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Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing, but aligning your model with real-world business metrics? That’s where the real challenge begins. Learn how to bridge the gap between models and impact: https://hubs.la/Q0
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Agentic AI, taught by Andrew Ng, is now available worldwide. Learn to design and evaluate AI systems that plan, reflect, and collaborate across multiple steps, built in raw Python. Available exclusively at DeepLearning.AI. Enroll now 👉 https://hubs.la/Q03My0HX0 | DeepLearning.AI
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I didn't study a lick of AI in college.I had studied computer science at MIT, but because I had been interning and working in quant trading, it was much more statistically focused than anything related to machine learning.Joining Exa AI early was my first exposure, and I still appreciate Jeff Wang for his patience, especially in the early days when I had never even heard of RAG.For anyone else looking to get started, I would really recommend watching Andrei Karpathy's 'intro to LLMs' 1hr compreh
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