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Here's what i found out while i googled Master Python: It's the go-to language for AI, so get comfortable with its basics first. Learn ML Fundamentals: Understand concepts like data types, algorithms (linear regression, decision trees), and how to evaluate models. Dive into Libraries: Start using popular tools like scikit-learn for traditional ML, then explore TensorFlow or PyTorch for deep learning. Hands-On Projects: Build small models, starting simple, using datasets from Kaggle or similar platforms. Utilize Online Resources: Take free courses (Coursera, edX, fast.ai) and join communities for guidance and collaboration.
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Excited to dive into Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow Machine learning is evolving rapidly, and this book is a goldmine for anyone looking to build intelligent systems using the latest tools and frameworks. From Scikit
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Just completed a deep dive into how neurons and layers in neural networks mirror linear and logistic regression and how TensorFlow simply automates the math we've already learned. I revisited core concepts from linear and logistic regression and saw
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Hiring AI/ML Engineer 🚀 Join us to shape the future of AI. Work hands-on with LLMs, transformers, and cutting-edge architectures. Drive breakthroughs in model training, fine-tuning, and deployment that directly influence product and research outcom
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Hi everyone , if you are a beginner in Data Science or wish to start , then this post is for you. Linear Regression is one of the first things you need to learn in ML. It is not just about the code . Code is not the important thing, you can make Chat
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🌦 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
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