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90% of Data Manipulation Made Easy with These Essential Pandas Commands 👇 Every data project starts with data cleaning and manipulation. Here's a focused list of pandas commands that handle 90% of real-world tasks: The basics: • Loading and viewing: read_csv(), head(), and info() give you a quick look at your data • Selection tricks: Use loc[] for labels and iloc[] for positions to grab exactly what you need • Missing data handling: dropna() and fillna() keep your data clean • Data reshaping: groupby() and merge() help structure your data just right The power moves: • Quick stats: value_counts() and describe() for fast insights • Filtering: query() for clean, readable conditions • Column management: rename() and drop() to keep your dataframe tidy These commands form the backbone of data manipulation in pandas. They're simple, effective, and handle most common scenarios without extra complexity. 𝘙𝘦𝘮𝘦𝘮𝘣𝘦𝘳: 𝘎𝘰𝘰𝘥 𝘥𝘢𝘵𝘢 𝘱𝘳𝘦𝘱 𝘪𝘴 80% 𝘰𝘧 𝘢𝘯𝘺 𝘴𝘶𝘤𝘤𝘦𝘴𝘴𝘧𝘶𝘭 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴. 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #datascience #ai #machinelearning #programming

You don’t need to know every Git command, but mastering the essentials will get you out of almost any situation. Here are 22 Git Commands Every Engineer Must Know 👇 Git helps teams track and manage code changes effectively. Here’s a quick guide to must-know Git commands: 𝗕𝗮𝘀𝗶𝗰 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: • Start fresh with 'git init' or copy existing projects using 'git clone' • Track file status and history with 'git status' and 'git log' 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗕𝗿𝗮𝗻𝗰𝗵𝗲𝘀: • Create and switch branches to work on features separately • Merge code safely when features are ready 𝗦𝘁𝗮𝗴𝗶𝗻𝗴 & 𝗖𝗼𝗺𝗺𝗶𝘁𝘁𝗶𝗻𝗴: • Save work with 'git add' and 'git commit' • Store temporary changes using 'git stash' • Undo mistakes with 'git reset' 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: • Share code through 'git push' and 'git pull' • Track remote connections with 'git remote' • Mark important points with 'git tag' 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀: • Find bugs faster with 'git bisect' • See file changes with 'git diff' • Track code ownership using 'git blame' These commands form the backbone of efficient code management and team collaboration. --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/eS-jpYem #git #ai #developer #machinelearning #programming

There has been a 59% increase in demand for AI engineers in the job market. I’ve created this roadmap to help you future-proof your skills and make yourself job-ready in this booming field ↓ Here's how you can stand out in the job market and get noticed by recruiters: 📈 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝘗𝘳𝘰𝘫𝘦𝘤𝘵𝘴 ↳ Real-World Applications: Create projects that solve actual problems (predictive analytics, NLP tools). ↳ Open Source Contributions: Help with AI-related open-source projects for experience and visibility. 𝘋𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯 ↳ Blogging: Write about what you learn and build to show your knowledge. ↳ GitHub: Keep a clean, well-organized code repository with documentation. 🔥 I've compiled these Top courses to help you get certified: ↳ Google AI Essentials – https://lnkd.in/efsqc8SQ ↳ IBM AI Developer – https://lnkd.in/e_iXr4FC ↳ IBM Generative AI: Prompt Engineering Basics – https://lnkd.in/eH63e_GV ↳ Google Prompting Essentials – https://lnkd.in/e-9-DvHZ ↳ Prompt Engineering – University of Vanderbilt – https://lnkd.in/ee35agHT ↳ IBM Generative AI Engineering – https://lnkd.in/eQSiTggG ↳ DeepLearning .AI – Building with Large Language Models – https://lnkd.in/e5n3iip2 ↳ Generative AI Automation – University of Vanderbilt – https://lnkd.in/egjY__hz There is no better time to get started than now, as Coursera is offering a special discount of 40% off their 3 month subscription for a limited time. Sign up here and start learning 👉 https://lnkd.in/e6PPg58H Whether you're a developer, data scientist, or just AI-curious, this guide is your starting point. #python #datascience #ai #machinelearning #programming

Loss functions are critical components in machine learning that guide model training. Here's a breakdown of five essential loss functions you need to understand: 𝟭. 𝗠𝗲𝗮𝗻 𝗦𝗾𝘂𝗮𝗿𝗲𝗱 𝗘𝗿𝗿𝗼𝗿 (𝗠𝗦𝗘) ↳ Use case: Backbone of regression problems (e.g., Multiple Linear Regression) ↳ Formula: MSE = (1/N) Σ(y_i - ŷ_i)² ↳ Key properties: Quadratic error penalization, always non-negative and differentiable ↳ When to use: For problems where you need to predict continuous values 𝟮. 𝗕𝗶𝗻𝗮𝗿𝘆 𝗖𝗿𝗼𝘀𝘀 𝗘𝗻𝘁𝗿𝗼𝗽𝘆 (𝗕𝗖𝗘) ↳ Use case: Binary classification problems ↳ Formula: BCE = -(1/N) Σ[y_i log(ŷ_i) + (1-y_i)log(1-ŷ_i)] ↳ Key properties: Derived from Bernoulli MLE; natural for probability outputs ↳ When to use: When your model outputs probabilities between 0 and 1 𝟯. 𝗙𝗼𝗰𝗮𝗹 𝗟𝗼𝘀𝘀 ↳ Use case: Classification with severe class imbalance or hard-to-detect examples ↳ Key components: α (balancing factor) and β (focusing parameter) ↳ Advantage: Assigns larger loss to harder-to-classify instances ↳ When to use: When dealing with highly imbalanced datasets 𝟰. 𝗗𝗶𝗰𝗲 𝗟𝗼𝘀𝘀 ↳ Use case: Image segmentation tasks ↳ Key property: Measures segmentation overlap ↳ Advantage: Handles class imbalance naturally ↳ When to use: When working with pixel-level predictions in computer vision 𝟱. 𝗧𝗿𝗶𝗽𝗹𝗲𝘁 𝗟𝗼𝘀𝘀 ↳ Use case: Metric learning, especially for face verification ↳ Formula: max(0, D(a,p) - D(a,n) + m) ↳ Key concept: Groups similar samples while separating dissimilar ones ↳ When to use: When you need to learn embeddings that cluster similar items Understanding these loss functions and knowing when to apply each one will significantly improve your model performance across various machine learning tasks. --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #machinelearning #programming

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