Master "machine learning", which may be more than eating watermelon efficient way to book

Original link: http://dmoj.greedyai.com/api/wxlogin/callback?state=longmen\x26amp;fromroad=20191010qitianxiaomage

I believe many of my friends on machine learning algorithms have to understand, there are attempts to learn and use machine learning algorithms and tools to do some AI product! But just stay in the "switching" stage. I want to go in-depth understanding of the core substance of some of the algorithms was XGBoost | GBDT algorithms discouraging!

To help you a solid foundation and skilled application of machine learning algorithms, heavy recommend an interactive machine learning practical courses,  covering 16 large machine learning algorithms, 20+ case to explain, nine major projects operate.

Starting today, limited to 100 experience places

5 steps pedagogy course, the real grasp the core of each algorithm, each algorithm will begin to explain the principle, step by step algorithm to derive, eventually combined with high-end applications in the enterprise of the algorithm to actual practice.

01

Congress classical algorithm

|  K-NN nearest neighbor | linear regression | logistic regression |

| Convex optimization | Naive Bayes | SVM |

| Tree | random forest | GBDT | 

| XGBoost | matrix factorization | K-Means | 

| GMM | topic model | EM  | clustering | PCA | 

02

The practical operation of the project work

No project practice hand, look here! The curriculum involved in the project, are each algorithm for high-end applications to expand. Meanwhile enhancement algorithms available, the results of the project can be directly applied work.

03

20个项目讲解

回归分析身高预测

利用KNN筛选简历

二手车价格预估

量化投资之股票价格预测

预测广告点击率

利用L1正则模拟神经科学中的稀疏性

垃圾邮件分类

员工离职率预测

基于随机森林的疾病分析

利用GBDT解决搜索中的排序问题

人脸识别

基于聚类的消费群分类

内容推荐算法的电影推荐引擎

基于协同过滤算法的音乐推荐引擎

搭建OCR识别引擎

利用聚类算法压缩图片

基于主题模型和SVM的文本分类

问答系统搭建

利用kemel SVM识别医疗图片

利用聚类算法压缩图片

从零搭建方向传播算法

04

学习收获

收获成为出众算法工程师的四大技能

• 知识：理解算法背后的原理以及算法之间的内在关联。

• 实战：学会如何把学到的原理应用在实际的工作当中。

• 逻辑：培养举一反三能力，解决问题能力，并提升逻辑思考能力。

• 业务：广泛接触行业中的经典的案例，加深对业务的理解。

05

适合人群

• 互联网从业者：想了解机器学习并在日常工作中加以应用。

• IT从业者：希望入门机器学习，并且能够把技术应用到自身的AI场景。

• 在校学生：想深入理解机器学习技术、或者之后想从事AI相关的岗位。

• AI从业者：很喜欢机器学习，也有一定的经验，希望根据业务场景能够在模型上做一些创新、以及有能力自己求解出来；

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今日开课，限100个体验名额

试学仅需8.8元

试学4天时间，四章内容包含

KNN、 KD树、交叉验证、特征编码、

特征缩放、复杂度分析、降维、

图像识别项目、二手车价格预测项目等

点击下方阅读原文 或 扫描二维码

加入《机器学习集训营》

下面，我来给各位朋友介绍一下这款重磅级产品吧，保证让大家有惊喜。

06

全新学习体验

A. 全程手推公式，确保你深入理解算法的核心。

B. 项目源码均有清晰明了的代码注释，看代码都能上瘾！

C. 对话式学习指导，实时检测所学所得。

D. 不需要安装任何环境，完全依赖于云端编程。

E. 项目作业代码审核，老师精心批改，错误纰漏一目了然。

F. 质量极致的视频讲解

视频内容通俗易懂而且保证简洁性，力求做到极致。保证用最低的时间成本学会核心知识点！下面截取了KNN算法相关的两个短视频（全部课程拥有几百个视频）

样例1：KNN算法中K值对预测的影响 

样例2： 构造KD树 (1)

G.  更多彩蛋等你发现 ... 

......

07

课程大纲

课程内容由李文哲博士（美国南加州大学AI博士）亲自操刀设计，目前市面上最火爆的NLP高阶训练营也是来自于李文哲博士。课程内容上涵盖了几乎所有主流的机器学习算法，由浅入深，非常通俗易懂。以下是课程大纲：

08

专业的教研团队

教研团队均是工业界和学术界顶尖的专家，具有丰富的教学经验和实战经验。确保内容的专业性和前瞻性。

09

超高的学员评价

10

答疑服务保障

1, each assistant teachers have master's experience, AI Artificial Intelligence tier domestic and foreign enterprises or higher. During the study, in addition to a professional to get answers, you can also gain powerful network of resources. Find a job within a push not matter!

2 , teaching assistant instructor + + class teacher supervision and counseling throughout the day learning, security of each student can get a satisfactory answer in time.

●●●

Starting today, limited to 100 Ge experience places

Try to learn only 8.8 Yuan

Try to learn four days, four chapters contain

KNN, KD tree, cross-validation, feature encoding,

Feature scaling, complexity analysis, dimension reduction,

Image recognition program, used car prices forecast projects

Click below to read the original or two-dimensional code scanning

Adding "Machine Learning training camp."