Udemy - Supervised Machine Learning Explained - The Top 5 Models

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3.4 GB
  • Uploaded Byfreecoursewb
  • Downloads212
  • Last checkedJun. 21st '26
  • Date uploadedJun. 19th '26
  • Seeders 21
  • Leechers18

Infohash : 267E5DBF4041C2F0D58417382BCC41DB4FDC5CFA

Supervised Machine Learning Explained: The Top 5 Models

https://WebToolTip.com

Published 6/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 49m | Size: 3.41 GB

A Beginner-Friendly Guide to Training and Evaluating Models

What you'll learn
Explain how supervised machine learning works by understanding features, targets, datasets, and how models learn from data.
Build core supervised learning models including linear regression, logistic regression, k-nearest neighbors, decision trees, and random forests.
Evaluate model performance using regression and classification metrics such as train/test splits, confusion matrices, precision, recall, and cross-validation.
Improve model performance by diagnosing overfitting and underfitting and applying feature scaling and preprocessing.
Develop the confidence and conceptual foundation needed to independently explore and continue building.

Requirements
No Machine Learning experience needed. You will learn everything you need to know.
Basic Python knowledge, including variables, data types, conditional statements, loops, and functions
Familiarity with Python syntax and simple scripts

Files:

[ WebToolTip.com ] Udemy - Supervised Machine Learning Explained - The Top 5 Models
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Course Introduction
    • 1 - Welcome! (Description).html (1.6 KB)
    • 1 - Welcome!.mp4 (123.2 MB)
    • 2 - Setup & Resources (Description).html (1.4 KB)
    • 2 - Setup & Resources.mp4 (159.8 MB)
    • 3 - Exploring Downloadable Notebooks.html (8.6 KB)
    2 - What It Means For a Machine to Learn
    • 4 - What Learning Means in Machine Learning (Description).html (2.6 KB)
    • 4 - What Learning Means in Machine Learning.mp4 (177.1 MB)
    • 5 - Datasets Features, Targets, and Rows (Description).html (2.7 KB)
    • 5 - Datasets Features, Targets, and Rows.mp4 (138.7 MB)
    • 5 - episode_1_2_used_car_dataset.xlsx (5.5 KB)
    • 6 - Train vs. Test Why We Split Data (Description).html (2.4 KB)
    • 6 - Train vs. Test Why We Split Data.mp4 (140.2 MB)
    • 7 - How To Train Test Split.html (12.9 KB)
    3 - Linear Regression Learning From Error
    • 1 - Foundations of Supervised Learning.html (24.7 KB)
    • 10 - How to Linear Regression and Evaluation.html (12.9 KB)
    • 11 - Overfitting and Underfitting (Description).html (2.3 KB)
    • 11 - Overfitting and Underfitting.mp4 (290.5 MB)
    • 8 - Linear Regression Predicting Numbers (Description).html (2.3 KB)
    • 8 - Linear Regression Predicting Numbers.mp4 (202.8 MB)
    • 8 - LinearRegressionExemplar.ipynb.bin (5.4 KB)
    • 9 - Loss Functions Measuring Error (Description).html (2.4 KB)
    • 9 - Loss Functions Measuring Error.mp4 (254.2 MB)
    4 - Logistic Regression Probabilities and Decisions
    • 12 - Logistic Regression Predicting Classes (Description).html (2.2 KB)
    • 12 - Logistic Regression Predicting Classes.mp4 (230.6 MB)
    • 12 - LogisticRegressionExemplar.ipynb.bin (26.4 KB)
    • 13 - Probabilities and Decision Thresholds (Description).html (2.1 KB)
    • 13 - Probabilities and Decision Thresholds.mp4 (75.5 MB)
    • 14 - Confusion Matrices and Classification Metrics (Description).html (2.3 KB)
    • 14 - Confusion Matrices and Classification Metrics.mp4 (171.8 MB)
    • 15 - How to Logistic Regression and Evaluation.html (13.4 KB)
    • 2 - Classification and Decision-Making.html (24.7 KB)
    5 - k-Nearest Neighbors Learning By Similarity
    • 16 - k-Nearest Neighbors Distance-Based Learning (Description).html (2.0 KB)
    • 16 - k-Nearest Neighbors Distance-Based Learning.mp4 (255.2 MB)
    • 16 - kNearestNeighborsExemplar.ipynb.bin (14.8 KB)
    • 17 - How to Choose k.html (10.6 KB)
    • 18 - Feature Scaling and Why It Matters (Description).html (2.1 KB)
    • 18 - Feature Scaling and Why It Matters.mp4 (141.7 MB)
    • 19 - How to KNN and Feature Scaling.html (13.2 KB)
    • 3 - Similarity-Based Learning.html (25.9 KB)
    6 - Decision Trees Learning Rules
    • 20 - Decision Trees Learning Rules (Description).html (1.8 KB)
    • 20 - Decision Trees Learning Rules.mp4 (215.4 MB)
    • 20 - DecisionTreeExemplar.ipynb.bin (9.9 KB)
    • 21 - Tree Depth and Model Complexity (Description).html (1.6 KB)
    • 21 - Tree Depth and Model Complexity.mp4 (178.4 MB)
    • 22 - How to Decision Trees.html (13.5 KB)
    • 23 - Cross-Validation Testing Model Stability (Description).html (2.0 KB)
    • 23 - Cross-Validation Testing Model Stability.mp4 (129.8 MB)
    • 24 - How to Cross-Validation.html (9.1 KB)
    • 4 - Decision Trees and Model Complexity.html (26.3 KB)
    7 - Random Forests Learning With Many Models
    • 25 - Random Forests Learning With Many Models (Description).html (1.9 KB)
    • 25 - Random Forests Learning With Many Models.mp4 (272.4 MB)
    • 25 - RandomForestExemplar.ipynb.bin (9.9 KB)
    • 26 - How to Random Forests.html (13.6 KB)
    • 27 - Bias Vs. Variance (Description).html (2.0 KB)
    • 27 - Bias Vs. Variance.mp4 (190.5 MB)
    • 5 - Bias, Variance, and Ensembles.html (26.0 KB)
    8 - Course Conclusion
    • 28 - Congratulations! And Next Steps.mp4 (148.2 MB)
    • 6 - Final Assessment.html (47.1 KB)
    • Bonus Resources.txt (0.1 KB)

Code:

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