Machine Learning courses

1770 Courses

Pluralsight

Generative AI Foundations: Generative AI in Action

Generative AI Foundations: Generative AI in Action Advance your AI knowledge with the course "Generative AI Foundations: Generative AI in Action." Move beyond the basics of GenAI chatbot interactions and delve into practical, real-world applications. Learn how to utilize Generative AI Agents and Retrieval-Augmented Generation (RAG) to implement.

edX

Data Science For Business Professionals

In today’s fast-paced digital economy, data is at the heart of every decision, making Data Science one of the most in-demand fields. Whether you are looking to enter the field of Data Science, improve your business analysis skills, or apply Machine Learning techniques to solve business challenges, this course will provide you with the essential.

Microsoft Learn

GitHub responsible AI

Module 1: Introduction to AI Workloads and Azure AI Services. Explore Microsoft's Responsible AI policies and learn about AI solutions and practices for ethical AI. Module 2: Using Azure AI Face. Gain skills in detecting and analyzing faces using Azure AI Face service. Module 3: Foundations of Generative AI. Understand large language mo.

AWS Skill Builder

Digital Classroom - Practical Data Science with Amazon SageMaker

Artificial intelligence and machine learning (AI/ML) are now widespread, and this course immerses students in the daily life of a data scientist. You'll collaborate effectively with data scientists and develop applications that integrate with ML. Learn how data scientists create ML solutions on Amazon Web Services (AWS) using Amazon SageMaker. Expe.

Coursera

IBM Generative AI Engineering

Embark on a transformative learning journey with the IBM Generative AI Engineering Professional Certificate, an opportunity tailor-made for aspiring generative AI engineers, AI developers, data scientists, machine learning engineers, and AI research engineers. With the generative AI market poised for a significant growth of over 46% CAGR until.

edX

Advanced Principles & Topics in AI & Machine Learning in Healthcare

Delve into the core of advanced AI and ML principles tailored for the healthcare sector. This course offers a comprehensive study on sophisticated machine learning methodologies and the operational aspects of AI systems, while discussing its transformative role in healthcare's future. Join the conversation in pivotal debates, partake in intera.

Coursera

Vertex AI Studio'ya Giriş

Bu kursta, Vertex AI Studio'nun potansiyelini keşfedecek ve üretken yapay zeka iş akışları için araçlar geliştirerek projelerinizde nasıl uygulayabileceğinizi öğreneceksiniz. İlgi çekici dersler ve sürükleyici demolar sayesinde, bu platformu etkin bir şekilde kullanarak birçok formatı destekleyen Gemini uygulamaları geliştirebileceksiniz. Ayrı.

Coursera

Google Cloud ile Yapay Zeka İlkelerinin Uygulanması

Swayam

Advances in Additive Manufacturing of Materials: Current status and emerging opportunities

About the Course: Additive manufacturing (AM) refers to the layer-by-layer deposition of materials to create objects as per their design, playing crucial roles in various sectors, such as aerospace, transportation, energy, and healthcare. Various AM processes are utilized globally for these applications. This course, founded on extensive research.

edX

Introduction to AI & Machine Learning in Healthcare

Introduction to AI & Machine Learning in Healthcare Embark on a journey to understand the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in the healthcare sector with this comprehensive introductory course. As the first part of a two-course program, it offers a foundational understanding of AI and ML's pivotal ro.

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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!