Foundations of Machine Learning: Logistic Regression By Dr Adnan Amin

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Dr. Adnan Amin · Instructor

Foundations of Machine Learning: Logistic Regression By Dr Adnan Amin

Master Logistic Regression with hands-on Python, covering theory, regularization, optimization, and real-world applicatiion

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You will not just learn theory — you will build, experiment, and truly understand how the algorithm works.

What you will learn:

1. Introduction to Machine Learning
2. Types of Machine Learning
3. Introduction to Classification
4. Real-world applications of ML and Classification
5. Classification Algorithm: Logistic Regression
6. Logistic Regression and Loss Function (Binary Cross-Entropy)
7. Logistic Regression and Gradient Descent Optimization
8. Overfitting and Underfitting in Machine Learning
9. Prediction Error Decomposition (Bias–Variance Tradeoff)
10. Regularization in Logistic Regression
11. Elastic Net Regularization
12. Model evaluation and performance metrics
13. Implementation using Python (Scikit-learn)

This course will follow an instructor-led approach, combining recorded lectures with interactive learning.

Each week, students will be provided with pre-recorded video lectures covering the core concepts and topics. These lectures are expected to be reviewed before the scheduled session.

In addition, there will be a weekly live online session between the instructor and students. These sessions are designed to:

* Address student questions and clarify concepts
* Discuss challenging topics in detail
* Provide additional insights and real-world context
* Support interactive learning and engagement

Students are encouraged to actively participate in these sessions and come prepared with questions based on the recorded lectures.

What you’ll learn

  • ✓ Python
  • ✓ Colab
  • ✓ Kaggle
  • ✓ Machine Learning Libraries (Python)

Program content

14 steps · 14 required

  1. 1

    Course Overview

    youtube · 4 min

  2. 2

    Introduction to Machine Learning

    youtube · 7 min

  3. 3

    Types of Machine Learning

    youtube · 8 min

  4. 4

    Quiz 1

    quiz · 10 min

  5. 5

    Introduction to Classification

    rich text · 10 min

  6. 6

    Real-world applications of ML and Classification

    rich text · 10 min

  7. 7

    Classification Algorithm: Logistic Regression

    rich text · 10 min

  8. 8

    Logistic Regression and Loss Function (Binary Cross-Entropy)

    rich text · 10 min

  9. 9

    Overfitting and Underfitting in Machine Learning

    rich text · 10 min

  10. 10

    Prediction Error Decomposition (Bias–Variance Tradeoff)

    rich text · 10 min

  11. 11

    Regularization in Logistic Regression

    rich text · 10 min

  12. 12

    Elastic Net Regularization

    rich text · 10 min

  13. 13

    Model evaluation and performance metrics

    rich text · 10 min

  14. 14

    Implementation using Python (Scikit-learn)

    rich text · 10 min