Tags
Language
Tags
July 2025
Su Mo Tu We Th Fr Sa
29 30 1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 31 1 2
    Attention❗ To save your time, in order to download anything on this site, you must be registered 👉 HERE. If you do not have a registration yet, it is better to do it right away. ✌

    KoalaNames.com
    What’s in a name? More than you think.

    Your name isn’t just a label – it’s a vibe, a map, a story written in stars and numbers.
    At KoalaNames.com, we’ve cracked the code behind 17,000+ names to uncover the magic hiding in yours.

    ✨ Want to know what your name really says about you? You’ll get:

    🔮 Deep meaning and cultural roots
    ♈️ Zodiac-powered personality insights
    🔢 Your life path number (and what it means for your future)
    🌈 Daily affirmations based on your name’s unique energy

    Or flip the script – create a name from scratch using our wild Name Generator.
    Filter by star sign, numerology, origin, elements, and more. Go as woo-woo or chill as you like.

    💥 Ready to unlock your name’s power?

    👉 Tap in now at KoalaNames.com

    Supervised Machine Learning with R

    Posted By: IrGens
    Supervised Machine Learning with R

    Supervised Machine Learning with R
    .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 24m | 190 MB
    Instructor: Janani Ravi

    Predicting outcomes from labeled data is a key task in many real-world analytics problems. This course will teach you how to build, evaluate, and interpret supervised learning models in R for both regression and classification tasks.

    What you'll learn

    Building accurate predictive models requires you to know how to choose and apply the right algorithms—it involves preparing data, selecting the right models, and understanding how to evaluate and communicate results. In this course, Supervised Machine Learning with R, you’ll gain the ability to train, evaluate, and interpret regression and classification models using R.

    First, you’ll explore how to differentiate between regression and classification problems and prepare data using tools from the tidyverse, data.table, and rsample packages. Next, you’ll discover how to train and evaluate models like linear regression, logistic regression, and decision trees using performance metrics such as RMSE, R2, Accuracy, Precision, and Recall. Finally, you’ll learn how to compare models using cross-validation and interpret model behavior using techniques like SHAP values.

    When you’re finished with this course, you’ll have the skills and knowledge of supervised learning needed to apply predictive modeling techniques effectively in R.


    Supervised Machine Learning with R