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Passionate about lea...Ā ā¢Ā 2m
š Day 1 of My Machine Learning Journey Machine Learning isnāt magic ā itās built on three strong pillars š š¹ 1. Mathematics You donāt need to be a professor in math ā Just a clear understanding of these core concepts is enough: ā Linear Algebra ā
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Day 2/100 : free AI resources sharing topic of day : machine learning Books āMachine Learning For Absolute BeginnersĀ by Oliver Theobald https://mrce.in/ebooks/Machine%20Learning%20for%20Absolute%20Beginners.pdf āHands-On Machine Learning with
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The ultimate AI/ML roadmap for beginners š š š®ššµš What to learn: ⢠Linear Algebra ⢠Calculus ⢠Statistics Resources: ⢠Practical Statistics for Data Science( https://amzn.to/446czl5 ) ⢠Mathematics for Machine Learning( https://amzn.to/441s
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šššÆšš„ šš© ššØš®š« šš šš¤š¢š„š„š¬ š¢š§ šššš ā šš”š šš§š„š² ššš¬šØš®š«ššš¬ ššØš®āš„š„ šššš AI skills are šš¬š¬šš§šš¢šš„ in 2025 as they enhance job security and productivity across industries, with employers increasingly
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AI Resources for Beginners Courses: 1. Stanford CS229: Machine Learning Full Course taught by Andrew NG also you can try his website DeepLearning. Al - https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU 2. Convolutional Neur
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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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