Udemy – Mastering Machine Learning Algorithms 2025-4

Udemy – Mastering Machine Learning Algorithms 2025-4 Downloadly IRSpace

Aug 17, 2025 - 02:22 Updated: Aug 19, 2025 - 14:18

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Udemy – Mastering Machine Learning Algorithms 2025-4

Mastering Machine Learning Algorithms. This course helps participants master the most important algorithms in the field by learning the fundamental concepts, mathematical understanding, and practical applications of machine learning models. Suitable for beginners and experts, this course begins with a comprehensive introduction to machine learning and then explores widely used supervised and unsupervised algorithms, including linear and logistic regression, decision trees, random forests, KNN, Naïve Bayes, K-Means clustering, dimensionality reduction (t-SNE), and advanced techniques such as Bagging, Boosting, and XGBoost. Each algorithm is taught with real-world examples, evaluation methods, and implementation in Python with libraries such as Scikit-Learn. Participants are also introduced to cross-validation methods for improving models. By the end of the course, they will be able to understand the logic of algorithms, choose the right method for different problems, implement models, and use machine learning in real-world projects. This course is designed for data science students, analysts, developers, and professionals who want to strengthen their machine learning skills.

What you will learn

A thorough understanding of fundamental machine learning concepts, including the principles of classification and regression.
Learn key terms 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 linear regression, KNN, decision trees, support vector machines, and more.
Gain practical skills by implementing machine learning algorithms using industry-standard programming tools and languages such as Python, Scikit-learn, etc.
Work on real datasets to gain practical experience in data preprocessing, model training, and performance metrics evaluation.

This course is suitable for people who:

People 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 such as decision trees for classification and regression tasks.
Programmers and software developers who are interested in incorporating machine learning into their applications or better understanding how decision trees work.
Students studying data science, computer science, or related fields who want to deepen their knowledge in machine learning algorithms, especially decision trees.
Enthusiasts and lifelong learners who have a general interest in machine learning and want to explore decision trees as part of their broader understanding of the field.

Mastering Machine Learning Algorithms Course Details

Publisher: Udemy
Instructor: Pralhad Teggi
Training level: Beginner to advanced
Training duration: 9 hours and 43 minutes
Number of lessons: 99

Course headings

Prerequisites for the Mastering Machine Learning Algorithms course

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.

Udemy – Mastering Machine Learning Algorithms 2025-4