Python中完整的线性和逻辑回归课程

套索和岭回归、弹性网回归、线性回归、逻辑回归、pickle、tempfile。你对机器学习、深度学习、人工智能感兴趣吗？那么这课程就是为你准备的！The Complete Linear and Logistic Regression Course in Python

一个软件工程师设计了这个课程。凭借我这些年来获得的经验和知识，我可以分享我的知识，帮助你学习复杂的理论、算法和代码库。我将带你走进线性和逻辑回归的世界。这些是机器学习、深度学习和人工智能中的基本概念。理解了这些基本概念，就更容易理解机器学习、深度学习、人工智能中更复杂的概念。没有涵盖线性和逻辑回归的课程。然而，线性和逻辑回归技术在许多应用中使用。因此，学习和理解线性回归和逻辑回归是非常必要的。通过每一个教程，你将发展新的技能，并提高你对这个充满挑战但利润丰厚的数据科学子领域的理解。

这门课程既有趣又令人兴奋，但同时，我们也深入研究线性回归和逻辑回归。在这门课程的全新版本中，我们涵盖了大量的工具和技术，包括:Google ColabScikit-learn logical Regression。线性回归。SeabornLasso和Ridge RegressionKeras。pandas . tensor flow . tensorboardmatplotlib . elastic Net regression从UCI知识库导入数据。多元线性回归。TensorFlow Keras此外，该课程还包含基于现实生活实例的实践练习。因此，你不仅会学到理论，还会得到一些构建模型的实践机会。这门课有几个大项目。这些项目列举如下:糖尿病项目。乳腺癌项目。住房项目。MNIST项目。课程结束时，你将对线性和逻辑回归有深入的了解，通过了解线性和逻辑回归，你将获得更高的升职或工作机会。

由Hoang Quy La创作
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子教学|语言:英语|时长:27节课(4小时46分钟)|

你会学到什么
张量流
张量板
熊猫
ReLU激活功能。
海生的
Google Colab
从UCI存储库中导入数据。
sci kit-学习
逻辑回归。
线性回归。
numpy
泡菜
临时文件
套索和岭回归
弹性净回归
多元和多变量线性回归
TensorFlow Keras API

要求
需要Python的基础知识。

这门课程是给谁的
任何对机器学习感兴趣的人。
至少具备高中数学知识，并且希望开始学习机器学习、深度学习和人工智能的学生
任何对编码不太熟悉，但对机器学习、深度学习、人工智能感兴趣并希望将其轻松应用于数据集的人。
任何想在数据科学领域开始职业生涯的大学生
任何希望通过使用强大的机器学习、人工智能和深度学习工具为其业务创造附加值的人。任何想在车企做数据科学家、机器学习、深度学习、人工智能工程师的人。

Lasso and Ridge Regression, Elastic Net Regression, Linear Regression, Logistic Regression, pickle, tempfile.

What you’ll learn
Tensorflow
Tensorboard
pandas
ReLU activation function.
Seaborn
Google Colab
Import data from the UCI repository.
scikit-learn
Logistic Regression.
Linear Regression.
numpy
pickle
tempfile
Lasso and Ridge Regression
Elastic Net Regression
Multiple and multivariate linear regression
TensorFlow Keras API

Requirements
Basic knowledge of Python is required.

Description
Are you interested in Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!A software engineer has designed this course. With the experience and knowledge I gained throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries.I will walk you into the world of Linear and Logistic Regression. These are fundamental concepts in machine learning, deep learning, and artificial intelligence. Understanding these basic concepts makes it easier to understand more complex concepts in machine learning, deep learning, and artificial intelligence. There are no courses out there that cover Linear and Logistic Regression. However, Linear and Logistic Regression techniques are used in many applications. So it is essential to learn and understand Linear and Logistic Regression. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Linear and Logistic Regression. Throughout the brand new version of the course, we cover tons of tools and technologies, including:Google ColabScikit-learnLogistic Regression.Linear Regression.SeabornLasso and Ridge RegressionKeras.Pandas.TensorFlow.TensorBoardMatplotlib.Elastic Net RegressionImport data from the UCI repository.Multiple and multivariate linear regression.TensorFlow Keras APIMoreover, the course is packed with practical exercises based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your models. There are several big projects in this course. These projects are listed below:Diabetes project.Breast Cancer Project.Housing project.MNIST Project.By the end of the course, you will have a deep understanding of Linear and Logistic Regression, and you will get a higher chance of getting promoted or a job by knowing Linear and Logistic Regression.

Who this course is for
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science
Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.