Machine Learning courses

814 Courses

Introduction to AI and Machine Learning on GC - Português

Neste curso, apresentamos os recursos de IA e machine learning (ML) no Google Cloud que criam projetos de IA generativa e preditiva. Vamos conhecer as tecnologias, os produtos e as ferramentas disponíveis em todo o ciclo de vida de dados à IA, o que inclui os fundamentos dessa tecnologia, o desenvolvimento e as soluções dela. O objetivo é aj.
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Fundamentos de machine learning e inteligência artificial (Português) | Fundamentals of Machine Learning and Artificial Intelligence (Portuguese)

Fundamentos de Machine Learning e Inteligência Artificial (Português) | Fundamentals of Machine Learning and Artificial Intelligence (Portuguese) Neste curso, você aprenderá sobre os fundamentos de machine learning (ML) e inteligência artificial (IA). Você explorará as conexões entre IA, ML, aprendizado profundo e o campo emergente da inteligência.
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Databricks Concepts

Databricks Concepts Discover the potential of the Databricks Lakehouse Platform and its ability to revolutionize your data processes through the Databricks Concepts course. This program is designed to guide you from start to finish, demonstrating how the Databricks Lakehouse provides a unified, scalable, and high-performance solution for a variet.
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Introduction to AI and Machine Learning on GC - 한국어

Introduction to AI and Machine Learning on GC - 한국어 이 과정에서는 예측 및 생성형 AI 프로젝트를 모두 빌드하는 Google Cloud 기반 AI 및 머신러닝(ML) 제품군을 소개합니다. AI 기반, 개발, 솔루션을 모두 포함하여 데이터에서 AI로 이어지는 수명 주기 전반에 걸쳐 사용할 수 있는 기술과 제품, 도구를 살펴봅니다. 이 과정의 목표는 흥미로운 학습 경험과 실제적.
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Artificial Intelligence

Artificial Intelligence Designed as an introduction to the evolving area of AI, this course emphasizes potential business applications and related managerial insights. Artificial Intelligence (AI) is the science behind systems that can program themselves to classify, predict, and offer solutions based on structured and unstructured data. For mil.
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Optimizing Foundation Models (Simplified Chinese)

优化基础模型 (简体中文) 在本课程中,您将探索两种可提高基础模型 (FM) 性能的技术:检索增强生成 (RAG) 和微调。您将了解有助于使用向量数据库存储嵌入的 Amazon Web Services (AWS) 服务、代理在多步骤任务中的作用、定义微调 FM 的方法以及如何准备用于微调的数据等等。 课程级别:基础级 时长:1 小时 本课程包含互动元素、文字说明、配文图表和知识考核。 在本课.
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AWS ML Engineer Associate 2.1 Choose a Modeling Approach

AWS ML Engineer Associate 2.1 Choose a Modeling Approach Explore the AWS ML stack layers and learn how to solve common business challenges with AWS services. This course explores how to use Amazon SageMaker for machine learning tasks and how to review strategies for selecting appropriate models. Additionally, this course highlig.
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Fundamentals of Machine Learning and Artificial Intelligence

Fundamentals of Machine Learning and Artificial Intelligence In this course, you will learn about the foundations of machine learning (ML) and artificial intelligence (AI). You will explore the connections between AI, ML, deep learning, and the emerging field of generative artificial intelligence (generative AI). You will gain a solid understanding.
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Exploring Artificial Intelligence Use Cases and Applications

Exploring Artificial Intelligence Use Cases and Applications In this course, you will explore real-world artificial intelligence (AI), machine learning (ML), and generative artificial intelligence (generative AI) use cases across a range of industries. These areas include healthcare, finance, marketing, entertainment, and more. You will also learn.
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Responsible Artificial Intelligence Practices

Responsible Artificial Intelligence Practices In this course, you will learn about responsible AI practices. First, you will be introduced to what responsible AI is. You will learn how to define responsible AI, understand the challenges that responsible AI attempts to overcome, and explore the core dimensions of responsible AI. Then, you will dive.
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More and more products are now being developed using artificial intelligence. To avoid being left on the sidelines of progress, managers must understand how the robot’s “brains” work

Artificial intelligence (AI) and machine learning technologies have been used for many years, but now the intensity of their use has increased significantly. For example, machine learning is being actively implemented in telecommunications, retail, marketing and e-commerce. But many still do not fully understand what it is.

Machine learning involves the system processing a large number of examples, during which it identifies patterns and uses them to predict the characteristics of new data. In other words, this is the process of giving AI ml courses “consciousness”, the ability to remember and analyze.

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:

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:

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

  2. Average knowledge of statistics and probability.

  3. 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.

  4. Understanding Calculus.

  5. 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!