[利用python进行机器学习(含字幕)].01.Practical.Machine.Learning.Tutorial.with.Python.Intro.p.1.mp4 64.97MB
[利用python进行机器学习(含字幕)].02.Regression.Intro.-.Practical.Machine.Learning.Tutorial.with.Python.p.2-JcI5Vnw0b2c.mp4 36.18MB
[利用python进行机器学习(含字幕)].03.Regression.Features.and.Labels.-.Practical.Machine.Learning.Tutorial.with.Python.p.3-lN5jesocJjk.mp4 33.31MB
[利用python进行机器学习(含字幕)].04.Regression.Training.and.Testing.-.Practical.Machine.Learning.Tutorial.with.Python.p.4-r4mwkS2T9aI.mp4 58.58MB
[利用python进行机器学习(含字幕)].05.Regression.forecasting.and.predicting.-.Practical.Machine.Learning.Tutorial.with.Python.p.5-QLVMqwpOLPk.mp4 51.79MB
[利用python进行机器学习(含字幕)].06.Pickling.and.Scaling.-.Practical.Machine.Learning.Tutorial.with.Python.p.6-za5s7RB_VLw.mp4 32.36MB
[利用python进行机器学习(含字幕)].07.Regression.How.it.Works.-.Practical.Machine.Learning.Tutorial.with.Python.p.7-V59bYfIomVk.mp4 10.12MB
[利用python进行机器学习(含字幕)].08.How.to.program.the.Best.Fit.Slope.-.Practical.Machine.Learning.Tutorial.with.Python.p.8-SvmueyhSkgQ.mp4 31.11MB
[利用python进行机器学习(含字幕)].09.How.to.program.the.Best.Fit.Line.-.Practical.Machine.Learning.Tutorial.with.Python.p.9-KLGfMGsgP34.mp4 23.89MB
[利用python进行机器学习(含字幕)].10.R.Squared.Theory.-.Practical.Machine.Learning.Tutorial.with.Python.p.10--fgYp74SNtk.mp4 11.38MB
[利用python进行机器学习(含字幕)].11.Programming.R.Squared.-.Practical.Machine.Learning.Tutorial.with.Python.p.11-QUyAFokOmow.mp4 23.58MB
[利用python进行机器学习(含字幕)].12.Testing.Assumptions.-.Practical.Machine.Learning.Tutorial.with.Python.p.12-Kpxwl2u-Wgk.mp4 84.1MB
[利用python进行机器学习(含字幕)].13.Classification.w_.K.Nearest.Neighbors.Intro.-.Practical.Machine.Learning.Tutorial.with.Python.p.13-44jq6ano5n0.mp4 14.69MB
[利用python进行机器学习(含字幕)].14.K.Nearest.Neighbors.Application.-.Practical.Machine.Learning.Tutorial.with.Python.p.14-1i0zu9jHN6U.mp4 85.58MB
[利用python进行机器学习(含字幕)].15.Euclidean.Distance.-.Practical.Machine.Learning.Tutorial.with.Python.p.15-hl3bQySs8sM.mp4 17.31MB
[利用python进行机器学习(含字幕)].16.Creating.Our.K.Nearest.Neighbors.Algorithm.-.Practical.Machine.Learning.with.Python.p.16-n3RqsMz3-0A.mp4 32.55MB
[利用python进行机器学习(含字幕)].17.Writing.our.own.K.Nearest.Neighbors.in.Code.-.Practical.Machine.Learning.Tutorial.with.Python.p.17-GWHG3cS2PKc.mp4 44.91MB
[利用python进行机器学习(含字幕)].18.Applying.our.K.Nearest.Neighbors.Algorithm.-.Practical.Machine.Learning.Tutorial.with.Python.p.18-3XPhmnf96s0.mp4 61.83MB
[利用python进行机器学习(含字幕)].19.Final.thoughts.on.K.Nearest.Neighbors.-.Practical.Machine.Learning.Tutorial.with.Python.p.19-r_D5TTV9-2c.mp4 81.99MB
[利用python进行机器学习(含字幕)].Machine.Learning.with.Python-subtitle.zip 1.4MB
[利用python进行机器学习(含字幕)].20.Support.Vector.Machine.Intro.and.Application.-.Practical.Machine.Learning.Tutorial.with.Python.p.20.mp4 17.11MB
内容介绍:

