Machine Learning courses

1335 Courses

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Responsible AI for Developers: Privacy & Safety - 日本語版

Responsible AI for Developers: Privacy & Safety - 日本語版このコースでは、AI のプライバシーと安全性に関する重要なトピックを紹介します。具体的には、Google Cloud プロダクトとオープンソースツールを使用して AI のプライバシーと安全性の推奨プラクティスを実装するための実践的な方法とツールを検証します。提供元: Coursera カテゴリ: 人工知能コース,.

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Responsible AI for Developers: Privacy & Safety - Italiano

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Responsible AI for Developers: Privacy & Safety - 한국어

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Responsible AI for Developers: Privacy & Safety - Українська

Відповідальний ШІ для Розробників: Конфіденційність та Безпека - Українська Під час цього курсу ви ознайомитеся з важливими темами, що стосуються конфіденційності й безпеки в системах ШІ. Ви дізнаєтеся про практичні методи й інструменти, які дають змогу застосувати рекомендації щодо конфіденційності й безпеки в системах.

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Responsible AI: Interpretability & Transparency - Deutsch

Responsible AI: Interpretability & Transparency - Deutsch In diesem Kurs werden Konzepte in Bezug auf die Interpretierbarkeit und Transparenz von künstlicher Intelligenz vorgestellt. Sie erfahren, warum die Transparenz der KI für Entwickler-Teams wichtig ist. Dabei lernen Sie praktische Techniken und Tools kennen, mit denen Sie sowohl die Interp.

edX

IA para todos: domina los conceptos básicos

En este MOOC, aprenderás qué es la IA y comprenderás sus aplicaciones y casos de uso y cómo está transformando nuestras vidas. Explorarás los conceptos básicos de la IA, como el aprendizaje automático, el aprendizaje profundo y las redes neuronales, así como los casos de uso y las aplicaciones de la IA. Estarás expuesto a las preocupaciones que r.

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Smart Analytics, Machine Learning and AI on GC - 한국어

Smart Analytics, Machine Learning and AI on GC - 한국어 머신러닝을 데이터 파이프라인에 통합하면 데이터에서 더 많은 인사이트를 도출할 수 있습니다. 이 과정에서는 머신러닝을 Google Cloud의 데이터 파이프라인에 포함하는 방법을 알아봅니다. 맞춤설정이 거의 또는 전혀 필요 없는 경우에 적합한 AutoML에 대해 알아보고 맞춤형 머신러닝 기능이 필요한 경우를 위.

Coursera

Smart Analytics, Machine Learning, and AI on GCP - Italiano

Smart Analytics, Machine Learning, and AI on GCP - Italiano Integrare il machine learning nelle pipeline di dati aumenta significativamente la capacità di estrarre insight preziosi dai dati. Questo corso esplora vari metodi per includere il machine learning nelle pipeline di dati su Google Cloud. Per una personalizzazione minima o nulla, il co.

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Responsible AI for Developers: Privacy & Safety - 简体中文

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Responsible AI for Developers: Privacy & Safety - Polski

Responsible AI for Developers: Privacy & Safety - Polski To szkolenie wprowadza w ważne kwestie dotyczące prywatności i bezpieczeństwa w dziedzinie AI. W jego trakcie przedstawiamy praktyczne techniki i narzędzia, które umożliwiają wdrożenie sprawdzonych metod w zakresie prywatności i bezpieczeństwa AI przy użyciu usług Google.

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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!