Introduction to Machine Learning
Автор: Karungi Irene
Загружено: 2026-02-20
Просмотров: 23
Описание:
Machine Learning with Python – Course Overview & Traffic Prediction Project
In this video, I present an overview of my key learnings from the Alison Machine Learning course, covering both the theoretical foundations and practical applications of Machine Learning using Python.
🔹 Introduction to Machine Learning
What is Machine Learning?
Deterministic vs Probabilistic models
Supervised vs Unsupervised Learning
Regression modeling steps
The goal of ML: Generalization to unseen data
🔹 Key Algorithms Explained
K-Nearest Neighbours (KNN) – Distance metrics, hyperparameters, curse of dimensionality
Decision Trees – Gini Impurity, Entropy, Information Gain, Pruning
Ensemble Learning – Bagging, Boosting, and Random Forest
Support Vector Machines (SVM) – Soft margins, Kernel functions, Gamma effects
K-Means Clustering – Strengths and weaknesses
Principal Component Analysis (PCA) – Dimensionality reduction and real-world applications
🌍 Real-World Applications Covered
Fraud detection
Video classification
Crop classification
Facial recognition
Image compression
Traffic prediction systems
Повторяем попытку...
Доступные форматы для скачивания:
Скачать видео
-
Информация по загрузке:
