WebToolTip com Mastering Machine Learning Algorithms 2025 | AA89E29642649DB3597C530A1DC8272A3A4F6958

WebToolTip com Mastering Machine Learning Algorithms 2025

Name	[ WebToolTip.com ] Mastering Machine Learning Algorithms (2025)	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	4.0 GB
Total Files	101
Last Seen	2025-07-19 00:18
Hash	AA89E29642649DB3597C530A1DC8272A3A4F6958

/
Get Bonus Downloads Here.url	0.2 KB
/.../1 - Introduction to Machine Learning/
1 -Introduction to the Course.mp4	11.1 MB
2 -What is Machine Learning with Example.mp4	94.9 MB
3 -Tom M. Mitchell Definition of Machine Learning.mp4	24.7 MB
4 -Types of Machine Learning and List of most ML algorithms.mp4	58.3 MB
5 - Read and Learn - List of All Machine Learning Algorithms.html	4.6 KB
5 - Read and Learn - What are the types of Machine Learning.html	2.7 KB
5 - Read and Learn - What is Machine Learning and its applications.html	3.9 KB
/.../10 - Cross Validation Techniques/
1 -Hold Out Cross Validation Technique.mp4	24.5 MB
10 -Parameters and Hyper-Parameters of the ML Algorithms.mp4	52.3 MB
11 -GridSearchCV - Hyper-Parameter Tuning Method.mp4	47.0 MB
2 -K-Fold Cross Validation Technique.mp4	27.7 MB
3 -Stratified K-Fold Cross Validation Technique.mp4	69.4 MB
4 -Leave P-Out Cross Validation Technique.mp4	33.3 MB
5 -Leave One Out Cross Validation.mp4	10.9 MB
6 -Imbalanced Dataset.mp4	27.6 MB
7 -OverSampling and UnderSampling.mp4	26.7 MB
8 -Synthetic Minority Oversampling Technique (SMOTE).mp4	19.5 MB
9 -Use case using the SMOTE.mp4	39.2 MB
/.../2 - Simple and Multi Linear Regression/
1 -Introduction to Correlation and Regression.mp4	60.1 MB
2 - Read and Learn - What is Correlation and Regression.html	2.8 KB
2 -Regression Algorithm Assumptions.mp4	54.8 MB
3 - Read and Learn - Linear Regression algorithm Assumptions.html	3.1 KB
3 -Simple and Multi Linear Regression (SLR) Algorithm.mp4	90.6 MB
4 - Read and Learn - Multi Linear Regression with Implementation Example.html	3.4 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	43.8 MB
5 -R-Square Performance Measure.mp4	48.0 MB
6 -Simple Linear Regression Implementation using sklearn library.mp4	19.1 MB
7 -Introduction to Use Case.mp4	20.5 MB
8 -Use case discussion.mp4	77.5 MB
/3 - Logistic Regression/
1 -What is classification and regression.mp4	20.5 MB
10 -Maximum Likelihood Estimation (MLE).mp4	81.3 MB
11 -Solving Logistic Regression Example with MLE.mp4	24.4 MB
2 -What is Logistic Regression, How it is different from linear regression and how.mp4	49.3 MB
3 -Logistic Regression Explanation with Example.mp4	49.5 MB
4 -Linear VS Logistic Regression.mp4	49.6 MB
5 -Confusion Matrix.mp4	63.9 MB
6 -Performance Metrics in Classification.mp4	47.1 MB
7 -Difference between Probability and Odds.mp4	75.0 MB
8 -Logistic Regression Derivation.mp4	22.1 MB
9 -Difference between Probability and Likelihood.mp4	34.1 MB
/4 - Decision Trees/
1 -Agenda.mp4	7.3 MB
2 -What is DT, its intuition and Terminologies.mp4	103.3 MB
3 -Impurity Measures - Entropy, Gini Index and Classification Error.mp4	131.3 MB
4 -Decision Tree Algorithms and Lets learn ID3 DT.mp4	135.7 MB
5 -CART Decision Tree Algorithm - wrt Classification.mp4	50.0 MB
6 -CART Decision Tree Algorithm - wrt Regression.mp4	39.2 MB
7 - Implementation of CART using SKLearn Library.html	5.7 KB
7 -Use case on Decision Tree - Prediction of Wine Quality.mp4	85.1 MB
/.../5 - K-Nearest Neighbor Algorithm/
1 -Parametric and Non-Parametric ML Algorithms.mp4	53.8 MB
2 -Distance Measures.mp4	53.3 MB
3 -Introduction to KNN Algorithm.mp4	73.4 MB
4 -How KNN Algorithm works.mp4	19.4 MB
5 -How to find optimum K Value in KNN.mp4	33.7 MB
6 -Use case explaining KNN implementation.mp4	25.9 MB
7 -Example - How to find an optimum k value for KNN.mp4	28.1 MB
/.../6 - Naïve Bayes Algorithm/
1 -Partition Theorem.mp4	27.6 MB
2 -Naïve Bayes Algorithm Pre-requisites.mp4	55.9 MB
3 -Bayes Theorem With Example.mp4	62.1 MB
4 -Bayes Theorem Formal Defination.mp4	13.6 MB
5 -Naïve Bayes Classifier with example.mp4	69.3 MB
/.../7 - Un-Supervised Machine Learning Algorithms/
1 -Recap of our learning.mp4	12.2 MB
10 -Elbow Method.mp4	24.6 MB
11 -Performance Metrics in Clustering.mp4	24.8 MB
12 -Silhouette Score Example.mp4	26.6 MB
13 -Use case using Silhouette score.mp4	29.8 MB
2 -Agenda.mp4	7.1 MB
3 -Distance Measures.mp4	52.0 MB
4 -Distance Measures Use cases.mp4	77.6 MB
5 -Use of Distance Measures in Machine Learning.mp4	24.9 MB
6 -KMeans Clustering Algorithm.mp4	28.0 MB
7 -Example - Clustering the data using KMeans Clustering Algorithm.mp4	23.4 MB
8 -KMeans Cost Function.mp4	11.5 MB
9 -KMeans Use cases.mp4	40.2 MB
/.../8 - Dimensionality Reduction Techniques in Un-Supervised ML/
1 -tSNE Introduction.mp4	66.1 MB
2 -tSNE Algorithm Steps.mp4	15.0 MB
3 -tSNE use case.mp4	24.1 MB
4 -tSNE Using the MINIST Dataset.mp4	44.4 MB
/9 - Ensemble Modeling/
1 -Introduction.mp4	19.5 MB
10 -Random Forest.mp4	66.0 MB
11 -Hyperparameters to tune Random Forest.mp4	56.2 MB
12 -Stacking Ensemble Learning.mp4	80.9 MB
13 -Use case On Stacking.mp4	43.3 MB
14 -Boosting.mp4	88.1 MB
15 -Boosting Algorithm Steps.mp4	47.7 MB
16 -AdaBoosting Ensemble Learning Model.mp4	41.4 MB
17 -AdaBoosting Ensemble Learning - Example.mp4	50.2 MB
18 -Bagging and Boosting Comparison.mp4	24.8 MB
19 -Gradient Boosting Algorithm.mp4	38.1 MB
2 -What is Ensemble and Model Error.mp4	51.1 MB
20 -Gradient Boosting Example.mp4	25.1 MB
21 -XGBoost Ensemble Learning Method.mp4	23.6 MB
3 -Bias and Variance Tradeoff.mp4	63.3 MB
4 -Simple Ensemble Modeling Methods - Voting, Averaging and Weighted Averaging.mp4	66.4 MB
5 -Random Sampling with Replacement.mp4	38.3 MB
6 -Use case 1 - Random Sampling with Replacement using customer feedback data.mp4	19.7 MB
7 -Use case 2 - Understanding the 63.21% Rule in Sampling with Replacement.mp4	42.7 MB
8 -Bagging.mp4	17.5 MB
9 -Vanilla Bagging Algorithm.mp4	46.1 MB
/~Get Your Files Here !/
Bonus Resources.txt	0.1 KB
Total files 101