Lecture 9 : Binary Classification | LogisticRegression | Sigmoid | Step function | Complete Project

Описание: Lecture 9 : Binary Classification | LogisticRegression | Sigmoid | Step function | Complete Project

Lecture Work:
Notes : https://miro.com/app/board/uXjVJ0oa8b...

Colab Notebook : https://colab.research.google.com/dri...
Frontend Code : https://github.com/banvro/9-20-to-11-...

Used Dataset : https://www.kaggle.com/datasets/sahil...

Streamlit :    • Streamlit - 1 | Complete Streamlit | All f...  

Machine Learning Playlist :    • Machine Learning Using Python  

In this lecture you will learn binary classification end-to-end and build a complete Logistic Regression project with a user-friendly web UI using streamlit. We start from the basics — what binary classification is — then derive the logistic regression formulation, explain the sigmoid activation, cost function, and gradient descent. Finally, we implement a full project with dataset loading, model training, evaluation, and a Django-based UI to upload data, make predictions, and view model metrics.

TimeStemps:
0:00 – 1:57 — Basic Intro
1:57 – 4:25 — Logistic Regression
4:25 – 12:02 — Binary Classification
12:02 – 26:05 — Step Function (Sigmoid Function)
26:05 – 28:36 — Finding Dataset
28:36 – 40:35 — Model Building
40:35 – End — Build Streamlit Web UI for Project

What you’ll learn in this video
What is Binary Classification and common use-cases
Logistic Regression intuition and when to use it
Sigmoid function (logistic function) and interpretation of outputs as probabilities
Log-loss (binary cross-entropy) cost function and why it’s used
Gradient Descent for finding optimal weights (including learning rate)

More Playlist:
Python Playlist :    • Python in 45 Days Seminars  
Pandas :    • Pandas  
Pandas Data Analysis Process :    • Pandas Data Analysis Process  
Django Playlist :    • Django  
CSS Playlist :    • CSS  
HTML Playlist :    • HTML in 5 Lectures  
Bootstrap :    • Bootstrap  

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A practical lecture on binary classification and logistic regression: intuition, sigmoid, log-loss, gradient descent, evaluation metrics, and a complete Django-based UI project (upload data, train, predict, and visualize results).