机器学习 A-Z (Machine Learning A-Z in Chinese)

• 完全掌握机器学习及在Python和R里的应用
• 深刻理解各种机器学习的模型
• 做出准确的预测和强大的分析
• 利用机器学习创造更多价值
• 利用机器学习解决私人问题
• 掌握并熟练处理强大的算法，例如强化学习，自然语言处理，还有深度学习
• 掌握并熟练处理先进的技术，例如对降低数据维度
• 了解对不同的问题怎样选择合适的机器学习模型
• 建立起强大的机器学习知识架构，并且知道如何创建和运用不同的模型来解决任何问题
• Master Machine Learning on Python & R
• Have a great intuition of many Machine Learning models
• Make accurate predictions and 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

想了解机器学习？这门课程为您订做！

这门课程是英文课程Machine Learning A-Z的翻译和再创造。原版英文课程是Udemy上最畅销的机器学习课程。您在这门课里，会用深入浅出的方法学会复杂的模型，算法，还有基础的编程语句。

我们会手把手地教会您机器学习。每一节课都会让您获得新的知识，完备机器学习的知识架构，在享受机器学习的同时对这个领域有更深的理解。

这门课程十分有趣，包含了机器学习的方方面面。课程结构如下：

• 第一部分 -数据预处理
• 第二部分-回归：简单线性回归，多元线性回归，多项式回归
• 第三部分-分类：逻辑回归，支持向量机（SVM），核函数与支持向量机（Kernel SVM），朴素贝叶斯，决策树分类，随机森林分类
• 第四部分-聚类：K-平均聚类分析
• 第五部分-关联规则学习：先验算法
• 第六部分 (待更新)-强化学习：置信区间上界算法（UCB），Thompson抽样算法
• 第七部分(待更新)-自然语言处理：自然语言处理算法
• 第八部分(待更新)-深度学习：人工神经网络，卷积神经网络
• 第九部分(待更新)-降维（Dimensionality Reduction）：主成分分析 （PCA），核函数主成分分析（Kernel PCA）
• 第十部分(待更新)-模型选择：模型选择，极端梯度上升

对于每个模型，除了学会理论基础之外，您还会学习如何将这些模型运用到各种实际生活的案例里，并且课程也包括Python和R的代码模板，您可以下载并且直接将代码运用到您自己的项目里。

Interested in the field of Machine Learning?Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory,algorithms and coding libraries in a simple way.

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 isfun and exciting, but 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,PolynomialRegression
• Part 3 - Classification: Logistic Regression,SVM, Kernel SVM, Naive Bayes, Decision Tree Classification,RandomForest Classification
• Part 4 - Clustering: K-Means
• Part 5 - Association Rule Learning: Apriori
• Part 6 - Reinforcement Learning:Upper Confidence Bound,Thompson Sampling
• Part 7 - Natural Language Processing: Bag-of-words modelandalgorithms for NLP
• Part 8 - Deep Learning: Artificial Neural Networks,Convolutional Neural Networks
• Part 9 - Dimensionality Reduction: PCA, Kernel PCA
• Part 10 - Model Selection & Boosting: k-fold Cross Validation, Grid Search.

Moreover, the course is packed with practical exercises which are based on real-lifeexamples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes bothPython and Rcode templates which you can download and use on your own projects.