General Machine Learning
Explore key machine learning concepts, algorithms, and evaluation methods in this comprehensive introduction to the field.
Supervised Learning
Explore linear regression, logistic regression, perceptron algorithm, and support vector machines in this friendly introduction to key supervised learning concepts.
Introduction to Deep Learning for Everyone
Learn deep learning fundamentals using Keras and TensorFlow in R, covering regression, neural networks, regularization, and convolutional networks for practical applications.
Applied Analytical Programming
Comprehensive introduction to statistical concepts and analytical programming, covering probability, distributions, hypothesis testing, regression, and data visualization techniques.
Data Science Projects in Python
This video teaches these projects by carrying out simultaneous demonstrations and examples to help you get started on the right foot.
Linear Regression vs Logistic Regression - Learn Data Science
This video teaches Linear Regression, Logistic Regression and the key differences between both with a variety of demonstrations & examples to help you get started on the right foot.
PyTorch Tutorials - Complete Beginner Course
Comprehensive guide to PyTorch: from basics to advanced concepts, covering tensors, neural networks, and model deployment. Ideal for beginners seeking practical skills.
PyTorch Zero to All
Learn essential machine learning and deep learning concepts using PyTorch, covering linear models, neural networks, CNNs, and RNNs in a comprehensive series of lectures.
Application of Classification Algorithms
Explore popular classification algorithms, their comparisons, and real-world applications through a case study on employee promotion prediction using various techniques.
Complete Machine Learning in 6 Hours - Krish Naik
Comprehensive machine learning tutorial covering key algorithms, techniques, and practical implementations for regression, classification, clustering, and ensemble methods.
Deep Learning With PyTorch - Full Course
Comprehensive PyTorch tutorial covering fundamentals, advanced concepts, and practical implementations for deep learning, from tensor basics to CNNs and transfer learning.
Machine Learning From Scratch in Python - Full Course With 12 Algorithms
Comprehensive Python implementation of 12 popular machine learning algorithms from scratch, covering KNN, regression, SVM, decision trees, random forests, PCA, K-means, AdaBoost, and LDA.
Ultimate Machine Learning Projects with Python for Beginners - 2024
Master Python-based machine learning through hands-on projects, from basic algorithms to advanced applications like fake news detection, predictive maintenance, and emotion recognition. Build a portfolio with real-world implementations.
Machine Learning
從基礎到高級主題的全面機器學習課程,涵蓋回歸、分類、深度學習、無監督學習等,並包含實際演示和案例研究,幫助學習者掌握關鍵概念和技術。
Classification Algorithm in Hindi
वर्गीकरण एल्गोरिदम की व्यापक समझ: लिनियर रिग्रेशन से लेकर सपोर्ट वेक्टर मशीन तक, सैद्धांतिक अवधारणाओं और पायथन में कार्यान्वयन के साथ।
Machine learning en espanol
Introducción práctica al aprendizaje automático en español, cubriendo técnicas supervisadas y no supervisadas con aplicaciones reales como reconocimiento de imágenes y sistemas de recomendación.
모두를 위한 딥러닝 - PyTorch
PyTorch를 활용한 딥러닝 기초부터 고급 기술까지 실습 중심으로 학습. 텐서 조작, 선형 회귀, CNN, RNN 등 다양한 모델 구현 및 최적화 기법 습득.
Machine Learning in Hindi - Python for Machine Learning
मशीन लर्निंग की मूल अवधारणाओं और पायथन के उपयोग को समझें। पांडास, नंपी, मैटप्लॉटलिब जैसे लाइब्रेरीज़ और लीनियर रिग्रेशन, लॉजिस्टिक रिग्रेशन, डिसीजन ट्री जैसे एल्गोरिदम सीखें।
Machine Learning Full Course in Hindi
मशीन लर्निंग की मूल अवधारणाओं और एल्गोरिदम का व्यापक हिंदी परिचय, जिसमें लीनियर रिग्रेशन, लॉजिस्टिक रिग्रेशन, डिसीजन ट्री, रैंडम फॉरेस्ट, नेइव बेयस और SVM शामिल हैं।
Regression Analysis in Hindi
रिग्रेशन विश्लेषण की मूल अवधारणाओं और उपयोग को समझें। लीनियर और लॉजिस्टिक रिग्रेशन के सिद्धांत और पायथन में प्रैक्टिकल डेमो शामिल हैं।
Logistic Regression in Hindi
लॉजिस्टिक रिग्रेशन की मूल अवधारणाओं को समझे ं, सिग्मॉइड फ़ंक्शन का अध्ययन करें, और पायथन में इसका उपयोग करके वर्गीकरण समस्याओं को हल करना सीखें।