My route on python and MachineLearning

最新推荐文章于 2025-01-16 09:39:14 发布

IT_Shero

最新推荐文章于 2025-01-16 09:39:14 发布

阅读量425

点赞数

本文链接：https://blog.youkuaiyun.com/UI_Shero/article/details/78818064

版权

本文详细介绍Python机器学习环境的搭建过程，包括Python IDE的选择与配置（如Anaconda和PyCharm），TensorFlow的入门资源及官方文档推荐，matplotlib和numpy等科学计算库的学习资料，并提供了上手练习代码和Git教程。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

1. python IDE 搭建 anaconda | pyCharm，python 3.6

2. tensorflow 中文手册w3cSchool、tensorflow中文论坛

3. matplotlib 哔哩哔哩 python数据分析-matplotlib:https://www.bilibili.com/video/av6989413/?from=search&seid=17894542786230270665

matplotlib 使用手册官网：http://matplotlib.org/examples/index.html

4. numpy 手册

5. 上手代码，练习：GitHub 搜“tensorflow-mnist-tutorial”边看边学

6. git教程：廖雪峰，书：大塚弘记《GitHub入门与实践》

确定要放弃本次机会？

福利倒计时

: :

立减 ¥

普通VIP年卡可用

立即使用

IT_Shero

关注关注

0
点赞
踩
0

收藏

觉得还不错? 一键收藏
0
评论
分享

复制链接

分享到 QQ

分享到新浪微博

扫一扫
举报

举报

Python面试宝典：云服务概览以及Python在云计算服务中的应用相关面试笔试题（1000加面试笔试题助你轻松捕获大厂Offer）

今晚打老虎的专栏

06-20

243

python在云计算服务中的应用相关面试笔试题

使用Flask进行机器学习模型部署

weixin_26752765的博客

09-13

2355

In this article, we are going to discuss majorly machine learning model deployment using flask API but the code for model building and deployment using flask API will be available in my GitHub reposit...

参与评论您还未登录，请先登录后发表或查看评论

Deep Learning for Computer Vision with Python.

CVAIDL的博客

03-27

6183

Welcome back! This is the fourth post in the deep learning development environment configuration series which accompany my new book,Deep Learning for Computer Vision with Python. Today, we will conf...

[C2P1] Andrew Ng - Machine Learning

weixin_30764771的博客

12-05

4324

About this Course Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech...

[C5] Andrew Ng - Structuring Machine Learning Projects

weixin_30856725的博客

04-14

1293

About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will ...

Python开发从入门到精通(上) - 基础编程

YunWisdom

01-16

1405

本书致力于让读者通过阅读、学习及实践成为Python大师。我们从“Hello, World！”开始，一步一步走向精通Python的道路。在这趟旅程中，你将已经不再是新手，而将成为拥有Python的核心技能的大师，这趟通往未来的旅程只是刚刚开始。我们通过不断进步与探索，去探索未知和科学。记住Python是一个强大且灵活的工具，读者朋友们请永远保持好奇心，继续探索数据科学、人工智能、Web开发等领域，让Python陪伴你走得更远！

python 数据科学书籍_您必须在2020年阅读的数据科学书籍

weixin_26752765的博客

09-04

418

python 数据科学书籍“We’re entering a new world in which data may be more important than software.” — - Tim O’Reilly “我们正在进入一个新世界，在这个世界中，数据可能比软件更重要。” --蒂姆·奥雷利 The Data Science industry is seeing a rapid incr...

40 Interview Questions asked at Startups in Machine Learning / Data Science

12-19

754

Introduction Careful! These question can make you think THRICE! Machine learning and data science are being looked as the drivers of the next industrial revolution happening in the world tod

《Mastering Python Code Writing: A Comprehensive Guide》：此文为AI自动生成

zheng_ruiguo的专栏

12-17

755

Mastering Python Code Writing: A Comprehensive Guide

CSCI 2600 Generics and Least-Cost Paths

re5damancy的博客

08-09

980

be graded.nodes.weights.continueThere is5share.graph....

开始使用python编程所需的一切

weixin_26711425的博客

09-08

647

Are you ready to take the leap into programming in Python? For a beginner, it can be scary to jump into the water but trust me, all you need is a pair of swimming pants and you are good to go. And by ...

