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Learn Supervision online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.
Deep learning has achieved remarkable success in supervised and reinforcement learning problems including image classification, speech recognition, and game playing. These models are, however, to a large degree, specialized for the single task they are trained for. This course will cover the settin…
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
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this cours…
Great Learning
This free sentiment analysis using Python course helps learners learn everything from scratch. First, you will go through what Machine Learning is and its categories. You will dive into supervised and unsupervised Machine Learning and understand its various categories. You will then get introduced…
Great Learning
Support vector machines or support vector networks are models based on supervised learning models that are primarily used for classification and regression analysis. Support vector machine or SVM is a robust and highly efficient prediction method with a firm foundation in statistical methods It was…
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…
IBM
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best…
MIT OpenCourseWare
Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification.…
IIT Hyderabad
Computer Science and Engineering course by Prof. Sayantee Jana.
Great Learning
Reinforcement Learning in Python is an eminent area of modern research in artificial intelligence. It differs from supervised and unsupervised learning but is about how humans learn in real life. It is the science of decision-making and allows the creation of optimal behavior simulations to obtain…
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…
Delft University of Technology
Learn the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning and deep learning to solve real-life problems.
MIT OpenCourseWare
This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse d…
Delft University of Technology
Learn the fundamentals of machine learning to help you correctly apply various classification and regression machine learning algorithms to real-life problems using the Python toolbox scikit-learn.
University of Alberta
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generali…
MIT OpenCourseWare
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed i…
IBM
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI appl…
Microsoft
This course covers the core algorithms and techniques used in AI and ML, including approaches that use pre-trained large-language models (LLMs). You will explore supervised, unsupervised, and reinforcement learning paradigms, as well as deep learning approaches, including how these operate in pre-t…
MITx
Learn how neural networks form internal representations, how those representations transfer to new tasks, and how useful features can be learned without labels. Part four in a five-part series, this online course in the Deep Learning and Generative Models program covers representation analysis, aut…
University of California San Diego
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statist…
