机器学习 A-Z (Machine Learning A-Z in Chinese)
What you'll learn
- 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
Requirements
- 高中数学知识即可
- Just some high school mathematics level.
Description
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 is fun 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, Polynomial Regression
- Part 3 - Classification: Logistic Regression, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest 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 model and algorithms 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-life examples. 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 both Python and R code templates which you can download and use on your own projects.
Who this course is for:
- 所有对机器学习感兴趣的人
- 任何有高中数学知识并且想开始学习机器学习的学生
- 任何有机器学习基本知识并想了解更多这个领域的人
- 任何不太了解编程但对机器学习感兴趣，并希望将机器学习应用在数据上的人
- 任何想进入数据科学领域的大学生
- 任何想提高机器学习技能的数据分析师
- 任何对目前工作不满意并想成为数据科学家的人
- 任何希望运用强大的机器学习工具扩大自己事业的人
- 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.
Instructors
- 4.5 Instructor Rating
- 326,919 Reviews
- 1,844,375 Students
- 45 Courses
Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.7 million students have subscribed to his courses.
- 4.8 Instructor Rating
- 2,084 Reviews
- 9,013 Students
- 1 Course
大家好，我是亦文。
我毕业于法国最富盛名的工程师学院Ecole Polytechnique, 拥有应用数学及统计专业的硕士学位。我在金融及数据科学领域有四年的工作经验，涉足过宏观交易、智能估价等领域，始终保持着对最顶尖新鲜科技的学习热情。
我对机器学习、人工智能这些领域有着执着甚至狂热的信念。在业余时间，我也是一个围棋爱好者。AlphaGo的横空出世让我们认识到知识与科技的强大，以及人脑的局限性。科技对于围棋这个古老游戏带来了前瞻性的冲击与变革；对于其他行业，这一天也不会遥远。
我充分地认识到，比起掌握一门顶尖技术更加重要的，是构建起一个完整而逻辑的知识架构。我将尽我所能，将我对科学的热情带给大家。我的课程既用深入浅出的方式解释宏观原理，又涉及实际的应用。期待您可以在我的课程中收获到知识、趣味和灵感！
Hello, my name is Yiwen.
I graduated from Ecole Polytechnique, France's top science school, with a master degree in applied maths and statistics. For 4 years, I've been working in the financial industry and data science, and I have covered a variety of topics such as macro trading and intelligent valuation, etc. Moreover, I always keep the deepest interest for the most cutting-edge technologies.
I am personally a strong believer of Machine Learning and Artificial Intelligence. Myself being a part-time Go Player, the rise of Google's AlphaGo undoubtedly demonstrated the power of these new technologies, as well as the limitation of human brain. Technologies changed the world's oldest game forever. For other playgrounds, rules will inevitably change in the near future as well.
Across all my working and academic experiences, I realised that in addition to mastering a cutting-edge skill, it's essential to build up a complete and structured knowledge architecture. My courses will cover both intuitions and real-life applications. I'm looking forward to sharing my knowledge and passion with you!
- 4.8 Instructor Rating
- 2,084 Reviews
- 9,013 Students
- 1 Course
大家好，我是小秦。
我毕业于巴黎中央理工学校，并且拥有欧洲最顶尖的金融数学硕士。
在多年的金融、数据科学领域的研究和工作中，我构造实现过各种数学和统计模型，并且用这些模型解决了很多实际问题，包括宏观经济分析，金融衍生品定价，等等。于我而言，这些模型既拥有美妙的构造，又是解决实际问题的强大工具。其中最引人入胜的部分是理解模型原理，并通过编写程序将它们应用在现实世界中。
我从不怀疑机器学习是未来发展的方向。近几年来，机器学习被广泛运用在各种领域，尤其是和数据及统计紧密相连的金融业。机器学习的最吸引人之处在于运用确定的原理解决各种复杂不确定的问题。我对此产生了浓厚的兴趣，工作之余持续地吸收新的知识，并且一直渴望将自己学习所得分享给他人。
我始终坚信，尖端的知识不应只存在于象牙塔上。任何人，只要掌握了正确地方法，都可以对其了解并且应用。我想尽自己所能，让更多的人了解机器学习以及其它科技前沿领域，让知识走向您。
Hi, I'm Qin.
I graduated from Ecole Centrale Paris in France, and I have the top financial mathematics master degree in Europe.
During my several years of research and working experience in financial and data science industry, I constructed a lot of different mathematical and statistical models, and I used these models to solve many practical problems, including macroeconomic analysis, pricing of financial derivatives, etc. For me, these models not only have beautiful constructions but also are powerful tools to solve practical problems. The most interesting part is to understand the principle behind these models and use them in real world by programming.
I never doubt that Machine Learning is the future. In recent years, Machine Learning is widely used in various fields, especially in financial industry which is highly linked to data science and statistics. The most exciting aspect of Machine Learning is that we use definite principle to solve uncertain and complex problems. I'm really passionate about this subject and keep learning new knowledge in my spare time. I'm eager to share what I've learnt with you.
I strongly believe that the knowledge on advanced technology shouldn't only stay in academic world. Anybody can learn and apply the knowledge as long as he finds the right way. I want to contribute my effort to make more people know Machine Learning and other advanced technology subjects, and make knowledge come to you.
- 4.5 Instructor Rating
- 682,685 Reviews
- 2,554,146 Students
- 152 Courses
Hi there,
We are the SuperDataScience team. You will hear from us when new SuperDataScience courses are released, when we publish new podcasts, blogs, share cheat sheets, and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
SuperDataScience Team!