Udemy - Mastering Machine Learning Algorithms (2025)

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Free Download Udemy - Mastering Machine Learning Algorithms (2025)

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.
Description
Unlock the power of Machine Learning with this in-depth course designed to help you master the most essential algorithms in the field. Whether you're a beginner looking to build a strong foundation or a practitioner aiming to deepen your understanding, this course will guide you through the core concepts, mathematical intuition, and practical applications of machine learning models.You'll start with a solid introduction to the world of Machine Learning - what it is, its types, and where it's applied - followed by hands-on learning of the most widely-used supervised and unsupervised algorithms including:Linear and Logistic RegressionDecision Trees and Random ForestK-Nearest Neighbors (KNN)Naïve BayesClustering with K-MeansDimensionality Reduction (t-SNE)Advanced Ensemble Techniques (Bagging, Boosting, Stacking, XGBoost)Each algorithm is broken down with real-world use cases, performance evaluation techniques, and Python-based implementations using libraries like Scikit-Learn. You'll also learn about Cross-Validation strategies to enhance your model's robustness.By the end of this course, you'll be equipped to:Understand the math and logic behind key ML algorithmsChoose the right algorithm for different problemsImplement models using Python and evaluate their performanceApply machine learning in real-world scenariosThis course is ideal for data science students, analysts, software developers, and professionals seeking to add machine learning skills to their portfolio.
Who this course is for
Individuals who are just starting their journey in data science and machine learning and want to understand the basics of decision trees as a predictive modeling technique.
Professionals working with data analysis who want to expand their skills to include machine learning techniques like decision trees for classification and regression tasks.
Programmers and software developers interested in incorporating machine learning into their applications or gaining a better understanding of how decision trees work.
Students studying data science, computer science, or related fields who want to deepen their knowledge of machine learning algorithms, specifically decision trees.
Enthusiasts and lifelong learners who have a general interest in machine learning and want to explore decision trees as a part of their broader understanding of the field.
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3.77 GB | 17min 11s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