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01.Practical Machine Learning Tutorial with Python Intro p.1.mp4: https://jsjzlgx.ctfile.com/fs/yrR164570787

02.Regression Intro - Practical Machine Learning Tutorial with Python p.2-JcI5Vnw0b2c.mp4: https://jsjzlgx.ctfile.com/fs/Z5g164570783

03.Regression Features and Labels - Practical Machine Learning Tutorial with Python p.3-lN5jesocJjk.mp4: https://jsjzlgx.ctfile.com/fs/leI164570791

04.Regression Training and Testing - Practical Machine Learning Tutorial with Python p.4-r4mwkS2T9aI.mp4: https://jsjzlgx.ctfile.com/fs/GKi164570795

05.Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5-QLVMqwpOLPk.mp4: https://jsjzlgx.ctfile.com/fs/vkz164570799

06.Pickling and Scaling - Practical Machine Learning Tutorial with Python p.6-za5s7RB_VLw.mp4: https://jsjzlgx.ctfile.com/fs/uxr164570803

07.Regression How it Works - Practical Machine Learning Tutorial with Python p.7-V59bYfIomVk.mp4: https://jsjzlgx.ctfile.com/fs/9jL164570807

08.How to program the Best Fit Slope - Practical Machine Learning Tutorial with Python p.8-SvmueyhSkgQ.mp4: https://jsjzlgx.ctfile.com/fs/QfM164541659

09.How to program the Best Fit Line - Practical Machine Learning Tutorial with Python p.9-KLGfMGsgP34.mp4: https://jsjzlgx.ctfile.com/fs/7kh164541663

10.R Squared Theory - Practical Machine Learning Tutorial with Python p.10--fgYp74SNtk.mp4: https://jsjzlgx.ctfile.com/fs/XiY164541667

11.Programming R Squared - Practical Machine Learning Tutorial with Python p.11-QUyAFokOmow.mp4: https://jsjzlgx.ctfile.com/fs/epW164541671

12.Testing Assumptions - Practical Machine Learning Tutorial with Python p.12-Kpxwl2u-Wgk.mp4: https://jsjzlgx.ctfile.com/fs/Btw164541675

13.Classification w_ K Nearest Neighbors Intro - Practical Machine Learning Tutorial with Python p.13-44jq6ano5n0.mp4: https://jsjzlgx.ctfile.com/fs/5Cs164541679

14.K Nearest Neighbors Application - Practical Machine Learning Tutorial with Python p.14-1i0zu9jHN6U.mp4: https://jsjzlgx.ctfile.com/fs/Nyk164541683

15.Euclidean Distance - Practical Machine Learning Tutorial with Python p.15-hl3bQySs8sM.mp4: https://jsjzlgx.ctfile.com/fs/SvS164541687

16.Creating Our K Nearest Neighbors Algorithm - Practical Machine Learning with Python p.16-n3RqsMz3-0A.mp4: https://jsjzlgx.ctfile.com/fs/uid164541691

17.Writing our own K Nearest Neighbors in Code - Practical Machine Learning Tutorial with Python p.17-GWHG3cS2PKc.mp4: https://jsjzlgx.ctfile.com/fs/DXk164541695

18.Applying our K Nearest Neighbors Algorithm - Practical Machine Learning Tutorial with Python p.18-3XPhmnf96s0.mp4: https://jsjzlgx.ctfile.com/fs/TMt164541699

19.Final thoughts on K Nearest Neighbors - Practical Machine Learning Tutorial with Python p.19-r_D5TTV9-2c.mp4: https://jsjzlgx.ctfile.com/fs/Ysq164541703

20.Support Vector Machine Intro and Application - Practical Machine Learning Tutorial with Python p.20.mp4: https://jsjzlgx.ctfile.com/fs/rdI164541707

21.Understanding Vectors - Practical Machine Learning Tutorial with Python p.21-HHUqhVzctQE.mp4: https://jsjzlgx.ctfile.com/fs/SPM164541715

22.Support Vector Assertion - Practical Machine Learning Tutorial with Python p.22-VngCRWPrNNc.mp4: https://jsjzlgx.ctfile.com/fs/oMF164541723

