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Mastering Machine Learning Algorithms 2025

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Files:

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

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