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FileName :1 -Introduction to the Course.mp4 | Size: (10.54 MB)
FileName :2 -What is Machine Learning with Example.mp4 | Size: (90.48 MB)
FileName :3 -Tom M Mitchell Definition of Machine Learning.mp4 | Size: (23.54 MB)
FileName :4 -Types of Machine Learning and List of most ML algorithms.mp4 | Size: (55.64 MB)
FileName :1 -Hold Out Cross Validation Technique.mp4 | Size: (23.34 MB)
FileName :10 -Parameters and Hyper-Parameters of the ML Algorithms.mp4 | Size: (49.88 MB)
FileName :11 -GridSearchCV - Hyper-Parameter Tuning Method.mp4 | Size: (44.85 MB)
FileName :2 -K-Fold Cross Validation Technique.mp4 | Size: (26.39 MB)
FileName :3 -Stratified K-Fold Cross Validation Technique.mp4 | Size: (66.16 MB)
FileName :4 -Leave P-Out Cross Validation Technique.mp4 | Size: (31.76 MB)
FileName :5 -Leave One Out Cross Validation.mp4 | Size: (10.4 MB)
FileName :6 -Imbalanced Dataset.mp4 | Size: (26.31 MB)
FileName :7 -OverSampling and UnderSampling.mp4 | Size: (25.42 MB)
FileName :8 -Synthetic Minority Oversampling Technique (SMOTE).mp4 | Size: (18.58 MB)
FileName :9 -Use case using the SMOTE.mp4 | Size: (37.39 MB)
FileName :1 -Introduction to Correlation and Regression.mp4 | Size: (57.32 MB)
FileName :2 -Regression Algorithm Assumptions.mp4 | Size: (52.3 MB)
FileName :3 -Simple and Multi Linear Regression (SLR) Algorithm.mp4 | Size: (86.39 MB)
FileName :4 -Hypothesis Testing to evaluate the significance of regression line.mp4 | Size: (41.76 MB)
FileName :5 -R-Square Performance Measure.mp4 | Size: (45.76 MB)
FileName :6 -Simple Linear Regression Implementation using sklearn library.mp4 | Size: (18.17 MB)
FileName :7 -Introduction to Use Case.mp4 | Size: (19.52 MB)
FileName :8 -Use case discussion.mp4 | Size: (73.86 MB)
FileName :1 -What is classification and regression.mp4 | Size: (19.59 MB)
FileName :10 -Maximum Likelihood Estimation (MLE).mp4 | Size: (77.56 MB)
FileName :11 -Solving Logistic Regression Example with MLE.mp4 | Size: (23.24 MB)
FileName :2 -What is Logistic Regression, How it is different from linear regression and how.mp4 | Size: (47.02 MB)
FileName :3 -Logistic Regression Explanation with Example.mp4 | Size: (47.16 MB)
FileName :4 -Linear VS Logistic Regression.mp4 | Size: (47.26 MB)
FileName :5 -Confusion Matrix.mp4 | Size: (60.91 MB)
FileName :6 -Performance Metrics in Classification.mp4 | Size: (44.89 MB)
FileName :7 -Difference between Probability and Odds.mp4 | Size: (71.53 MB)
FileName :8 -Logistic Regression Derivation.mp4 | Size: (21.11 MB)
FileName :9 -Difference between Probability and Likelihood.mp4 | Size: (32.55 MB)
FileName :1 -Agenda.mp4 | Size: (6.94 MB)
FileName :2 -What is DT, its intuition and Terminologies.mp4 | Size: (98.56 MB)
FileName :3 -Impurity Measures - Entropy, Gini Index and Classification Error.mp4 | Size: (125.26 MB)
FileName :4 -Decision Tree Algorithms and Lets learn ID3 DT.mp4 | Size: (129.45 MB)
FileName :5 -CART Decision Tree Algorithm - wrt Classification.mp4 | Size: (47.69 MB)
FileName :6 -CART Decision Tree Algorithm - wrt Regression.mp4 | Size: (37.37 MB)
FileName :7 -Use case on Decision Tree - Prediction of Wine Quality.mp4 | Size: (81.15 MB)
FileName :1 -Parametric and Non-Parametric ML Algorithms.mp4 | Size: (51.29 MB)
FileName :2 -Distance Measures.mp4 | Size: (50.84 MB)
FileName :3 -Introduction to KNN Algorithm.mp4 | Size: (70.03 MB)
FileName :4 -How KNN Algorithm works.mp4 | Size: (18.5 MB)
FileName :5 -How to find optimum K Value in KNN.mp4 | Size: (32.11 MB)
FileName :6 -Use case explaining KNN implementation.mp4 | Size: (24.68 MB)
FileName :7 -Example - How to find an optimum k value for KNN.mp4 | Size: (26.77 MB)
FileName :1 -Partition Theorem.mp4 | Size: (26.37 MB)
FileName :2 -Naïve Bayes Algorithm Pre-requisites.mp4 | Size: (53.29 MB)
FileName :3 -Bayes Theorem With Example.mp4 | Size: (59.21 MB)
FileName :4 -Bayes Theorem Formal Defination.mp4 | Size: (12.94 MB)
FileName :5 -Naïve Bayes Classifier with example.mp4 | Size: (66.11 MB)
FileName :1 -Recap of our learning.mp4 | Size: (11.59 MB)
FileName :10 -Elbow Method.mp4 | Size: (23.42 MB)
FileName :11 -Performance Metrics in Clustering.mp4 | Size: (23.66 MB)
FileName :12 -Silhouette Score Example.mp4 | Size: (25.36 MB)
FileName :13 -Use case using Silhouette score.mp4 | Size: (28.39 MB)
FileName :2 -Agenda.mp4 | Size: (6.78 MB)
FileName :3 -Distance Measures.mp4 | Size: (49.59 MB)
FileName :4 -Distance Measures Use cases.mp4 | Size: (73.96 MB)
FileName :5 -Use of Distance Measures in Machine Learning.mp4 | Size: (23.79 MB)
FileName :6 -KMeans Clustering Algorithm.mp4 | Size: (26.72 MB)
FileName :7 -Example - Clustering the data using KMeans Clustering Algorithm.mp4 | Size: (22.36 MB)
FileName :8 -KMeans Cost Function.mp4 | Size: (10.94 MB)
FileName :9 -KMeans Use cases.mp4 | Size: (38.34 MB)
FileName :1 -tSNE Introduction.mp4 | Size: (63.05 MB)
FileName :2 -tSNE Algorithm Steps.mp4 | Size: (14.28 MB)
FileName :3 -tSNE use case.mp4 | Size: (22.95 MB)
FileName :4 -tSNE Using the MINIST Dataset.mp4 | Size: (42.37 MB)
FileName :1 -Introduction.mp4 | Size: (18.61 MB)
FileName :10 -Random Forest.mp4 | Size: (62.95 MB)
FileName :11 -Hyperparameters to tune Random Forest.mp4 | Size: (53.63 MB)
FileName :12 -Stacking Ensemble Learning.mp4 | Size: (77.13 MB)
FileName :13 -Use case On Stacking.mp4 | Size: (41.3 MB)
FileName :14 -Boosting.mp4 | Size: (83.99 MB)
FileName :15 -Boosting Algorithm Steps.mp4 | Size: (45.47 MB)
FileName :16 -AdaBoosting Ensemble Learning Model.mp4 | Size: (39.5 MB)
FileName :17 -AdaBoosting Ensemble Learning - Example.mp4 | Size: (47.89 MB)
FileName :18 -Bagging and Boosting Comparison.mp4 | Size: (23.66 MB)
FileName :19 -Gradient Boosting Algorithm.mp4 | Size: (36.29 MB)
FileName :2 -What is Ensemble and Model Error.mp4 | Size: (48.78 MB)
FileName :20 -Gradient Boosting Example.mp4 | Size: (23.93 MB)
FileName :21 -XGBoost Ensemble Learning Method.mp4 | Size: (22.47 MB)
FileName :3 -Bias and Variance Tradeoff.mp4 | Size: (60.36 MB)
FileName :4 -Simple Ensemble Modeling Methods - Voting, Averaging and Weighted Averaging.mp4 | Size: (63.34 MB)
FileName :5 -Random Sampling with Replacement.mp4 | Size: (36.48 MB)
FileName :6 -Use case 1 - Random Sampling with Replacement using customer feedback data.mp4 | Size: (18.74 MB)
FileName :7 -Use case 2 - Understanding the 63 21% Rule in Sampling with Replacement.mp4 | Size: (40.7 MB)
FileName :8 -Bagging.mp4 | Size: (16.69 MB)
FileName :9 -Vanilla Bagging Algorithm.mp4 | Size: (44 MB)

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