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Category:
Language:
English
Total Size:
3.8 GB
Info Hash:
AA89E29642649DB3597C530A1DC8272A3A4F6958
Added By:
Added:
May 8, 2025, 7:22 a.m.
Stats:
|
(Last updated: May 20, 2025, 1:03 a.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 180 bytes |
| 1 -Introduction to the Course.mp4 | 10.5 MB |
| 2 -What is Machine Learning with Example.mp4 | 90.5 MB |
| 3 -Tom M. Mitchell Definition of Machine Learning.mp4 | 23.5 MB |
| 4 -Types of Machine Learning and List of most ML algorithms.mp4 | 55.6 MB |
| 5 - Read and Learn - List of All Machine Learning Algorithms.html | 4.5 KB |
| 5 - Read and Learn - What are the types of Machine Learning.html | 2.6 KB |
| 5 - Read and Learn - What is Machine Learning and its applications.html | 3.8 KB |
| 1 -Hold Out Cross Validation Technique.mp4 | 23.3 MB |
| 10 -Parameters and Hyper-Parameters of the ML Algorithms.mp4 | 49.9 MB |
| 11 -GridSearchCV - Hyper-Parameter Tuning Method.mp4 | 44.8 MB |
| 2 -K-Fold Cross Validation Technique.mp4 | 26.4 MB |
| 3 -Stratified K-Fold Cross Validation Technique.mp4 | 66.2 MB |
| 4 -Leave P-Out Cross Validation Technique.mp4 | 31.8 MB |
| 5 -Leave One Out Cross Validation.mp4 | 10.4 MB |
| 6 -Imbalanced Dataset.mp4 | 26.3 MB |
| 7 -OverSampling and UnderSampling.mp4 | 25.4 MB |
| 8 -Synthetic Minority Oversampling Technique (SMOTE).mp4 | 18.6 MB |
| 9 -Use case using the SMOTE.mp4 | 37.4 MB |
| 1 -Introduction to Correlation and Regression.mp4 | 57.3 MB |
| 2 - Read and Learn - What is Correlation and Regression.html | 2.8 KB |
| 2 -Regression Algorithm Assumptions.mp4 | 52.3 MB |
| 3 - Read and Learn - Linear Regression algorithm Assumptions.html | 3.1 KB |
| 3 -Simple and Multi Linear Regression (SLR) Algorithm.mp4 | 86.4 MB |
| 4 - Read and Learn - Multi Linear Regression with Implementation Example.html | 3.3 KB |
| 4 - Read and Learn - Simple Linear Regression with Implementation Example.html | 2.0 KB |
| 4 -Hypothesis Testing to evaluate the significance of regression line.mp4 | 41.8 MB |
| 5 -R-Square Performance Measure.mp4 | 45.8 MB |
| 6 -Simple Linear Regression Implementation using sklearn library.mp4 | 18.2 MB |
| 7 -Introduction to Use Case.mp4 | 19.5 MB |
| 8 -Use case discussion.mp4 | 73.9 MB |
| 1 -What is classification and regression.mp4 | 19.6 MB |
| 10 -Maximum Likelihood Estimation (MLE).mp4 | 77.6 MB |
| 11 -Solving Logistic Regression Example with MLE.mp4 | 23.2 MB |
| 2 -What is Logistic Regression, How it is different from linear regression and how.mp4 | 47.0 MB |
| 3 -Logistic Regression Explanation with Example.mp4 | 47.2 MB |
| 4 -Linear VS Logistic Regression.mp4 | 47.3 MB |
| 5 -Confusion Matrix.mp4 | 60.9 MB |
| 6 -Performance Metrics in Classification.mp4 | 44.9 MB |
| 7 -Difference between Probability and Odds.mp4 | 71.5 MB |
| 8 -Logistic Regression Derivation.mp4 | 21.1 MB |
| 9 -Difference between Probability and Likelihood.mp4 | 32.6 MB |
| 1 -Agenda.mp4 | 6.9 MB |
| 2 -What is DT, its intuition and Terminologies.mp4 | 98.6 MB |
| 3 -Impurity Measures - Entropy, Gini Index and Classification Error.mp4 | 125.3 MB |
| 4 -Decision Tree Algorithms and Lets learn ID3 DT.mp4 | 129.4 MB |
