Machine Learning courses

1736 Courses

Coursera

Operationalizing ML Models: MLOps for Scalable AI

In this course you’ll explore how to turn promising ML prototypes into robust, scalable, and maintainable systems that deliver real value. Through hands-on demos, practical tools, and real-world case studies from companies like Netflix, Uber, and Google, you’ll gain a comprehensive understanding of what it takes to run ML systems effectively in pro.

Coursera

GenAI for Risk Managers: Advanced Risk Analysis Techniques

In this course, you’ll discover how Generative AI can enhance risk detection, automate monitoring, and improve decision-making. Through hands-on projects and real-world case studies, you’ll gain practical expertise to apply AI-driven strategies in complex risk environments. Whether you’re a risk professional or business leader, this course will equ.

Coursera

GenAI for Financial Analysts: Essential Predictive Analytics

In this course, you will take on a practical journey into how GenAI transforms financial forecasting, portfolio optimization, risk management, and reporting automation. You’ll explore how top institutions like investment banks, hedge funds, and fintech innovators are already integrating GenAI into their workflows—and you’ll gain the hands-on skills.

Northeastern University

Responsible AI for Mental Health

Unlock the future of AI-driven mental health care while tackling the critical ethical challenges shaping the field today. From bias and misinformation to privacy and patient safety, this course dives into the complexities of AI’s role in mental health. Explore cutting-edge advancements in computing and social robotics, and compare basic and advance.

ESSEC Business School

Advancing Dairy Management with Artificial Intelligence

By completing this course, you will gain a solid understanding of the basics of AI and its role in dairy systems. You will learn to identify key data types used in dairy analytics, such as production, health, and nutrition data. Additionally, you will explore AI applications in various aspects of dairy systems and discuss current AI applications in.

Harvard University

Data Science and Machine Learning

Have you thought about a career in data science and machine learning but didn’t know where to start? Demand for professionals in the machine learning (ML) and artificial intelligence (AI) space is growing exponentially, with no signs of slowing thanks to the ever changing data science landscape. In fact, as the availability of machine learning too.

Harvard University

Python for Data Science and Machine Learning

Coursera

Machine Learning on Google Cloud

What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code? When should you use automated machine learning or custom training? This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build B.

edX

Artificial Intelligence & Machine Learning in Healthcare

In this two-course MicroMasters program, you will learn how Artificial Intelligence (AI) and Machine Learning (ML) can be used to improve healthcare. In the first self-paced course, you’ll build a solid foundation in AI and ML fundamentals, learning how these technologies can enhance both clinical and operational decision-making. You’ll also learn.

AWS Skill Builder

Automating with AI/ML for Small Business Owners

This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey.This course provides small business owners with a high-level overview of generative artificial intelligence (AI). Learners.

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Machine learning use cases

The use of machine learning has touched many areas in our lives. Let's look at the most striking examples of the use of computer intelligence:

Voice assistant Alisa. This system, without human help, will always find the answer to any question of interest, turn on music, answer the call and much more.

Selecting relevant advertising in browsers is not an easy process for a person, but it is quick for a computer. The system will analyze the viewing data and select what a particular user will like. The same system is used on various marketplaces - products that a person viewed or purchased are offered.

Chatbots in various dialogues. For example, many banks use bots to solve customer problems. It's convenient and fast.

Data security: Machine learning models can identify data security vulnerabilities before they become breaches. By analyzing past experiences, machine learning models can predict future high-risk actions so that risk can be mitigated proactively.

Finance: Banks, trading brokers and fintech firms use machine learning algorithms to automate trading and provide financial advisory services to investors. Bank of America uses chatbot Erica to automate customer support.

Healthcare: Machine learning is used to analyze massive medical data sets to speed up the discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human errors. For example, IBM Watson uses data mining to provide doctors with data they can use to personalize patient care.

Fraud detection: AI is used in the finance and banking sector to autonomously analyze large numbers of transactions to identify fraudulent activities in real time. Technology services company Capgemini says fraud detection systems using machine learning and analytics reduce fraud investigation time by 70% and increase detection accuracy by 90% .

Retail: AI researchers and developers are using machine learning algorithms to develop AI recommendation engines that make relevant product suggestions based on shoppers' past choices, as well as historical, geographic and demographic data.

Facial recognition in the subway will help identify violators or criminals in a huge mass of people. Ordinary observers cannot cope with this task. But a fast-learning machine will do this job without any problems.

What do you need for machine learning (ML)?

For those interested in training, there are several requirements to be met in order to be successful in this field. So, there are the main points you need to know about the machine learning course. These requirements include:

Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc.

Average knowledge of statistics and probability.

Basic knowledge of linear algebra in the ml course. In a linear regression model, a line is drawn through all the data points, and that line is used to calculate new values.

Understanding Calculus.

Knowledge of how to clean and structure raw data into the desired format to reduce the time required for decision making.

Machine learning courses from AI Eeducation are the best choice!