BDQ: Break into AI/ML

🔥This is a new era we live in 🥲 Open-source Auto-GPT: Task-Driven Autonomous AI Agents 🤖 Different forms of these AI agents have been developed in the past couple of weeks. Three trending AutoGPT Projects: 🔖 Clone them on GitHub: Significant Gravitas’ Auto-GPT Mini Yohei’s BabyAGI 🤗 Microsoft’s JARVIS An alternative to having Auto GPT locally is AgentGPT, a web UI for AutoGPT, (yes needs your OpenAI API key). - It can assign tasks to itself - Can search the web unattended - It can improve itself - Agents collaborate to complete tasks - Long+short-term “memory” management - It’s experimental & open-source - They are based on GPT-4 Qs? Ask away! #bigdataqueen #autogpt #chatgpt #gpt4 #nlproc #ai #generativeai #machinelearning #datascience #computerscience #artificialintelligence #techtok

✨ Your Ultimate Data Science Roadmap ✨ Lets first see how this roadmap is different from all other roadmaps you see 🔥 👉 Other roadmaps might say: start with math and stats in the beginning, and holding on to real-life practice until you get the fundamentals! 🚫⛔️ You’ll feel overwhelmed and frustrated before you even got started. 👎 👉 Others might say: “don’t worry about math in the beginning, start with open source models, get your hands dirty, then back to math only when needed” 🚫⛔️ you’ll not be a competent scientist as a result, and you can’t debug modeling issues when they arise without linear algebra, calculus and inferential stats knowledge 👎 Now here comes BDQ roadmap 🚀 Your learning has two parallel tracks: 🔹Machine Learning Theory Stack 🔹Coding Practical Stack 🥇In parallel you’ll learn: 1. math/stats 🤝 data structures and algos 2. inferential stats 🤝 Python 101 3. ML theory 🤝 Python ML libraries 4. DL theory 🤝 DL Python libraries) I am prepping a comprehensive reel on all math/stats requirements ✅ 1️⃣ Main conventional ML algos, which are the absolute pre-requisites to DL. (Jumping straight to DL is not BDQ approved) 🔸Linear Regression 🔸Logistic Regression 🔸Decision Tree 🔸Random Forest 🔸SVM 🔸Naive Bayes 🔸kNN 🔸K-Means 🔸Boosting techniques 🔖 Learn these from: 👉 Machine Learning specialization by university of Washington: https://www.coursera.org/specializations/machine-learning (Optional) Move on to basics of DL algos: 🔸Basics of neural networks, starting from perceptrons 🔸CNNs, RNNs/LSTMs 🔸If NLP focus: Transformers and generative AI 🔖 Learn the basics from: 👉 Intro to Deep Learning by MIT: http://introtodeeplearning.com For coding you’ll need: 🔖 Data structures and algorithms 🔖 Python Data analytics libraries: NumPy, Pandas 🔖 Python ML and DL libs: SkLearn, PyTorch/TensorFlow We will dive deep into coding stack and generative AI in the upcoming posts. #bigdataqueen #datascience #machinelearning #datascientist #dataanalytics #datasciencetok #dataanalysis