Python实用机器学习:从头开始构建25个项目

Practical Machine Learning: Build 25 Projects From Scratch

实用机器学习课程:学习用Python构建机器学习、数据科学项目和案例研究
数据科学-机器学习-项目-课程

你会学到:
构建性能最佳的机器学习模型
对许多数据科学模型有很强的直觉
了解如何构建数据科学模型
了解如何使用Scikit-学习应用强大的机器学习算法。
制作健壮的数据科学模型
如何改进机器学习模型
创建有监督的机器学习算法来预测类。
了解真实数据集的最佳实践。

时长:16h 2m |视频:. MP4，1280×720 30 fps |音频:AAC，44.1 kHz，2ch |大小解压后:8.96 GB 含课程文件
语言：英语+中英文字幕（云桥网络 机译）

描述:
基本上，机器学习过程包括以下几个阶段:
为机器学习算法提供输入数据的示例和该输入的一系列预期标签。
输入数据被转换成文本向量，即代表不同数据特征的数字数组。
算法学习基于手动标记的样本将特征向量与标记相关联，并在处理未看到的数据时自动进行预测。
虽然人工智能和机器学习经常互换使用，但它们是两个不同的概念。人工智能是一个更广泛的概念——机器做出决策，学习新技能，并以类似于人类的方式解决问题——而机器学习是人工智能的一个子集，它使智能系统能够从数据中自主学习新事物。
机器学习(ML)是人工智能(AI)的一个分支，它使计算机能够在没有明确编程的情况下随着时间的推移进行自我学习和改进。简而言之，机器学习算法能够检测和学习数据中的模式，并做出自己的预测。
在传统编程中，有人编写一系列指令，以便计算机能够将输入数据转换成所需的输出。指令大多基于IF-THEN结构:当满足某些条件时，程序执行特定的动作。
另一方面，机器学习是一个自动化的过程，它使机器能够根据过去的观察来解决问题并采取行动。

这门课是给谁上的:
数据科学初学者
data-science-machine-learning-projects-course
Duration: 16h 2m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 8.81 GB
Genre: eLearning | Language: English

Practical Machine Learning Course: Learn To Build Machine Learning, Data Science Projects & Case Studies With Python

What you’ll learn:
Build Best Performing Machine Learning Models
Have a great intuition of many data science models
Learn how to build data science models
Learn how to use Scikit-learn to apply powerful machine learning algorithms.
Make robust data science models
How to improve your Machine Learning Models
Create supervised machine learning algorithms to predict classes.
Learn best practices for real-world data sets.

Description:
Basically, the machine learning process includes these stages:
Feed a machine learning algorithm examples of input data and a series of expected tags for that input.
The input data is transformed into text vectors, an array of numbers that represent different data features.
Algorithms learn to associate feature vectors with tags based on manually tagged samples, and automatically makes predictions when processing unseen data.
While artificial intelligence and machine learning are often used interchangeably, they are two different concepts. AI is the broader concept – machines making decisions, learning new skills, and solving problems in a similar way to humans – whereas machine learning is a subset of AI that enables intelligent systems to autonomously learn new things from data.
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions.
In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action.
Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.

Who this course is for:
Beginners in data science
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