Lecture 9 : Binary Classification | LogisticRegression | Sigmoid | Step function | Complete Project скачать в хорошем качестве

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

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   If this video helps you, please like, comment, and subscribe for more machine learning lectures and full-stack ML projects. #LogisticRegression #BinaryClassification #Sigmoid #MachineLearning #DataScience #Python #scikitlearn #GradientDescent #ModelEvaluation #Django #MLProject #ROC #AUC #ConfusionMatrix #MachineLearningTutorial #BinaryClassification #LogisticRegression #SigmoidFunction #LogLoss #CrossEntropy #GradientDescent #ModelTraining #FeatureScaling #StandardScaler #Pandas #NumPy #ScikitLearn #MachineLearningProject #DjangoProject #DjangoUI #MLWebApp #UploadDataset #PredictiveModel #ConfusionMatrix #Precision #Recall #F1Score #ROC #AUC #Thresholding #DecisionBoundary #ClassImbalance #SMOTE #Oversampling #Undersampling #Regularization #L1 #L2 #ModelSelection #TrainTestSplit #CrossValidation #HyperparameterTuning #LearningRate #PythonMachineLearning #DataScienceProjects #AI #ArtificialIntelligence #SupervisedLearning #MLLecture #MLEducation #MLFromScratch #CodingTutorial #FullStackML #WebAppForML #DeploymentBasics #SQLite #PostgreSQL #ModelInterpretation #ProbabilityOutput 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).