23.Support Vector Machine Fundamentals - Practical Machine Learning Tutorial with Python p.23-ZDu3LKv9gOI.mp4: https://jsjzlgx.ctfile.com/fs/E7P164541731

24.Support Vector Machine Intro and Application - Practical Machine Learning Tutorial with Python p.20-mA5nwGoRAOo.mp4: https://jsjzlgx.ctfile.com/fs/FOh164541735

24.Support Vector Machine Optimization - Practical Machine Learning Tutorial with Python p.24-bGCafQT5h1s.mp4: https://jsjzlgx.ctfile.com/fs/o22164541747

25.Creating an SVM from scratch - Practical Machine Learning Tutorial with Python p.25-AbVtcUBlBok.mp4: https://jsjzlgx.ctfile.com/fs/WQe164541751

26.SVM Training - Practical Machine Learning Tutorial with Python p.26-QAs2olt7pJ4.mp4: https://jsjzlgx.ctfile.com/fs/Hpc164541759

27.SVM Optimization - Practical Machine Learning Tutorial with Python p.27-VhHLpg7ZS4Q.mp4: https://jsjzlgx.ctfile.com/fs/iTP164541767

28.Completing SVM from Scratch - Practical Machine Learning Tutorial with Python p.28-yrnhziJk-z8.mp4: https://jsjzlgx.ctfile.com/fs/Ktl164541779

29.Kernels Introduction - Practical Machine Learning Tutorial with Python p.29-9IfT8KXX_9c.mp4: https://jsjzlgx.ctfile.com/fs/2Qv164541791

30.Why Kernels - Practical Machine Learning Tutorial with Python p.30-xqg5S-GrrDQ.mp4: https://jsjzlgx.ctfile.com/fs/8yq164541799

31.Soft Margin SVM - Practical Machine Learning Tutorial with Python p.31-JHaqodAQqiI.mp4: https://jsjzlgx.ctfile.com/fs/VVf164541803

32.Soft Margin SVM and Kernels with CVXOPT - Practical Machine Learning Tutorial with Python p.32-XdcfJX-mDG4.mp4: https://jsjzlgx.ctfile.com/fs/BpJ164541815

33.SVM Parameters - Practical Machine Learning Tutorial with Python p.33-93AjE1YY5II.mp4: https://jsjzlgx.ctfile.com/fs/RmD164541823

34.Clustering Introduction - Practical Machine Learning Tutorial with Python p.34-ZueoXMgCd1c.mp4: https://jsjzlgx.ctfile.com/fs/5Vk164541827

35.Handling Non-Numeric Data - Practical Machine Learning Tutorial with Python p.35-8p6XaQSIFpY.mp4: https://jsjzlgx.ctfile.com/fs/a2G164541839

36.K Means with Titanic Dataset - Practical Machine Learning Tutorial with Python p.36-j6jstahQp2A.mp4: https://jsjzlgx.ctfile.com/fs/ZNr164541843

37.Custom K Means - Practical Machine Learning Tutorial with Python p.37-H4JSN_99kig.mp4: https://jsjzlgx.ctfile.com/fs/kcg164541851

38.K Means from Scratch - Practical Machine Learning Tutorial with Python p.38-HRoeYblYhkg.mp4: https://jsjzlgx.ctfile.com/fs/CfF164541855

39.Mean Shift Intro - Practical Machine Learning Tutorial with Python p.39-3ERPpzrDkVg.mp4: https://jsjzlgx.ctfile.com/fs/MW1164541863

40.Mean Shift with Titanic Dataset - Practical Machine Learning Tutorial with Python p.40-TO8I-nggpzs.mp4: https://jsjzlgx.ctfile.com/fs/QFD164541875

41.Mean Shift from Scratch - Practical Machine Learning Tutorial with Python p.41-P-iAd8b7zl4.mp4: https://jsjzlgx.ctfile.com/fs/nEq164541879

42.Mean Shift Dynamic Bandwidth - Practical Machine Learning Tutorial with Python p.42-k1alPDpSGBE.mp4: https://jsjzlgx.ctfile.com/fs/uT5164541883

