Our most in-depth course yet (10 hours long) 🥳 Crash Course for beginners on Data Structures & Algorithms https://lnkd.in/gbnbnvnX
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Completed the course: Supervised Learning with scikit-learn from DataCamp
Jordan Faulkner's Statement of Accomplishment | DataCamp
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How well do you know Pandas? If you’re starting with data manipulation and analysis, mastering Pandas is non-negotiable. I recently came across this fantastic breakdown of Pandas concepts that covers everything from Series slicing to advanced operations like pivot tables, data merging, and hierarchical indexing. Key concepts to explore: ✅ Creating & manipulating DataFrames ✅ Handling NULL values (because who doesn’t deal with missing data?) ✅ GroupBy operations and pivot tables for insights ✅ Regex magic in Pandas for text manipulation ✅ Loading data in chunks for large datasets The learning path is hands-on, filled with practical code snippets to upskill your Pandas proficiency. 🚀 Thanks to Nirav Prajapati
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Intern @Almanet Professional Services || Head of Technical Department @JLUG JEC || Java EE || MERN Stack || ACM JEC || Machine Learning
🚀 Excited to share a new milestone! 🚀 I’ve just completed the Introduction to Machine Learning course, where I learned the fundamentals of data manipulation, visualization, and basic machine learning algorithms using some key libraries: 📊 Pandas for data analysis and manipulation 🔢 NumPy for numerical computing 📉 Matplotlib for data visualization 🤖 Scikit-learn for building and evaluating machine learning models It’s been a rewarding journey, and I’ve documented everything in a GitHub repository where you can check out the Jupyter Notebooks and see how much I’ve learned. 🔗 Check it out here: https://lnkd.in/d-Pg5Zc4 Looking forward to diving deeper into the world of data science and machine learning! 💻 #MachineLearning #DataScience #Python #Pandas #NumPy #Matplotlib #ScikitLearn #JupyterNotebooks #LearningJourney Thanks to Zero To Mastery Academy.
GitHub - AnushkJain2201/introduction-to-machine-learning: This repository provides a comprehensive introduction to machine learning, covering essential tools and libraries commonly used in the field. It is designed for beginners and includes Jupyter Notebooks that guide you through key concepts, including data manipulation, visualization, and basic machine learning algorithms.
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I've just finished Supervised Learning with scikit-learn in DataCamp!!
אבישי צרפתי's Statement of Accomplishment | DataCamp
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I’ve completed the Supervised Learning with scikit-learn Course on DataCamp!
Алибек Есекеев's Statement of Accomplishment | DataCamp
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Done a course of Supervised Learning with scikit-learn #DataCamp
ABDULLAH SAJID's Statement of Accomplishment | DataCamp
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Data Science | Machine Learning | Deep Learning | Python | SQL | Flow Cytometry | Immunology | Fertility | Molecular Biology | Cell Culture | BlockChain Enthusiast. Combining fields of Healthcare, Data, MachineLearning.
Recap and broaden knowledge in scikit-learn 🎉
Luis Peñafiel Palmer's Statement of Accomplishment | DataCamp
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I just completed the "Supervised Learning with scikit-learn" course on DataCamp!
Jason Pollock's Statement of Accomplishment | DataCamp
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IT Undergrad | Python | C++ | Java | Front-End Dev. | DSA Enthusiast | Finance | Project Management
Excited to share that I've just completed a course on Data Structures and Algorithms in C for Beginners! Looking forward to applying these foundational skills to tackle complex problems and optimize solutions. #LearningJourney #DataStructures #Algorithms
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From like 16 - 20 hours down to 3 hours. Polars is the way for single machine data crunching workloads. Throw enough threads and disk at it you may be tempted to skip distributed processing 👀 #datascience
𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗵𝗶𝗴𝗵𝗲𝘀𝘁 𝘀𝗽𝗲𝗲𝗱-𝘂𝗽 𝘆𝗼𝘂’𝘃𝗲 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗱 𝘄𝗶𝘁𝗵 𝗣𝗼𝗹𝗮𝗿𝘀? As someone who claims to make data pipelines 100x faster, I often get asked: “𝘏𝘰𝘸 𝘦𝘹𝘢𝘤𝘵𝘭𝘺 𝘪𝘴 𝘵𝘩𝘢𝘵 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦?” The short answer: 𝘐𝘵 𝘥𝘦𝘱𝘦𝘯𝘥𝘴 𝘰𝘯 𝘵𝘩𝘦 𝘤𝘰𝘥𝘦. That 100x improvement is actually a conservative estimate, especially when starting with poorly optimized Pandas code (and this is quite often the case). 🚩 𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀 𝗼𝗳 𝘽𝙖𝙙 𝗣𝗮𝗻𝗱𝗮𝘀 𝗖𝗼𝗱𝗲 🐼: • Lack of vectorization • Overuse of loops • Heavy reliance on .apply() • Excessive intermediate DataFrame copies • Inefficient data types Even 𝘨𝘰𝘰𝘥 Pandas code can face inherent limitations because Pandas itself doesn’t fully leverage modern hardware resources or optimize pipeline execution. 𝗠𝘆 𝗥𝗲𝗰𝗼𝗿𝗱 𝗦𝗽𝗲𝗲𝗱-𝗨𝗽? 15,000x. Yes, you read that right: 𝙛𝙞𝙛𝙩𝙚𝙚𝙣 𝙩𝙝𝙤𝙪𝙨𝙖𝙣𝙙. The original pipeline ran for nearly 𝘁𝘄𝗼 𝗱𝗮𝘆𝘀, and after optimization, it completed in just 𝟭𝟮 𝘀𝗲𝗰𝗼𝗻𝗱𝘀. 🚀 Of course, that was with some of the worst Pandas code imaginable (I’ll spare you the gory details). With well-written Pandas code as a starting point, a realistic speed-up using #Polars is typically around 𝟭𝟱-𝟯𝟬𝘅 I would say, depending on the use case. 𝗬𝗼𝘂𝗿 𝘁𝘂𝗿𝗻: Have you encountered massive speed-ups when switching from Pandas to Polars? I’d love to hear about your results!
