What is Natural Language Processing - NLP Full Course
Comprehensive introduction to Natural Language Processing, covering key concepts, techniques, and applications using Python, with hands-on projects in sentiment analysis and chatbot development.
Artificial Intelligence in OBGYN Ultrasound by Suresh Seshadri
Explore AI applications in OBGYN ultrasound imaging, focusing on advanced techniques for improved diagnosis and patient care in obstetrics and gynecology.
Machine Learning
Comprehensive introduction to machine learning concepts, from fundamentals to advanced techniques, with clear explanations and practical applications.
Opioid Use Disorder Prediction Using AI and Existing Risk Models
Comparative study on AI models for predicting opioid use disorder, showcasing superior performance of transformer-based AI over existing clinical tools and other machine learning approaches.
Deep Learning Intuition - Lecture 2
Explore deep learning intuition with Stanford experts, covering logistic regression, face verification, neural architectures, and advanced applications in computer vision and image processing.
Logistic Regression
Learn to predict discrete outcomes using logistic regression, covering coefficients, maximum likelihood, R-squared, p-values, deviance, and practical implementation in R.
Data Analytics with Python
Learn Python-based data analytics, covering statistics, probability, hypothesis testing, regression, ANOVA, clustering, and decision trees. Gain practical skills for data-driven decision making and statistical analysis.
Statistics and Machine Learning in R
If you want to do statistics and machine learning in R, this playlist is for you, and you only. No one else is allowed to watch it.
Gradient Boost
Explore the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.
Explaining and Harnessing Adversarial Examples in Machine Learning - Spring 2021
Explore adversarial examples in machine learning, their impact on linear and non-linear models, and techniques like adversarial training to improve model robustness and generalization.
Evolution of ML Training and Serving Infrastructure at Pinterest Ads
Explore Pinterest Ads' ML infrastructure evolution, from logistic regression to transformer models, and learn key challenges and lessons in revolutionizing ad capabilities.
Low Latency Neural Network Inference for ML Ranking Applications - Yelp Case Study
Explore Yelp's ML platform overhaul for low-latency neural network inference in ranking applications. Learn about Tensorflow model training, deployment using MLEAP, and integration with Nrtsearch for improved performance.
Logistic Regression on Customer Data - Machine Learning Algorithms Using Python
In this video, the we will walk you through a case study on Customer data of a financial institution. The objective of the case study is to reduce expenses by targeting only those customers who are likely to subscribe using predictive modeling.
Supervised Learning Full Course - Supervised Learning Tutorial for Beginners
In this video we will be explaining various advantages in detail. We will also be covering different topics such as, What is Machine Learning, What are their types, Python implementation for Regression, and much more.
Data Science for Beginners - Learn Data Science in 9 Hours
This tutorial on "Data Science for Beginners" would help you to comprehensively master all the concepts of Data Science.
Machine Learning Applications
Explore text mining applications in machine learning, covering techniques, challenges, transformations, and model building using logistic regression and TF-IDF for practical implementation.
Machine Learning with Python for Beginners
Learn Python libraries, machine learning concepts, regression techniques, Naive Bayes, and deep learning fundamentals. Gain practical skills through demonstrations and examples to kickstart your machine learning journey.
Marketing & Retail Analytics
Comprehensive tutorial on marketing analytics, covering RFM analysis, clustering, churn prediction, market basket analysis, and CLV modeling using tools like Tableau and KNIME.
Logistic Regression and Naive Bayes in Machine Learning - Machine Learning Tutorial - Great Learning
Comprehensive tutorial on Logistic Regression and Naive Bayes, covering theory and practical applications in fraud detection and diabetes prediction. Includes machine learning basics and hands-on demos.
Machine Learning
Comprehensive 7-day exploration of key machine learning algorithms, from regression techniques to clustering methods, enhancing data science skills through live interactive sessions.
Generalized Linear Models
Comprehensive exploration of Generalized Linear Models, covering various regression techniques including Probit, Logistic, Complementary Log-Log, Poisson, Ordinal Logistic, and Multinomial Logistic.