Download Mastering Machine Learning Algorithms (2025) Torrent

Torrent Contents Size: 3.8 GB

Mastering Machine Learning Algorithms (2025)

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1 -Agenda.mp4	MP4	6.9 MB
1 -Hold Out Cross Validation Technique.mp4	MP4	23.3 MB
1 -Introduction to Correlation and Regression.mp4	MP4	57.3 MB
1 -Introduction to the Course.mp4	MP4	10.5 MB
1 -Introduction.mp4	MP4	18.6 MB
1 -Parametric and Non-Parametric ML Algorithms.mp4	MP4	51.3 MB
1 -Partition Theorem.mp4	MP4	26.4 MB
1 -Recap of our learning.mp4	MP4	11.6 MB
1 -What is classification and regression.mp4	MP4	19.6 MB
1 -tSNE Introduction.mp4	MP4	63 MB
10 -Elbow Method.mp4	MP4	23.4 MB
10 -Maximum Likelihood Estimation (MLE).mp4	MP4	77.6 MB
10 -Parameters and Hyper-Parameters of the ML Algorithms.mp4	MP4	49.9 MB
10 -Random Forest.mp4	MP4	63 MB
11 -GridSearchCV - Hyper-Parameter Tuning Method.mp4	MP4	44.8 MB
11 -Hyperparameters to tune Random Forest.mp4	MP4	53.6 MB
11 -Performance Metrics in Clustering.mp4	MP4	23.7 MB
11 -Solving Logistic Regression Example with MLE.mp4	MP4	23.2 MB
12 -Silhouette Score Example.mp4	MP4	25.4 MB
12 -Stacking Ensemble Learning.mp4	MP4	77.1 MB
13 -Use case On Stacking.mp4	MP4	41.3 MB
13 -Use case using Silhouette score.mp4	MP4	28.4 MB
14 -Boosting.mp4	MP4	84 MB
15 -Boosting Algorithm Steps.mp4	MP4	45.5 MB
16 -AdaBoosting Ensemble Learning Model.mp4	MP4	39.5 MB
17 -AdaBoosting Ensemble Learning - Example.mp4	MP4	47.9 MB
18 -Bagging and Boosting Comparison.mp4	MP4	23.7 MB
19 -Gradient Boosting Algorithm.mp4	MP4	36.3 MB
2 - Read and Learn - What is Correlation and Regression.html	HTML	2.8 KB
2 -Agenda.mp4	MP4	6.8 MB
2 -Distance Measures.mp4	MP4	50.8 MB
2 -K-Fold Cross Validation Technique.mp4	MP4	26.4 MB
2 -Naïve Bayes Algorithm Pre-requisites.mp4	MP4	53.3 MB
2 -Regression Algorithm Assumptions.mp4	MP4	52.3 MB
2 -What is DT, its intuition and Terminologies.mp4	MP4	98.6 MB
2 -What is Ensemble and Model Error.mp4	MP4	48.8 MB
2 -What is Logistic Regression, How it is different from linear regression and how.mp4	MP4	47 MB
2 -What is Machine Learning with Example.mp4	MP4	90.5 MB
2 -tSNE Algorithm Steps.mp4	MP4	14.3 MB
20 -Gradient Boosting Example.mp4	MP4	23.9 MB
21 -XGBoost Ensemble Learning Method.mp4	MP4	22.5 MB
3 - Read and Learn - Linear Regression algorithm Assumptions.html	HTML	3.1 KB
3 -Bayes Theorem With Example.mp4	MP4	59.2 MB
3 -Bias and Variance Tradeoff.mp4	MP4	60.4 MB
3 -Distance Measures.mp4	MP4	49.6 MB
3 -Impurity Measures - Entropy, Gini Index and Classification Error.mp4	MP4	125.3 MB
3 -Introduction to KNN Algorithm.mp4	MP4	70 MB
3 -Logistic Regression Explanation with Example.mp4	MP4	47.2 MB
3 -Simple and Multi Linear Regression (SLR) Algorithm.mp4	MP4	86.4 MB
3 -Stratified K-Fold Cross Validation Technique.mp4	MP4	66.2 MB
3 -Tom M. Mitchell Definition of Machine Learning.mp4	MP4	23.5 MB
3 -tSNE use case.mp4	MP4	23 MB
4 - Read and Learn - Multi Linear Regression with Implementation Example.html	HTML	3.3 KB
