Machine Learning with Python

Master MCP: The Best Free Learning Resources 1️⃣ Everything you need to know about MCP: The first learning resource is a beginner-friendly introduction to MCP by Replit https://lnkd.in/djVD73Gz 2️⃣ Model Context Protocol (MCP): A Guide With Demo Project: In this blog, you will be guided through building an MCP-powered PR review server that integrates with Claude Desktop https://lnkd.in/dXDNbAat 3️⃣ Model Context Protocol (MCP) Hugging Face Course: This free course will take you on a journey, from beginner to informed, in understanding, using, and building applications with MCP https://lnkd.in/dX5Ja_9m 4️⃣ MCP: Build Rich-Context AI Apps with Anthropic: In this hands-on course, you’ll learn the core concepts of MCP and how to implement it in your AI Application https://lnkd.in/dxRyjRiW 5️⃣ Official MCP Documents: The official MCP docs are a good resource to learn the fundamentals, a tutorial to create your first MCP server, debugging, and inspection instructions https://lnkd.in/dqkQ6e_b 6️⃣ Awesome MCP Servers: A curated list of awesome Model Context Protocol (MCP) servers https://lnkd.in/d2AvkBmb 🌟 You can find more information about each learning resource in this article: https://lnkd.in/dbDHJnNi

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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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The 2025 MIT deep learning course is excellent, covering neural networks, CNNs, RNNs, and LLMs. You build three projects for hands-on experience as part of the course. It is entirely free. Highly recommended for beginners. Enroll Free: https://introtodeeplearning.com/

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks — full tutorials and code available on GitHub: 1️⃣ Text-Based Applications 1.1. Building a Chatbot Using HuggingFace Open Source Models https://lnkd.in/dku3bigK 1.2. Building a Text Translation System using Meta NLLB Open-Source Model https://lnkd.in/dgdjaFds 2️⃣ Speech-Based Applications 2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model https://lnkd.in/dbgQgDyn 2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio https://lnkd.in/dcbp-8fN 2.3. Building Text-to-Speech Systems Using VITS & ArTST Models https://lnkd.in/dwFcQ_X5 3️⃣ Image-Based Applications 3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model https://lnkd.in/dnk6epGB 3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide https://lnkd.in/d573SvYV 3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM) https://lnkd.in/dFavEdHS 3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio https://lnkd.in/d9jjJu_g 4️⃣ Vision Language Applications 4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models https://lnkd.in/dHNFaHFV 4.2. Building an Image Captioning System using Salesforce Blip Model https://lnkd.in/dh36iDn9 4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models https://lnkd.in/d7fsJEAF ➡️ You can find the articles and the codes for each article in this GitHub repo: https://lnkd.in/dG5jfBwE

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🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph Link: https://realpython.com/langgraph-python/

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