如何学习机器学习？路线图。 [英] How to learn machine learning? Road map.

问题描述

您好，



我是具有3年以上经验的C ++开发人员。

现在想学习机器学习。



有人可以帮我解决路线图，比如我应该从哪里开始？



如果有在线/离线课程在印度有售。



非常感谢您的回答。



Vishal Bhatia


我尝试过：



正在寻找类似网站上的课程Udemy，udacity，Coursera。

Hello,

I am C++ Developer with 3+ years of experience.
Now thinking to learn machine learning.

Can someone please help me with the road map, like where should i start from?

And if any online /offline courses available in India.

Your answers are really appreciated.

Vishal Bhatia

What I have tried:

Was looking out for courses available on sites like Udemy, udacity , Coursera.

推荐答案

你好。一旦你提出这个问题，我就在这里，非常乐意帮助你。

通常，人工智能和机器学习的大学课程，在大多数情况下，包括诸如案例之类的主题研究：



单元＃1：数据挖掘算法：



1. K-Means聚类算法;

2.相似性和可能性评估算法（欧几里德距离，皮尔森

相关性，概率......）;

3.贝叶斯理论（贝叶斯分类器算法+贝叶斯预测网络）;

4.奇异值分解算法（经典SVD和SVD ++）;

5.各种其他算法，如滚动平均计算算法;



单元＃2：AI机器学习算法：



1.人工神经网络（人工神经网络：

1.1。前馈和反向传播学习程序;

1.2。前向传播神经网络;



2.遗传算法和进化算法：

2.1。遗传进化搜索算法;

2.2。经典遗传算法要点：

2.2.1。找一个函数的极值;

2.2.2。丢番图方程求解;

2.2.3。数值逼近;



单元＃3：



1.各种AI和机器学习应用特定材料，如

算法交易，电子商务工具和组件，社交媒体等。



这里有一些AI和机器学习的大指南您可以查看：



1.人工智能现代Aporoach，由Manning的Google研究中心信息检索负责人撰写，以及大数据分析;

2. Tyrtyshnikov的线性代数;



这是我自己的人工智能和机器学习出版物清单：



羽毛之鸟粘在一起在JavaScript中生成用户到项目的建议 [ ^ ]



使用Node.JS，JavaScript和Ajax请求的Naїve贝叶斯反垃圾邮件过滤器[ ^ ]



C＃.NET：实现SVD ++ AI数据挖掘算法根据评级预测生成建议 [ ^ ]



C＃.NET实现K-Means聚类算法以生成建议 [ ^ ]



实施K-Means图像分割算法 [ ^ ]



实施AI用于求解数值逼近问题的进化二元分布算法 [ ^ ]



此外，搜索CodeProject的网站并查找由 Mahsa Hassankashi发布的文章：

Mahsa Hassankashi - 专业档案 [ ^ ]



这就是全部。祝你好运：）

Hello. Once you've given this question, I'm here and really glad to help you out.
Normally, the university course of AI and machine learning, in the most cases, includes such topics as a case study:

Unit #1: Data mining algorithms:

1. K-Means clustering algorithm;
2. Similarity and likelihood assessment algorithms (Euclidean distance, Pearson
correlation, probabilistic,...);
3. Bayesian Theory (Bayesian Classifier Algorithm + Bayesian Prediction Networks);
4. Singular Values Decomposition Algorithm (classical SVD and SVD++);
5. Various of other algorithms such as a rolling-average computation algorithm;

Unit #2: AI Machine Learning algorithms:

1. Artificial Neural Networks (ANNs):
1.1. Feed-Forward and Back-propagation learning procedures;
1.2. Forward-propagation neural networks;

2. Genetic and Evolutionary Algorithms:
2.1. Genetic evolutionary search algorithm;
2.2. Classical Genetic algorithms essentials:
2.2.1. Find a function's extrema;
2.2.2. Diophantine equations solving;
2.2.3. Numerical Approximation;

Unit #3:

1. Various AI and machine learning applied-specific materials such as
Algorithmic trading, e-commerce tools and components, social media, etc.

Here're a couple of AI and machine learning large guidelines you can check with:

1. Artificial Intelligence Modern Aporoach, written by head of Google Research Center Information Retrieval by Manning, and Big data analysis;
2. Linear Algebra by Tyrtyshnikov;

Here's a list of my own AI and machine learning publications:

"Birds of a Feather stick together" To Produce Users-To-Items Recommendations In JavaScript[^]

Naїve Bayesian Anti-Spam Filter Using Node.JS, JavaScript And Ajax Requests[^]

C#.NET: Implementing SVD++ AI Data Mining Algorithm To Produce Recommendations Based On Ratings Prediction[^]

C#.NET Implementation of K-Means Clustering Algorithm to Produce Recommendations[^]

Implementing K-Means Image Segmentation Algorithm[^]

Implementing AI Evolutionary binary distribution algorithm for solving the numeric approximation problem[^]

Also, do a search to CodeProject's website and find articles published by Mahsa Hassankashi:
Mahsa Hassankashi - Professional Profile[^]

That's all. Good Luck :)

明显的起点是 www.google.com [ ^ ]以及其他致力于您感兴趣的主题的网站。您也可以尝试 CodeProject文章部分 [ ^ ]。

The obvious places to start are www.google.com[^] and other websites dedicated to the subject you are interested in. You could also try the CodeProject Articles section[^].

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