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Supervised Machine Learning topics

Track :

Computer Science

Lessons no : 16

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What will you learn in this course?

Understand core concepts of supervised machine learning including regression and classification techniques
Apply algorithms like linear regression, logistic regression, and decision trees to real-world datasets
Build and train predictive models using popular machine learning libraries and tools in Hindi
Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score

Preprocess and clean labeled data to improve model accuracy and robustness
Implement techniques for overfitting prevention and model optimization in supervised learning
Solve practical problems across domains like finance, healthcare, and marketing using supervised models
Interpret and communicate machine learning results effectively for data-driven decision making

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Lessons | 16

Supervised Machine Learning Tutorial for Beginners Part 13 Objective of Support Vector Machine

Supervised Machine Learning Tutorial for Beginners Part 13 Objective of Support Vector Machine

Supervised Machine Learning Tutorial Part 15 Hands on SVM Dataset and Pair Plotting

Supervised Machine Learning Tutorial Part 15 Hands on SVM Dataset and Pair Plotting

Supervised Machine Learning Tutorial for Beginners Part 16 Hands on SVM Building model

Supervised Machine Learning Tutorial for Beginners Part 16 Hands on SVM Building model

Supervised Machine Learning Tutorial for Beginners Part 14 Functional Margin Great Learning

Supervised Machine Learning Tutorial for Beginners Part 14 Functional Margin Great Learning

Supervised Machine Learning Tutorial Part 11 Hands on Exercise on Logistic Regression

Supervised Machine Learning Tutorial Part 11 Hands on Exercise on Logistic Regression

Supervised Machine Learning Tutorial Part 12 Advantages and Disadvantages of Logistic Regression

Supervised Machine Learning Tutorial Part 12 Advantages and Disadvantages of Logistic Regression

Supervised Machine Learning Tutorial for Beginners Part 8 Hands on Linear Regression

Supervised Machine Learning Tutorial for Beginners Part 8 Hands on Linear Regression

Supervised Machine Learning Tutorial for Beginners Part 7 Concept of Squared Errors

Supervised Machine Learning Tutorial for Beginners Part 7 Concept of Squared Errors

Supervised Machine Learning Tutorial for Beginners Part 6 How to Get the Best Fit Line

Supervised Machine Learning Tutorial for Beginners Part 6 How to Get the Best Fit Line

Supervised Machine Learning Tutorial Part 9 Why Logistic Regression is a Classification Technique

Supervised Machine Learning Tutorial Part 9 Why Logistic Regression is a Classification Technique

Supervised Machine Learning Tutorial for Beginners Part 2 Why the Name Linear Regression

Supervised Machine Learning Tutorial for Beginners Part 2 Why the Name Linear Regression

Supervised Machine Learning Tutorial for Beginners Part 10 Logloss Function its Interpretation

Supervised Machine Learning Tutorial for Beginners Part 10 Logloss Function its Interpretation

Supervised Machine Learning Tutorial Part 4 How Slope of Line is Measured in Linear Regression

Supervised Machine Learning Tutorial Part 4 How Slope of Line is Measured in Linear Regression

Supervised Machine Learning Tutorial for Beginners Part 5 Coefficient of Correlation

Supervised Machine Learning Tutorial for Beginners Part 5 Coefficient of Correlation

Supervised Machine Learning Tutorial Part 3 Linear Models are Linear in Terms of Coefficient

Supervised Machine Learning Tutorial Part 3 Linear Models are Linear in Terms of Coefficient

Supervised Machine Learning Tutorial for Beginners Part 1 What is Supervised Machine Learning

Supervised Machine Learning Tutorial for Beginners Part 1 What is Supervised Machine Learning

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Muhammad Maaz Ul Haq

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2024-12-26

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Supervised Machine Learning course in hindi, in this course is a fundamental course where participants delve into the core concepts of this branch of AI. Through lectures and practical exercises, students explore various algorithms and techniques used in supervised learning, including regression and classification. They learn to build predictive models from labeled data, understand model evaluation methods, and tackle real-world problems using popular tools and libraries. By the end, students gain a solid understanding of supervised learning principles and their applications in diverse domains.