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Udemy - Supervised Machine Learning Explained - The Top 5 Models

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Udemy - Supervised Machine Learning Explained - The Top 5 Models (Size: 3.4 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~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
  2 - What It Means For a Machine to Learn
  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
  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 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 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 KB
  27 - Bias Vs. Variance.mp4 190.5 MB
  5 - Bias, Variance, and Ensembles.html 26 KB
  8 - Course Conclusion
  28 - Congratulations! And Next Steps.mp4 148.2 MB
  6 - Final Assessment.html 47.1 KB
  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 - 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 - Exploring Downloadable Notebooks.html 8.6 KB

Description


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

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