Machine Learning courses

1795 Courses

Udemy

Complete Generative AI for upskilling & learning initiatives

Master Generative AI in Personalized Learning and Upskilling and Learn Various types of Generative AI Models!

Udemy

edX

Predicting Failures with Machine Learning

Engineers are tasked with minimizing downtime and enhancing operational reliability. Predictive maintenance has become a powerful way to predict equipment failures before they occur, leading to increased efficiency and cost savings. With operational efficiency being crucial to competitiveness, companies are seeking engineers with skills in applyin.

edX

Detecting Anomalies with Machine Learning

Engineers are responsible for detecting abnormalities and ensuring reliability in the products they produce. Developments in artificial intelligence (AI) provide new tools for engineers to quicky identify anomalies and anticipate maintenance needs, preventing costly downtime and failures. Acquiring these skills will enable you to remain competitiv.

edX

Udemy

60 PDUs for your next PMP Renewal 2.0

Renew Your PMP Certification 2.0 : Get 60 PMP PDUs You Need To Satisfy PMI Requirements

Udemy

AI & ML Made Easy: From Basic to Advanced (2025)

Learn AI and Machine Learning Fundamentals to Advanced Deep Learning with Real-World Projects

Udemy

Coursera

Teaching AI on the Edge

Mobile and edge devices are already able to deploy large language models (LLMs) in artificial intelligence (AI) applications that will have a transformational impact on society. How can academia prepare the next generation of engineers to leverage the opportunities and address the challenges presented by AI on the Edge? In this course, Dr. Catherin.

Coursera

Gen AI: Unlock Foundational Concepts

Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leaders learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google.

Coursera

edX

Azure AI Fundamentals: Build & Deploy AI

This course offers a comprehensive introduction to artificial intelligence (AI) concepts and the array of Azure services available for developing AI solutions. Designed for individuals with both technical and non-technical backgrounds, this course requires no prior experience in data science or software engineering. It is ideal for professionals l.

edX

Google Cloud Skills Boost

Gen AI: Unlock Foundational Concepts

Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google.

Google Cloud Skills Boost

Bachelor of Science in Computer Science (BCS) Online Degree

Expand your knowledge by learning about innovative software, computer programming and technology to give you a critical advantage in a popular industry. Become fluent in current programming languages like Java and Python, explore database management, and dive into the latest concepts in AI and machine learning. This degree is perfect for working p.

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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!