Learn Automated Machine Learning : Build Real World Projects

自动化机器学习提供了方法和流程，使机器学习可供非机器学习专家使用，以提高机器学习的效率并加速机器学习的研究。

机器学习 (ML) 近年来取得了相当大的成功，越来越多的学科依赖它。然而，这一成功关键依赖于人类机器学习专家来执行以下任务

预处理和清理数据。

选择并构建适当的特征。

选择合适的模型族。

优化模型超参数。

设计神经网络的拓扑结构（如果使用深度学习）。

后处理机器学习模型。

批判性地分析获得的结果。

由于这些任务的复杂性通常超出了非 ML 专家的范围，机器学习应用程序的快速增长催生了对无需专业知识即可轻松使用的现成机器学习方法的需求。我们将由此产生的研究领域称为机器学习的渐进式自动化 AutoML

您可以将机器学习视为人工智能技术的一个子集，因为它涉及训练机器以更快、更智能地学习。

人工智能技术专注于模仿人类智能，让计算机从经验中学习，而机器学习则专注于让它们从经验中学习更多、更快。

在某种程度上，机器学习就像人工智能技术的优化过程，机器学习工程师负责为人工智能解决方案提供更好、更快的培训。

机器学习过程的目标是使 AI 解决方案更快、更智能，以便他们能够为他们设定的任何任务提供更好的结果。

由于人工智能技术能够对社会和现代商业实践产生如此巨大的影响，彻底改变从规划到物流再到运营和生产的日常任务，机器学习专家的需求量非常大。

Instructors: Pianalytix .
16 sections • 117 lectures • 10h 14m
Video: MP4 1280×720 44 KHz | English + Sub
Updated 10/2022 | Size: 4.4 GB

Solve Data Science Problems Using Automated -ML, Learn To Use Eval ML, Pycaret, Auto Keras, Auto SK Learn, H20 Auto ML

What you’ll learn
Learn to perform Classification and Regression modelling
Implement Machine Learning algorithms
Master Machine Learning and use it on the job
Write clean, maintainable and performant code

Requirements
Knowledge Of Machine Learning

Description
Automated Machine Learning provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks

Preprocess and clean the data.

Select and construct appropriate features.

Select an appropriate model family.

Optimize model hyperparameters.

Design the topology of neural networks (if deep learning is used).

Postprocess machine learning models.

Critically analyze the results obtained.

As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML

You can think of machine learning as a subset of artificial intelligence technologies, as it involves training a machine to learn more quickly and more intelligently.

Where AI technology focuses on mimicking human intelligence, allowing computers to learn from experience, machine learning focuses on making them learn more, and faster, from that experience.

In a way, machine learning is like an optimization process for AI technologies, with the machine learning engineer being responsible for providing better, faster training to AI solutions.

The goal of the machine learning process is to make AI solutions faster and smarter so they can deliver even better results for whatever task they’ve been set to achieve.

Because AI technology is capable of having such a huge impact on society and modern business practices, revolutionizing everyday tasks from planning to logistics to operations and production, machine learning experts are in extremely high demand.

Who this course is for
Beginners in machine learning