| 1 -Agenda.mp4 | 6.9 MB | ||
| 1 -Hold Out Cross Validation Technique.mp4 | 23.3 MB | ||
| 1 -Introduction to Correlation and Regression.mp4 | 57.3 MB | ||
| 1 -Introduction to the Course.mp4 | 10.5 MB | ||
| 1 -Introduction.mp4 | 18.6 MB | ||
| 1 -Parametric and Non-Parametric ML Algorithms.mp4 | 51.3 MB | ||
| 1 -Partition Theorem.mp4 | 26.4 MB | ||
| 1 -Recap of our learning.mp4 | 11.6 MB | ||
| 1 -What is classification and regression.mp4 | 19.6 MB | ||
| 1 -tSNE Introduction.mp4 | 63 MB | ||
| 10 -Elbow Method.mp4 | 23.4 MB | ||
| 10 -Maximum Likelihood Estimation (MLE).mp4 | 77.6 MB | ||
| 10 -Parameters and Hyper-Parameters of the ML Algorithms.mp4 | 49.9 MB | ||
| 10 -Random Forest.mp4 | 63 MB | ||
| 11 -GridSearchCV - Hyper-Parameter Tuning Method.mp4 | 44.8 MB | ||
| 11 -Hyperparameters to tune Random Forest.mp4 | 53.6 MB | ||
| 11 -Performance Metrics in Clustering.mp4 | 23.7 MB | ||
| 11 -Solving Logistic Regression Example with MLE.mp4 | 23.2 MB | ||
| 12 -Silhouette Score Example.mp4 | 25.4 MB | ||
| 12 -Stacking Ensemble Learning.mp4 | 77.1 MB | ||
| 13 -Use case On Stacking.mp4 | 41.3 MB | ||
| 13 -Use case using Silhouette score.mp4 | 28.4 MB | ||
| 14 -Boosting.mp4 | 84 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 - Read and Learn - What is Correlation and Regression.html | 2.8 KB | ||
| 2 -Agenda.mp4 | 6.8 MB | ||
| 2 -Distance Measures.mp4 | 50.8 MB | ||
| 2 -K-Fold Cross Validation Technique.mp4 | 26.4 MB | ||
| 2 -Naïve Bayes Algorithm Pre-requisites.mp4 | 53.3 MB | ||
| 2 -Regression Algorithm Assumptions.mp4 | 52.3 MB | ||
| 2 -What is DT, its intuition and Terminologies.mp4 | 98.6 MB | ||
| 2 -What is Ensemble and Model Error.mp4 | 48.8 MB | ||
| 2 -What is Logistic Regression, How it is different from linear regression and how.mp4 | 47 MB | ||
| 2 -What is Machine Learning with Example.mp4 | 90.5 MB | ||
| 2 -tSNE Algorithm Steps.mp4 | 14.3 MB | ||
| 20 -Gradient Boosting Example.mp4 | 23.9 MB | ||
| 21 -XGBoost Ensemble Learning Method.mp4 | 22.5 MB | ||
| 3 - Read and Learn - Linear Regression algorithm Assumptions.html | 3.1 KB | ||
| 3 -Bayes Theorem With Example.mp4 | 59.2 MB | ||
| 3 -Bias and Variance Tradeoff.mp4 | 60.4 MB | ||
| 3 -Distance Measures.mp4 | 49.6 MB | ||
| 3 -Impurity Measures - Entropy, Gini Index and Classification Error.mp4 | 125.3 MB | ||
| 3 -Introduction to KNN Algorithm.mp4 | 70 MB | ||
| 3 -Logistic Regression Explanation with Example.mp4 | 47.2 MB | ||
| 3 -Simple and Multi Linear Regression (SLR) Algorithm.mp4 | 86.4 MB | ||
| 3 -Stratified K-Fold Cross Validation Technique.mp4 | 66.2 MB | ||
| 3 -Tom M. Mitchell Definition of Machine Learning.mp4 | 23.5 MB | ||
| 3 -tSNE use case.mp4 | 23 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 KB | ||
| 4 -Bayes Theorem Formal Defination.mp4 | 12.9 MB | ||
| 4 -Decision Tree Algorithms and Lets learn ID3 DT.mp4 | 129.4 MB | ||
| 4 -Distance Measures Use cases.mp4 | 74 MB | ||
| 4 -How KNN Algorithm works.mp4 | 18.5 MB | ||
| 4 -Hypothesis Testing to evaluate the significance of regression line.mp4 | 41.8 MB | ||
| 4 -Leave P-Out Cross Validation Technique.mp4 | 31.8 MB | ||
| 4 -Linear VS Logistic Regression.mp4 | 47.3 MB | ||
| 4 -Simple Ensemble Modeling Methods - Voting, Averaging and Weighted Averaging.mp4 | 63.3 MB | ||
| 4 -Types of Machine Learning and List of most ML algorithms.mp4 | 55.6 MB | ||
| 4 -tSNE Using the MINIST Dataset.mp4 | 42.4 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 | ||
| 5 -CART Decision Tree Algorithm - wrt Classification.mp4 | 47.7 MB | ||
| 5 -Confusion Matrix.mp4 | 60.9 MB | ||
| 5 -How to find optimum K Value in KNN.mp4 | 32.1 MB | ||
| 5 -Leave One Out Cross Validation.mp4 | 10.4 MB | ||
| 5 -Naïve Bayes Classifier with example.mp4 | 66.1 MB | ||
| 5 -R-Square Performance Measure.mp4 | 45.8 MB | ||
| 5 -Random Sampling with Replacement.mp4 | 36.5 MB | ||