The Application of OpenCV and Python Versions in Cloud Computing: Version Selection and Scalability,...

OpenCV (Open Source Computer Vision Library) is an open-source library of algorithms and functions for image processing, computer vision, and machine learning tasks. It is closely integrated with the ...

MCIT Online 553 Project 1 Documentation Link State and Distance Vector RoutingC/C++

yhinok666的博客

06-18

746

•$ ./waf“./waf–j 4”LIBRARY10 90 5 51 8 52 5 03 2 54 2 05 6 86 5 87 8 08 8 29 4 80 1 19730 2 42530 3 53410 5 40660 6 27020 9 43931 8 10332 7 105893 4 1126IP0, 1,IP0,2 , andIPIP⟨⟩⟨⟩⟨Command⟩⟨Arguments⟩⟨⟩⟨。

嵌入式系统开发_STM32微控制器_ESP8266WiFi模块_心率传感器_加速度计_OLED显示屏_蓝牙40_低功耗设计_实时操作系统_智能手表_多功能健康监测_运动数据记录_.zip

04-30

嵌入式系统开发_STM32微控制器_ESP8266WiFi模块_心率传感器_加速度计_OLED显示屏_蓝牙40_低功耗设计_实时操作系统_智能手表_多功能健康监测_运动数据记录_

驾校自动化_网页自动化爬虫技术_Python27多线程HTTP请求模拟_龙泉驾校2014版约车系统自动预约助手_通过模拟登录和循环请求实现自动约车功能_支持失败自动递增车号重试_.zip

04-30

驾校自动化_网页自动化爬虫技术_Python27多线程HTTP请求模拟_龙泉驾校2014版约车系统自动预约助手_通过模拟登录和循环请求实现自动约车功能_支持失败自动递增车号重试_

Linux系统编程_操作系统内核_系统调用_进程线程_信号处理_文件IO_进程间通信_多线程同步_网络编程_UNIX环境编程_中文翻译勘误_错误修正_代码示例优化_技术文档校对_开.zip

04-30

Linux系统编程_操作系统内核_系统调用_进程线程_信号处理_文件IO_进程间通信_多线程同步_网络编程_UNIX环境编程_中文翻译勘误_错误修正_代码示例优化_技术文档校对_开

wanjunshe_Python-Tensorflow_12888_1745868924470.zip

04-30

wanjunshe_Python-Tensorflow_12888_1745868924470

scratch少儿编程逻辑思维游戏源码-铅笔画.zip

最新发布

04-30

scratch少儿编程逻辑思维游戏源码-铅笔画.zip

即时通讯应用开发_基于LeanCloud云服务与Android原生开发_集成QQ第三方登录与即时聊天功能的社交应用_实现用户注册登录创建聊天室发送文字消息展示用户信息头像昵称并提供.zip

04-30

即时通讯应用开发_基于LeanCloud云服务与Android原生开发_集成QQ第三方登录与即时聊天功能的社交应用_实现用户注册登录创建聊天室发送文字消息展示用户信息头像昵称并提供

Machine Learning specific learning route

04-04

1. Mathematics: Understanding the mathematical concepts like probability, calculus, statistics, linear algebra, optimization, and multivariate calculus is essential for machine learning. 2. Programming: Learning programming languages like Python, R, and Java is necessary for machine learning. 3. Data Structures and Algorithms: Understanding data structures and algorithms is important for efficient data processing and analysis. 4. Data Preprocessing: Learning data preprocessing techniques like data cleaning, data transformation, and data integration is important for preparing data for analysis. 5. Supervised Learning: Understanding supervised learning algorithms like linear regression, logistic regression, decision trees, and support vector machines is important. 6. Unsupervised Learning: Understanding unsupervised learning algorithms like clustering, principal component analysis, and association rule mining is important. 7. Deep Learning: Learning about deep learning algorithms like neural networks, convolutional neural networks, and recurrent neural networks is important. 8. Reinforcement Learning: Understanding reinforcement learning algorithms like Q-learning and policy gradient methods is important. 9. Model Evaluation and Selection: Learning about techniques for evaluating and selecting the best models for specific tasks is important. 10. Deployment: Learning about deploying machine learning models in production environments is important.