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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/441selL ) ⢠Essence of Linear Algebra (3Blue1Brown) ⢠Brilliant & Khan Academy š£šššµš¼š» What to learn: ⢠Native data structures (dictionaries, lists, sets, and tuples) ⢠For and while loops ⢠If-else conditional statements ⢠Functions and classes ⢠Numpy, Pandas, Sci-Kit Learn Resources: ⢠W3Schools Python Course (free) ⢠Python for Everybody Specialization (Coursera) ⢠Machine Learning with Python and Scikit-Learnā-āFull Course šš®šš® šššæšš°šššæš²š š®š»š± š®š¹š“š¼šæš¶ššµšŗš What to learn: ⢠Arrays & Linked Lists ⢠Trees & Graphs ⢠HashMaps, Queues & Stacks ⢠Sorting & Searching Algorithms ⢠Dynamic Programming Resources: ⢠Neetcode.io ⢠Leetcode ⢠Hackerrank š š®š°šµš¶š»š² š¹š²š®šæš»š¶š»š“ What to learn: ⢠Linear, logistic and polynomial regression ⢠Decision trees, random forests and gradient-boosted trees ⢠Support vector machines ⢠K-means and K-nearest neighbour clustering ⢠Feature engineering ⢠Evaluation metrics ⢠Regularisation, bias vs variance tradeoff and cross-validation Resources: ⢠Machine Learning Specialisation by Andrew Ng ⢠The Hundred-Page ML Book ⢠Hands-On ML with Scikit-Learn, Keras, and TensorFlow ( https://amzn.to/4jCmP92) ⢠The Elements of Statistical Learning ( https://amzn.to/44aTjTH ) šš š®š»š± š±š²š²š½ š¹š²š®šæš»š¶š»š“ What to learn: ⢠Neural Networks ⢠Convolutional and Recurrent Neural Networks ⢠Transformers ⢠RAG, Vector Databases, LLM Fine Tuning ⢠Reinforcement Learning Resources: ⢠Deep Learning Specialization by Andrew Ng ⢠Introduction to LLMs by Andrej Karpathy ⢠Neural Networks: Zero to Hero ⢠Reinforcement Learning Courseā-āLectures by David Silver š šš¢š½š What to learn: ⢠Cloud technologies like AWS, GCP or Azure ⢠Docker and Kubernetes ⢠How to write production code ⢠Git, CircleCI, Bash/Zsh Resources: ⢠Practical MLOps (https://amzn.to/4iDPgCY ) ⢠Designing Machine Learning Systems ( https://amzn.to/4iwZ2q5 )
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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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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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My Favorite AI & ML Books That Shaped My Learning Over the years, Iāve read tons of books in AI, ML, and LLMs ā but these are the ones that stuck with me the most. Each book on this list taught me something new about building, scaling, and underst
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Old is Gold: Deep Learning Classics In the fast-paced world of AI, itās easy to overlook the timeless gems that laid the foundation for modern deep learning. Hereās a curated list of classic, high-quality courses taught by pioneers of the field tha
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AI Resources for Beginners Books: 1. Deep Learning. Illustrated Edition. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2. Mathematics for Machine Learning. Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. 3. Reinforcement learning, An Introd
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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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The AI Legends #75 Days Day 23: Andrew Ng Andrew Ng is best known for his significant contributions to artificial intelligence (AI) and machine learning. He co-founded and led Google Brain, a deep learning research project at Google, which helped pro
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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.Ā Reinforcement learning, much like baking a cake, involves trial and error to achieve a desired ou
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