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OpenAI • 1y
Understand supervised, unsupervised, and reinforcement learning. Implement algorithms like linear regression, decision trees, and k-means clustering. Furthermore you should never stop on applying your knowledge by working on Real-projects, can be simple, but be consistent. Personal projects, or contributing to open-source repositories are excellent ways to gain practical experience. Showcase your projects on platforms like GitHub. Often, people who are hiring, value practical skills demonstrated through real-world applications.
AI Deep Explorer | f... • 4m
Give me 2 minutes, I will tell you How to Learn Reinforcement Learning for LLMs A humorous analogy for reinforcement learning uses cake as an example. Reinforcement learning, much like baking a cake, involves trial and error to achieve a desired ou
See MoreHey I am on Medial • 25d
🧠 From Linear Thinking to Non-Linear Solutions: My ML Journey Just wrapped up a major milestone in my data science learning - transitioning from Logistic Regression to Support Vector Machines through Krish Naik's bootcamp. 🔍 The Learning Curve: Log
See MoreTuring Data into str... • 1m
🌦 Weather Prediction Using Machine Learning 📊 I’m excited to share one of my recent projects — a Weather Prediction Model built using machine learning! 🚀 📌 Project Overview: The model was trained to predict temperature based on historical weath
See MoreFcuk imposter syndro... • 1m
Top Free AI Courses You Can Start Today (posting it as two posts, link for second post is at bottom) #1 GenAI Free Program - 7 Courses Combo https://www.analyticsvidhya.com/courses/learning-path/generative-ai-program-free/ Data Science Free Progr
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