| 5 -CART Decision Tree Algorithm - wrt Classification.mp4 | 47.7 MB |
| 6 -CART Decision Tree Algorithm - wrt Regression.mp4 | 37.4 MB |
| 7 - Implementation of CART using SKLearn Library.html | 5.6 KB |
| 7 -Use case on Decision Tree - Prediction of Wine Quality.mp4 | 81.1 MB |
| 1 -Parametric and Non-Parametric ML Algorithms.mp4 | 51.3 MB |
| 2 -Distance Measures.mp4 | 50.8 MB |
| 3 -Introduction to KNN Algorithm.mp4 | 70.0 MB |
| 4 -How KNN Algorithm works.mp4 | 18.5 MB |
| 5 -How to find optimum K Value in KNN.mp4 | 32.1 MB |
| 6 -Use case explaining KNN implementation.mp4 | 24.7 MB |
| 7 -Example - How to find an optimum k value for KNN.mp4 | 26.8 MB |
| 1 -Partition Theorem.mp4 | 26.4 MB |
| 2 -Naïve Bayes Algorithm Pre-requisites.mp4 | 53.3 MB |
| 3 -Bayes Theorem With Example.mp4 | 59.2 MB |
| 4 -Bayes Theorem Formal Defination.mp4 | 12.9 MB |
| 5 -Naïve Bayes Classifier with example.mp4 | 66.1 MB |
| 1 -Recap of our learning.mp4 | 11.6 MB |
| 10 -Elbow Method.mp4 | 23.4 MB |
| 11 -Performance Metrics in Clustering.mp4 | 23.7 MB |
| 12 -Silhouette Score Example.mp4 | 25.4 MB |
| 13 -Use case using Silhouette score.mp4 | 28.4 MB |
| 2 -Agenda.mp4 | 6.8 MB |
| 3 -Distance Measures.mp4 | 49.6 MB |
| 4 -Distance Measures Use cases.mp4 | 74.0 MB |
| 5 -Use of Distance Measures in Machine Learning.mp4 | 23.8 MB |
| 6 -KMeans Clustering Algorithm.mp4 | 26.7 MB |
| 7 -Example - Clustering the data using KMeans Clustering Algorithm.mp4 | 22.4 MB |
| 8 -KMeans Cost Function.mp4 | 10.9 MB |
| 9 -KMeans Use cases.mp4 | 38.3 MB |
| 1 -tSNE Introduction.mp4 | 63.0 MB |
| 2 -tSNE Algorithm Steps.mp4 | 14.3 MB |
| 3 -tSNE use case.mp4 | 23.0 MB |
| 4 -tSNE Using the MINIST Dataset.mp4 | 42.4 MB |
| 1 -Introduction.mp4 | 18.6 MB |
| 10 -Random Forest.mp4 | 63.0 MB |
| 11 -Hyperparameters to tune Random Forest.mp4 | 53.6 MB |
| 12 -Stacking Ensemble Learning.mp4 | 77.1 MB |
| 13 -Use case On Stacking.mp4 | 41.3 MB |
| 14 -Boosting.mp4 | 84.0 MB |
| 15 -Boosting Algorithm Steps.mp4 | 45.5 MB |
| 16 -AdaBoosting Ensemble Learning Model.mp4 | 39.5 MB |
| 17 -AdaBoosting Ensemble Learning - Example.mp4 | 47.9 MB |
| 18 -Bagging and Boosting Comparison.mp4 | 23.7 MB |
| 19 -Gradient Boosting Algorithm.mp4 | 36.3 MB |
| 2 -What is Ensemble and Model Error.mp4 | 48.8 MB |
| 20 -Gradient Boosting Example.mp4 | 23.9 MB |
| 21 -XGBoost Ensemble Learning Method.mp4 | 22.5 MB |
| 3 -Bias and Variance Tradeoff.mp4 | 60.4 MB |
| 4 -Simple Ensemble Modeling Methods - Voting, Averaging and Weighted Averaging.mp4 | 63.3 MB |
| 5 -Random Sampling with Replacement.mp4 | 36.5 MB |
| 6 -Use case 1 - Random Sampling with Replacement using customer feedback data.mp4 | 18.7 MB |
| 7 -Use case 2 - Understanding the 63.21% Rule in Sampling with Replacement.mp4 | 40.7 MB |
| 8 -Bagging.mp4 | 16.7 MB |
| 9 -Vanilla Bagging Algorithm.mp4 | 44.0 MB |
| Bonus Resources.txt | 70 bytes |
Name
DL
Uploader
Size
S/L
Added
-
94.9 MB
[6
/
13]
2023-10-29
| Uploaded by coursedevil | Size 94.9 MB | Health [ 6 /13 ] | Added 2023-10-29 |
-
3.8 GB
[21
/
21]
2025-05-08
| Uploaded by freecoursewb | Size 3.8 GB | Health [ 21 /21 ] | Added 2025-05-08 |
NOTE
SOURCE: Mastering Machine Learning Algorithms 2025
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