4 - Read and Learn - Simple Linear Regression with Implementation Example.html	HTML	2 KB
4 -Bayes Theorem Formal Defination.mp4	MP4	12.9 MB
4 -Decision Tree Algorithms and Lets learn ID3 DT.mp4	MP4	129.4 MB
4 -Distance Measures Use cases.mp4	MP4	74 MB
4 -How KNN Algorithm works.mp4	MP4	18.5 MB
4 -Hypothesis Testing to evaluate the significance of regression line.mp4	MP4	41.8 MB
4 -Leave P-Out Cross Validation Technique.mp4	MP4	31.8 MB
4 -Linear VS Logistic Regression.mp4	MP4	47.3 MB
4 -Simple Ensemble Modeling Methods - Voting, Averaging and Weighted Averaging.mp4	MP4	63.3 MB
4 -Types of Machine Learning and List of most ML algorithms.mp4	MP4	55.6 MB
4 -tSNE Using the MINIST Dataset.mp4	MP4	42.4 MB
5 - Read and Learn - List of All Machine Learning Algorithms.html	HTML	4.5 KB
5 - Read and Learn - What are the types of Machine Learning.html	HTML	2.6 KB
5 - Read and Learn - What is Machine Learning and its applications.html	HTML	3.8 KB
5 -CART Decision Tree Algorithm - wrt Classification.mp4	MP4	47.7 MB
5 -Confusion Matrix.mp4	MP4	60.9 MB
5 -How to find optimum K Value in KNN.mp4	MP4	32.1 MB
5 -Leave One Out Cross Validation.mp4	MP4	10.4 MB
5 -Naïve Bayes Classifier with example.mp4	MP4	66.1 MB
5 -R-Square Performance Measure.mp4	MP4	45.8 MB
5 -Random Sampling with Replacement.mp4	MP4	36.5 MB
5 -Use of Distance Measures in Machine Learning.mp4	MP4	23.8 MB
6 -CART Decision Tree Algorithm - wrt Regression.mp4	MP4	37.4 MB
6 -Imbalanced Dataset.mp4	MP4	26.3 MB
6 -KMeans Clustering Algorithm.mp4	MP4	26.7 MB
6 -Performance Metrics in Classification.mp4	MP4	44.9 MB
6 -Simple Linear Regression Implementation using sklearn library.mp4	MP4	18.2 MB
6 -Use case 1 - Random Sampling with Replacement using customer feedback data.mp4	MP4	18.7 MB
6 -Use case explaining KNN implementation.mp4	MP4	24.7 MB
7 - Implementation of CART using SKLearn Library.html	HTML	5.6 KB
7 -Difference between Probability and Odds.mp4	MP4	71.5 MB
7 -Example - Clustering the data using KMeans Clustering Algorithm.mp4	MP4	22.4 MB
7 -Example - How to find an optimum k value for KNN.mp4	MP4	26.8 MB
7 -Introduction to Use Case.mp4	MP4	19.5 MB
7 -OverSampling and UnderSampling.mp4	MP4	25.4 MB
7 -Use case 2 - Understanding the 63.21% Rule in Sampling with Replacement.mp4	MP4	40.7 MB
7 -Use case on Decision Tree - Prediction of Wine Quality.mp4	MP4	81.1 MB
8 -Bagging.mp4	MP4	16.7 MB
8 -KMeans Cost Function.mp4	MP4	10.9 MB
8 -Logistic Regression Derivation.mp4	MP4	21.1 MB
8 -Synthetic Minority Oversampling Technique (SMOTE).mp4	MP4	18.6 MB
8 -Use case discussion.mp4	MP4	73.9 MB
9 -Difference between Probability and Likelihood.mp4	MP4	32.6 MB
9 -KMeans Use cases.mp4	MP4	38.3 MB
9 -Use case using the SMOTE.mp4	MP4	37.4 MB
9 -Vanilla Bagging Algorithm.mp4	MP4	44 MB
Bonus Resources.txt	TXT	102.4 B
Get Bonus Downloads Here.url	URL	204.8 B

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Mastering Machine Learning Algorithms (2025)

Torrent Contents Size: 3.8 GB

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