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Token Parameters in LLama3 META Models 8B 70B Parameters Model GPT model

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Lesson extensions

Lessons List | 38 Lesson

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Shivani Bhoyar

This course is very helpful,i learned lots of things from this .
2025-09-12

Pratham Parab

The concept was great.
2025-07-25

Biplab Sutradhar

Best for Beginner.
2025-06-25

RAHMAN MUSTAFIZUR

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2025-05-13

Kateryna Vavryniuk

Really helpful
2025-04-10

Mrs P.Murugeswari

Good
2025-03-27

Rajkumar Devarajan

Excellent
2025-03-27

Samruddhi Nichal

Helpful course
2025-01-15

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This a great course to learn ML and DS , thank you for this .
2025-01-03

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2024-12-30

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2024-12-27

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It's very good.
2024-12-26

Course Description

Supervised Learning algorithms , in this course provides a comprehensive introduction to supervised learning algorithms, a core area of machine learning. You’ll explore key concepts, including how models are trained on labeled data to predict outcomes for new inputs. The course covers popular algorithms such as Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Gradient Boosting methods like XGBoost. Learn how to solve classification and regression problems, evaluate model performance using metrics, and optimize hyperparameters. With practical examples and case studies, this course equips you with the skills to apply supervised learning techniques to real-world tasks such as fraud detection, healthcare analytics, and predictive modeling.