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Learn Logistic Regression online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
DeepLearning.AI
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural ne…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
Stanford University
Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R. Course material updated in 2021 for second edition of the course textbook.
DeepLearning.AI
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regr…
View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Dimitris Bertsimas Students of this course learn how to use data and analytics by examining real world examples and utilizing methods including linear and logistic regression, trees, text analytics, clustering, visualization, and o…
Stanford University
Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.
upGrad
Free Logistic Regression Course covers the concept of Logistic Regression and its applications. Learn topics such as univariate & multi-variate logistic regression
These videos pick up where Linear Regression and Linear Models leave off. Now, instead of predicting something continuous, like age, we can predict something discrete, like whether or not someone will enjoy the movie Troll 2.
Columbia University
Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.
Google
This is the fourth course in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable…
Great Learning
In this course, we will cover the fundamentals of supervised machine learning and dive deeper into two popular algorithms: logistic regression and Naïve Bayes. We will start with an overview of supervised learning and explore the key concepts and terminology used in this area of machine learning. F…
IBM
Become proficient with tensor operations in PyTorch. Learn how to build and train linear regression models from scratch, apply logistic regression for classification tasks, and handle data efficiently while optimizing models using gradient descent techniques.
Udacity
The linear and logistic regression course offers a detailed introduction to fundamental statistical and machine learning algorithms, particularly focusing on regression techniques. The course begins with simple linear regression and progresses to multiple linear regression, equipping students with…
Great Learning
When a model must place new observations into the right category, a classification algorithms course gives that decision a clear structure. This free course walks through logistic regression, Naive Bayes, decision trees, random forests, KNN, SVM, preprocessing, and comparison parameters so beginner…
Indian Institute of Management Bangalore
Learn the fundamental concepts in probability, statistics, optimization and linear algebra which form the foundations for data science.
Duke University
This course provides a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) and demonstrates how they can solve complex problems in various industries, from medical diagnostics to image…
Great Learning
Many have these questions: What is Logistic Regression? How does Logistic Regression works? Logistic Regression is a vital part of the applications that we have in Machine Learning today. It forms to be a part of the supervised learning algorithms that use labeled datasets to help with regression a…
Great Learning
Machine Learning is at the heart of Decision Making and Data Science. It is used for various prediction models, like customer churn prediction, etc. Under its umbrella of different supervised and unsupervised algorithms lies the concept of logistic regression, which is essential in dealing with cat…
University of Washington
Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan def…
