Machine Learning with Python

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10 GitHub repos to build a career in AI engineering: (100% free step-by-step roadmap) 1️⃣ ML for Beginners by Microsoft A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets. Includes quizzes, lessons, and hands-on projects, with some videos. GitHub repo → https://lnkd.in/dCxStbYv 2️⃣ AI for Beginners by Microsoft This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter. Beginner-friendly, project-based, and full of real-world apps. GitHub repo → https://lnkd.in/dwS5Jk9E 3️⃣ Neural Networks: Zero to Hero Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper. This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs. GitHub repo → https://lnkd.in/dXAQWucq 4️⃣ DL Paper Implementations So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work. This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc. GitHub repo → https://lnkd.in/dTrtDrvs 5️⃣ Made With ML Now it’s time to learn how to go from notebooks to production. Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices. GitHub repo → https://lnkd.in/dYyjjBGb 6️⃣ Hands-on LLMs - You've built neural nets. - You've explored GPTs and LLMs. Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc. GitHub repo → https://lnkd.in/dh2FwYFe 7️⃣ Advanced RAG Techniques Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques. This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc. GitHub repo → https://lnkd.in/dBKxtX-D 8️⃣ AI Agents for Beginners by Microsoft After diving into LLMs and mastering RAG, learn how to build AI agents. This hands-on course covers building AI agents using frameworks like AutoGen. GitHub repo → https://lnkd.in/dbFeuznE 9️⃣ Agents Towards Production The above course will teach what AI agents are. Next, learn how to ship them. This is a practical playbook for building agents covering memory, orchestration, deployment, security & more. GitHub repo → https://lnkd.in/dcwmamSb 🔟 AI Engg. Hub To truly master LLMs, RAG, and AI agents, you need projects. This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship. GitHub repo → https://lnkd.in/geMYm3b6

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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability. 1️⃣ Machine Learning (ML) Interview Types of ML Q&A in Data Science Interview https://shorturl.at/syN37 ML Interview Q&A for Data Scientists https://shorturl.at/HVWY0 Crack the ML Coding Q&A https://shorturl.at/CDW08 Deep Learning Interview Q&A https://shorturl.at/lHPZ6 Top LLMs Interview Q&A https://shorturl.at/wGRSZ Top CV Interview Q&A [Part 1] https://rb.gy/51jcfi Part 2 https://rb.gy/hqgkbg Part 3 https://rb.gy/5z87be 2️⃣ SQL Interview Preparation 13 SQL Statements for 90% of Data Science Tasks https://rb.gy/dkdcl1 SQL Window Functions: Simplifying Complex Queries https://t.ly/EwSlH Ace the SQL Questions in the Technical Interview https://lnkd.in/gNQbYMX9 Unlocking the Power of SQL: How to Ace Top N Problem Questions https://lnkd.in/gvxVwb9n How To Ace the SQL Ratio Problems https://lnkd.in/g6JQqPNA Cracking the SQL Window Function Coding Questions https://lnkd.in/gk5u6hnE SQL & Database Interview Q&A https://lnkd.in/g75DsEfw 6 Free Resources for SQL Interview Preparation https://lnkd.in/ghhiG79Q 3️⃣ Programming Questions Foundations of Data Structures [Part 1] https://lnkd.in/gX_ZcmRq Part 2 https://lnkd.in/gATY4rTT Top Important Python Questions [Conceptual] https://lnkd.in/gJKaNww5 Top Important Python Questions [Data Cleaning and Preprocessing] https://lnkd.in/g-pZBs3A Top Important Python Questions [Machine & Deep Learning] https://lnkd.in/gZwcceWN Python Interview Q&A https://lnkd.in/gcaXc_JE 5 Python Tips for Acing DS Coding Interview https://lnkd.in/gsj_Hddd 4️⃣ Statistics Mastering 5 Statistics Concepts to Boost Success https://lnkd.in/gxEuHiG5 Mastering Hypothesis Testing for Interviews https://lnkd.in/gSBbbmF8 Introduction to A/B Testing https://lnkd.in/g35Jihw6 Statistics Interview Q&A for Data Scientists https://lnkd.in/geHCCt6Q 5️⃣ Probability 15 Probability Concepts to Review [Part 1] https://lnkd.in/g2rK2tQk Part 2 https://lnkd.in/gQhXnKwJ Probability Interview Q&A [Conceptual Questions] https://lnkd.in/g5jyKqsp Probability Interview Q&A [Mathematical Questions] https://lnkd.in/gcWvPhVj 🔜 All links are available in the GitHub repository: https://lnkd.in/djcgcKRT

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This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the reader’s intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well. Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too. Read it online: https://visionbook.mit.edu/

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Top 50 LLM Interview Questions! A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations. 👇👇👇👇👇👇

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New to Pandas? Here's a cheat sheet you can download (2025)

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