Machine Learning courses

1334 Courses

人工智能基础核心技术之事件检测

欢迎参加由清华大学提供的“人工智能基础核心技术之事件检测”课程。在此课程中，您将学习如何运用人工智能中的事件检测技术来识别和分析数据流中的重大事件或模式。这是一门中文课程，旨在帮助您深入理解事件检测在人工智能中的应用。本课程涵盖多个关键主题，包括人工智能、机器学习、计算机视觉、神经网络、时间序列分析、信号处理及模式识别。这为您在这些领域的进一.

AI for Academic Search （人工智能助力学术搜索）

Unlock the potential of artificial intelligence in academic search and research discovery with this lecture from Tsinghua University. Delivered in Chinese, the session delves into how AI is revolutionizing the way scholars access and retrieve information. Attendees will learn about cutting-edge techniques for improving scholarly information re.

闲话AI：自然语言理解难在哪儿？

Udemy

Deep learning Preparation Course in Arabic Part 1 : Python

Embark on your machine learning journey with the Deep Learning Preparation Course in Arabic, Part 1: Python. Learn the essential skills needed to become a learning machine. This course is delivered by experts on Udemy, providing a comprehensive introduction to Python within the context of deep learning. Categories include: Python Courses.

Udemy

Machine Learning A-Z™: Hands-On Python & R in Data Science

Unlock the power of machine learning with the Machine Learning A-Z™ course, providing practical experience with Python and R. This Udemy course is designed for those looking to expand their knowledge in advanced data visualization, linear regression, decision trees, and essential libraries like NumPy and Pandas. Whether you're a budding data s.

Udemy

Python : Master Programming and Development with 15 Projects

Join our extensive Python course where you will delve into the complete Python programming landscape. This course offers a robust curriculum starting from the basics of Python and expanding into various fields such as Data Science, Data Visualization, Desktop Graphical Applications using Tkinter, and Machine Learning. With 15 hands-on project.

Udemy

Ensemble Machine Learning in Python: Random Forest, AdaBoost

Ensemble Machine Learning in Python: Random Forest, AdaBoost | Udemy Enhance your data science skills with our comprehensive course on ensemble machine learning in Python. Delve into powerful techniques like Random Forest and AdaBoost, as well as foundational ensemble methods such as Boosting, Bagging, and Bootstrap. This course is provided b.

Udemy

Machine Learning, Data Science and Generative AI with Python

Join our comprehensive, hands-on tutorial to delve into the realms of Machine Learning, Data Science, and Generative AI with Python. This course provides a deep dive into essential technologies and tools such as TensorFlow, GPT, OpenAI, and neural networks. Perfect for those seeking practical experience and aiming to advance their skills in m.

DataCamp

Reinforcement Learning from Human Feedback (RLHF)

Join the course on Reinforcement Learning from Human Feedback to effectively align Generative AI models with human values and preferences. Gain hands-on proficiency with advanced Large Language Models (LLMs), and delve into the complexities of designing reward models. Elevate your understanding of AI that not only learns from inputs bu.

DataCamp

Introduction to GPTs

Join our 'Introduction to GPTs' course and delve into the world of AI tools. Gain insights on how to responsibly and confidently utilize GPT tools across various applications. While OpenAI and ChatGPT are well-known, GPTs encompass a broad range of AI models developed for text-based tasks. Many organizations have harnessed GPT technologies to.

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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!