机器学习A-Z:人工智能、Python和MLOps

通过包含统计等基本主题的综合课程学习数据科学。对机器学习领域感兴趣？那么这道菜就是为你准备的！本课程由数据科学家和机器学习专家设计，以便我们可以分享我们的知识，并帮助您以简单的方式学习复杂的理论、算法和代码库。全世界有超过900，000名学生信任这门课程。我们将带你一步步进入机器学习的世界。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。本课程可以通过学习Python教程或R教程或两者来完成。选择你职业生涯中需要的编程语言。Machine Learning A-Z: AI, Python and MLOps

这门课程既有趣又令人兴奋，同时，我们还深入研究了机器学习。其结构如下:第1部分-数据预处理第2部分-回归:简单线性回归、多元线性回归、多项式回归、SVR、决策树回归、随机森林回归第3部分-分类:逻辑回归、K-NN、SVM、核SVM、朴素贝叶斯、决策树分类、随机森林分类第4部分-聚类:K-均值、分层聚类第5部分-关联规则学习:Apriori、EclatPart 6 -强化学习:置信上限、 Thompson SamplingPart 7 -自然语言处理:NLPPart 8的词袋模型和算法-深度学习:人工神经网络，卷积神经网络Part 9 -降维:PCA，LDA，内核PCAPart 10 -模型选择和； Boosting: k-fold交叉验证，参数调优，网格搜索，每个部分内部的XGBoostEach节都是独立的。因此，你可以从头到尾学习整个课程，也可以直接跳到任何特定的部分，了解你现在的职业生涯需要什么。此外，本课程还包含基于真实案例研究的实践练习。因此，你不仅会学到理论，还会获得大量构建自己模型的实践机会。本课程包括Python和R代码模板，你可以下载并在自己的项目中使用。

由Akhil Vydyula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:17节课(7小时44分钟)|

你会学到什么
知道为每种类型的问题选择哪种机器学习模型
进行有力的分析
对许多机器学习模型有很好的直觉
Python上的机器学习大师& R

要求
只是一些高中数学水平。

这门课程是给谁的
任何对机器学习感兴趣的人。
任何对编码不太熟悉，但对机器学习感兴趣并希望将其轻松应用于数据集的人。
任何中级水平的人，他们知道机器学习的基础知识，包括线性回归或逻辑回归等经典算法，但希望了解更多，并探索机器学习的所有不同领域。
任何对自己的工作不满意，想成为数据科学家的人。
至少拥有高中数学知识并希望开始学习机器学习的学生。
任何对编码不太熟悉，但对机器学习感兴趣并希望将其轻松应用于数据集的人。

Learn Data Science through a comprehensive course curriculum encompassing essential topics like statistics etc.

What you’ll learn
Know which Machine Learning model to choose for each type of problem
Make powerful analysis
Have a great intuition of many Machine Learning models
Master Machine Learning on Python & R

Requirements
Just some high school mathematics level.

Description
Interested in the field of Machine Learning? Then this course is for you!This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.Over 900,000 students world-wide trust this course.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:Part 1 – Data PreprocessingPart 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 – Clustering: K-Means, Hierarchical ClusteringPart 5 – Association Rule Learning: Apriori, EclatPart 6 – Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 – Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 – Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoostEach section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.this course includes both Python and R code templates which you can download and use on your own projects.

Who this course is for
Anyone interested in Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.