机器学习课程 Machine Learning A-Z: AI, Python & R + ChatGPT Bonus 2023

向两位数据科学专家学习用Python和R创建机器学习算法。包括代码模板。对机器学习领域感兴趣？那么这课程就是为你准备的！
这个课程是由
数据科学家和机器学习专家
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。
超过90万名学生
全世界都信任这个课程。
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。
本课程可通过以下任一方式完成
Python教程，或者R教程，
或者两者兼而有之——Python & r .选择你职业生涯需要的编程语言。
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。它的结构如下
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，支持向量回归机，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，层次聚类
第5部分-关联规则学习:Apriori，Eclat
第6部分-强化学习:置信上限，汤普森抽样
第7部分-自然语言处理:自然语言处理的词袋模型和算法
第8部分-深度学习:人工神经网络，卷积神经网络
第9部分-降维:主成分分析，线性判别分析，核主成分分析
第10部分——模型选择和提升:k重交叉验证、参数调整、网格搜索、XGBoost
每个部分中的每个部分都是独立的。所以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
现在就了解你的职业需要什么
。
此外，本课程包含基于以下内容的实践练习
真实案例研究
。所以你不仅会学到理论，还会学到很多
动手实践
建立你自己的模型。
作为奖励，这门课
包括Python和R代码模板
您可以下载并在自己的项目中使用。
这门课程是给谁的
任何对机器学习感兴趣的人。
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。
任何想在数据科学领域开始职业生涯的大学生。
任何想要提升机器学习水平的数据分析师。
任何对自己的工作不满意，想成为数据科学家的人。
任何希望通过使用强大的机器学习工具为其业务创造附加值的人。

你会学到什么
Python上的机器学习大师& R
对许多机器学习模型有很好的直觉
做出准确的预测
进行有力的分析
制作强大的机器学习模型
为您的企业创造强大的附加值
将机器学习用于个人目的
处理特定主题，如强化学习、NLP和深度学习
处理降维等高级技术
知道为每种类型的问题选择哪种机器学习模型
建立一个强大的机器学习模型军队，并知道如何组合它们来解决任何问题

课程时长:42小时 35分钟 |视频:. MP4，1280×720 30 fps |音频:AAC，48 kHz，2ch |大小:10.4 GB
类型:电子学习|语言:英语+.srt英文字幕

要求
只是一些高中数学水平。

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

What you’ll learn
Master Machine Learning on Python & R
Have a great intuition of many Machine Learning models
Make accurate predictions
Make powerful analysis
Make robust Machine Learning models
Create strong added value to your business
Use Machine Learning for personal purpose
Handle specific topics like Reinforcement Learning, NLP and Deep Learning
Handle advanced techniques like Dimensionality Reduction
Know which Machine Learning model to choose for each type of problem
Build an army of powerful Machine Learning models and know how to combine them to solve any problem

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 Preprocessing
Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 – Clustering: K-Means, Hierarchical Clustering
Part 5 – Association Rule Learning: Apriori, Eclat
Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Each 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.
And as a bonus, 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.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
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 that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Machine Learning tools.