to use this combo at work! Ready to build DIY data science skills? Join 6,175 professionals learning Python and ML with my free crash courses: https://lnkd.in/e7fVrjxC

5 ???????? ?????????????? ?????????????? ???? ???????????? ????????????????, clearly explained (with visuals): . . Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration! The following visual depicts the 5 most popular design patterns employed in building AI agents. 1) ???????????????????? ??????????????: - The AI reviews its own work to spot mistakes and iterate until it produces the final response. 2) ???????? ?????? ?????????????? Tools allow LLMs to gather more information by: - Querying a vector database - Executing Python scripts - Invoking APIs, etc. This is helpful since the LLM is not solely reliant on its internal knowledge. 3) ?????????? (???????????? ?????? ??????) ?????????????? ReAct combines the above two patterns: - The Agent can reflect on the generated outputs. - It can interact with the world using tools. This makes it one of the most powerful patterns used today. 4) ???????????????? ?????????????? Instead of solving a request in one go, the AI creates a roadmap by: - Subdividing tasks - Outlining objectives This strategic thinking can solve tasks more effectively. 5) ??????????-?????????? ?????????????? - We have several agents. - Each agent is assigned a dedicated role and task. - Each agent can also access tools. All agents work together to deliver the final outcome, while delegating task to other agents if needed. I'll soon dive deep into each of these patterns, showcasing real-world use cases and code implementations. ?? Over to you: Which Agentic pattern do you find the most useful? -- If you want to learn AI/ML engineering, I have put together a free PDF (530+ pages) with 150+ core DS/ML lessons. Get here: https://lnkd.in/gi6xKmDc -- Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

Join us to discuss AI governance and risk management event on Tuesday, October 22nd at 11:30 AM. Don't miss out on this crucial discussion - secure your tickets now!? #AIGovernance #RiskManagement #DataGovernance #AIEvent?