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Machine Learning Algorithms Solved Numerical Examples
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.47 GB | Duration: 2h 43m
Master Machine Learning algorithms through step-by-step solved numerical examples for university exams and problem-solvi
What you'll learn
Solve numerical problems related to Machine Learning algorithms using step-by-step solution techniques.
Apply Machine Learning algorithms such as KNN, Naïve Bayes, Logistic Regression, and SVM to solve numerical examples.
Develop confidence in solving university examination-oriented Machine Learning numerical questions accurately and systematically.
Analyze and interpret Machine Learning numerical problems by following a structured problem-solving approach.
Requirements
1.Basic understanding of Machine Learning concepts and terminology.
Familiarity with common Machine Learning algorithms is recommended.
Interest in practicing numerical problem solving for university examinations or interviews.
A notebook, pen, and calculator are recommended to work through the solved examples.
Description
This course contains the use of artificial intelligence.This course uses artificial intelligence (AI) tools to support the creation of course content, including instructional materials, examples, and learning resources. All content has been reviewed and curated by the instructor to ensure accuracy and educational value.Machine Learning Algorithms: Solved Numerical Examples is a practice-oriented course designed for learners who want to strengthen their problem-solving skills through step-by-step worked examples. Unlike theory-based or programming-focused courses, this course concentrates exclusively on solving numerical problems commonly encountered in university examinations and academic assessments.The course covers solved examples on key Machine Learning algorithms, including K-Nearest Neighbors (KNN), Weighted KNN, Naïve Bayes, Bayesian Belief Networks, Logistic Regression, Artificial Neurons, Perceptron Learning, Gradient Descent, and Support Vector Machines (SVM). Each example is explained in a structured manner, enabling learners to understand the calculations, reasoning, and solution process with ease.This course is ideal for undergraduate and postgraduate students in Computer Science, Information Technology, Artificial Intelligence, Data Science, and related disciplines. It is also beneficial for learners preparing for semester examinations or anyone seeking to improve their confidence in solving Machine Learning numerical problems.A basic understanding of Machine Learning concepts is recommended, as the course focuses on practical problem solving rather than theoretical explanations or programming implementations. By the end of the course, learners will be equipped with the skills to confidently approach and solve a variety of Machine Learning numerical problems. The course speaks lot about mathematics rather than just theory. Its all about understanding and pitching into practice.
Undergraduate and postgraduate students studying Machine Learning, Artificial Intelligence, or Data Mining.,Engineering, Computer Science, IT, MCA, and Data Science students preparing for university examinations.,Learners who want to improve their Machine Learning numerical problem-solving skills through worked examples.,Anyone looking for step-by-step solutions to common Machine Learning numerical questions.
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