| 5 -Use of Distance Measures in Machine Learning.mp4 | 23.8 MB | ||
| 6 -CART Decision Tree Algorithm - wrt Regression.mp4 | 37.4 MB | ||
| 6 -Imbalanced Dataset.mp4 | 26.3 MB | ||
| 6 -KMeans Clustering Algorithm.mp4 | 26.7 MB | ||
| 6 -Performance Metrics in Classification.mp4 | 44.9 MB | ||
| 6 -Simple Linear Regression Implementation using sklearn library.mp4 | 18.2 MB | ||
| 6 -Use case 1 - Random Sampling with Replacement using customer feedback data.mp4 | 18.7 MB | ||
| 6 -Use case explaining KNN implementation.mp4 | 24.7 MB | ||
| 7 - Implementation of CART using SKLearn Library.html | 5.6 KB | ||
| 7 -Difference between Probability and Odds.mp4 | 71.5 MB | ||
| 7 -Example - Clustering the data using KMeans Clustering Algorithm.mp4 | 22.4 MB | ||
| 7 -Example - How to find an optimum k value for KNN.mp4 | 26.8 MB | ||
| 7 -Introduction to Use Case.mp4 | 19.5 MB | ||
| 7 -OverSampling and UnderSampling.mp4 | 25.4 MB | ||
| 7 -Use case 2 - Understanding the 63.21% Rule in Sampling with Replacement.mp4 | 40.7 MB | ||
| 7 -Use case on Decision Tree - Prediction of Wine Quality.mp4 | 81.1 MB | ||
| 8 -Bagging.mp4 | 16.7 MB | ||
| 8 -KMeans Cost Function.mp4 | 10.9 MB | ||
| 8 -Logistic Regression Derivation.mp4 | 21.1 MB | ||
| 8 -Synthetic Minority Oversampling Technique (SMOTE).mp4 | 18.6 MB | ||
| 8 -Use case discussion.mp4 | 73.9 MB | ||
| 9 -Difference between Probability and Likelihood.mp4 | 32.6 MB | ||
| 9 -KMeans Use cases.mp4 | 38.3 MB | ||
| 9 -Use case using the SMOTE.mp4 | 37.4 MB | ||
| 9 -Vanilla Bagging Algorithm.mp4 | 44 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 101 total files | |||
Mastering Machine Learning Algorithms (2025)
https://WebToolTip.com
Published 4/2025
Created by Pralhad Teggi
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 99 Lectures ( 9h 43m ) | Size: 3.76 GB
A comprehensive, step-by-step guide to key Machine Learning algorithms, use cases, and implementation using Python.
What you'll learn
Gain a solid understanding of the foundational concepts of machine learning including the principles of classification and regression.
Learn the key terminology and mathematical concepts behind machine learning algorithms, such as features, labels, training data, and the role of algorithms.
Explore and master popular machine learning algorithms, including but not limited to linear regression, KNN, decision trees, support vector machines etc.
Acquire practical skills by implementing machine learning algorithms using industry-standard tools and programming languages like Python, scikit learn etc
Work on real-world datasets to gain hands-on experience in preprocessing data, training models, and evaluating performance metrics.
Requirements
Programming Proficiency - Prerequisite : Basic programming skills. Rationale : Participants should have a fundamental understanding of programming concepts, as the course may involve coding exercises and implementations using languages such as Python.
Mathematics and Statistics Background: Prerequisite: Basic understanding of algebra, calculus, and statistics. Rationale: Supervised machine learning often involves mathematical and statistical concepts. Familiarity with concepts like derivatives, linear algebra, probability, and basic statistical measures will aid in understanding algorithms and evaluation metrics.
Introduction to Data Science: Prerequisite: Basic knowledge of data science concepts. Rationale: Participants should be familiar with key data science concepts, such as data types, exploratory data analysis, and the overall data science workflow. This foundation helps in understanding how machine learning fits into the broader context of data science.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 632.9 MB | freecoursewb | 2 years | 0 | 0 | |
| 732.4 MB | freecoursewb | 3 years | 0 | 0 |
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