Machine Learning courses

1795 Courses

AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Español LATAM)

Después del 28 de marzo, los títulos de los cursos solo estarán disponibles en inglés. Sin embargo, las descripciones de los cursos permanecerán disponibles en su idioma preferido para que pueda consultarlas.

AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Português)

Depois de 28 de março, os títulos dos cursos estarão somente em inglês. No entanto, as descrições dos cursos permanecerão disponíveis no idioma de sua preferência para permitir que você pesquise nesse idioma.

AWS Skill Builder

Udemy

AI & Cognitive Science: Bridging Minds and Machines

Explore the Intersection of Artificial Intelligence and Human Cognition to Unlock New Frontiers in Technology

Udemy

Theoretical Foundations of AI in Cybersecurity

Unlock the Power of AI: Strengthen Cybersecurity with Theoretical Insights and Advanced Techniques

Udemy

Master Generative AI for RPA from basics to Advanced

Learn Efficient Automation and Optimization with Generative AI for RPA and Learn organizational workflows and Automation

Udemy

Master Generative AI with Java and Spring Boot | Spring AI

Use JAVA and Spring to Build Artificial Intelligence (AI) driven Applications and its lifecycle - Generative AI

Udemy

Custom ChatGPT Publishing & AI Bootcamp Masterclass

Learn Python, AI basics, ChatGPT customization, and build 150+ hands-on projects with zero prior experience.

Udemy

AWS Certified AI Practitioner - AIF-C01

Prepare yourself for the AWS Certified AI Practitioner certification exam

Udemy

GenAI and Cybersecurity – Frameworks and Best Practices 2025

Driving Enterprise GenAI Adoption: Tools, Frameworks, and Real-World Case Studies from Industry Leaders

Udemy

Generative AI for upskilling and learning initiatives

Be an expert at Generative AI in learning & upskilling & Learn how to use Generative AI in learning and development

Udemy

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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!