Machine Learning Basics (Coursera)

Coursera

Google Cloud

Introduction to Image Generation (Coursera)

CS: Information & Technology

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

Nov 9th 2026

3 Weeks

Machine Learning Coursera Plus Image Generation

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Coursera

IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

Data Science

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Nov 9th 2026

4 Weeks

Python Artificial Intelligence Prototyping

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Coursera

Sungkyunkwan University - SKKU

Using R for Regression and Machine Learning in Investment (Coursera)

Economics & Finance Business

In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning.

Nov 16th 2026

2 Weeks

Machine Learning Regression Investment

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Coursera

University of London

Machine Learning for All (Coursera)

Data Science Computer Science

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it.

Nov 16th 2026

4 Weeks

Artificial Intelligence Machine Learning AI Technology

Details

Coursera

Yonsei University

Deep Learning for Business (Coursera)

Management & Leadership

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers.

Nov 16th 2026

5-12 Weeks

Business ML Artificial Intelligence

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Coursera

Sungkyunkwan University - SKKU

Machine Learning for Smart Beta (Coursera)

Economics & Finance Business

In this 4 week course, you will learn about Smart Beta products. Smart betas products have the characteristics of both passive investment(having predetermined rules) and active investments(allows for factor investment). We will walk through the creation mechanisms behind different smart beta products and recreate some of them using R programming.

Nov 16th 2026

4 Weeks

Machine Learning Regression Classification

Details

Coursera

University of Michigan

Applied Machine Learning in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial.

Nov 9th 2026

4 Weeks

Python ML Machine Learning

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Coursera

DeepLearning.AI

Generative AI with Large Language Models (Coursera)

Robotics & Computer Vision

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

Nov 16th 2026

3 Weeks

Machine Learning Python Programming Large Language Models

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Coursera

Johns Hopkins University

Advanced Linear Models for Data Science 1: Least Squares (Coursera)

Statistics & Data Analysis Data Science

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Nov 16th 2026

5-12 Weeks

Linear Algebra Linear Regression Data Science

Details

Coursera

University of Maryland, College Park

Combining and Analyzing Complex Data (Coursera)

Statistics & Data Analysis

In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.

Nov 16th 2026

4 Weeks

Ethics Models Data Analysis

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Coursera

University of Illinois at Urbana-Champaign

Accounting Data Analytics with Python (Coursera)

Statistics & Data Analysis Data Science

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).

Nov 9th 2026

5-12 Weeks

Python SQL Linear Regression

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Coursera

Stanford University

Probabilistic Graphical Models 2: Inference (Coursera)

Statistics & Data Analysis Data Science

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

Nov 9th 2026

5-12 Weeks

Machine Learning PGM Inference

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