43.Deep Learning with Neural Networks and TensorFlow Introduction-oYbVFhK_olY.mp4: https://jsjzlgx.ctfile.com/fs/9fM164541887

44.Installing TensorFlow (OPTIONAL) - Deep Learning with Neural Networks and TensorFlow p2.1-CvspEt8kSIg.mp4: https://jsjzlgx.ctfile.com/fs/pUD164541891

45.TensorFlow Basics - Deep Learning with Neural Networks p. 2-pnSBZ6TEVjY.mp4: https://jsjzlgx.ctfile.com/fs/iYS164541895

46.Neural Network Model - Deep Learning with Neural Networks and TensorFlow-BhpvH5DuVu8.mp4: https://jsjzlgx.ctfile.com/fs/Erk164541899

47.Running our Network - Deep Learning with Neural Networks and TensorFlow-PwAGxqrXSCs.mp4: https://jsjzlgx.ctfile.com/fs/PgJ164541903

48.Processing our own Data - Deep Learning with Neural Networks and TensorFlow part 5-7fcWfUavO7E.mp4: https://jsjzlgx.ctfile.com/fs/lZv164541907

49.Preprocessing cont'd - Deep Learning with Neural Networks and TensorFlow part 6-YFxVHD2TNII.mp4: https://jsjzlgx.ctfile.com/fs/6Wa164541911

50.Training_Testing on our Data - Deep Learning with Neural Networks and TensorFlow part 7-6rDWwL6irG0.mp4: https://jsjzlgx.ctfile.com/fs/IKY164541915

51.Using More Data - Deep Learning with Neural Networks and TensorFlow part 8-JeamFbHhmDo.mp4: https://jsjzlgx.ctfile.com/fs/8j0164541919

52.Installing the GPU version of TensorFlow for making use of your CUDA GPU-io6Ajf5XkaM.mp4: https://jsjzlgx.ctfile.com/fs/wsb164541923

53.Recurrent Neural Networks (RNN) - Deep Learning with Neural Networks and TensorFlow 10-hWgGJeAvLws.mp4: https://jsjzlgx.ctfile.com/fs/Jh6164541927

54.RNN Example in Tensorflow - Deep Learning with Neural Networks 11-dFARw8Pm0Gk.mp4: https://jsjzlgx.ctfile.com/fs/rCD164541931

55.Convolutional Neural Networks Basics - Deep Learning withTensorFlow 12-7Wq-QmMT4gM.mp4: https://jsjzlgx.ctfile.com/fs/0jq164541935

56.Convolutional Neural Networks with TensorFlow - Deep Learning with Neural Networks 13-mynJtLhhcXk.mp4: https://jsjzlgx.ctfile.com/fs/2qu164541939

57.TFLearn - Deep Learning with Neural Networks and TensorFlow p. 14-NMd7WjZiCzc.mp4: https://jsjzlgx.ctfile.com/fs/RjK164541943

Machine Learning with Python-subtitle.zip: https://jsjzlgx.ctfile.com/fs/uxm164541947

1)机器学习解决的问题概括的讲,就是模糊的,不确定的问题;

比如说要计算机去做上千亿上万亿次四则运算,要去做一个排序或者选择,哪怕数据很大很精细,但这样的问题也是不需要用到机器学习的,因为这种问题本身是确定的,这种确定往往分两个方面:a)元方法的确定。b)计算目的的确定,或者说是算法结果的确定。

而机器学习不同,机器学习解决的问题和智慧生物解决的很多问题相似,比如说:分类、关联、归纳、总结决策,如果我们要让计算机去分辨水果的种类,去预测疾病的发病率,这些问题就需要机器学习做了,回到刚才那两个方面,对于机器学习(模糊计算问题)来说:a)元方法不确定,对于分类,有很多截然不同的思维方法,这些思维方法的不同源于对于智慧生物分类算法的模拟不同,不像诸如排序算法,其本质大多基于对于给定模型的二分决策流程。b)计算目的不确定,这里说的计算目的不确定,往往是说你基于某种机器学习算法的某种机器学习任务的模型是不确定的,比如说你要分类,但是这时候谈的不仅仅是分类这个领域了,而是基于你这个数据集上的这种机器学习分类算法上加上这样的参数的分类任务了,貌似和前面一点有点相似,但是不同的地方更多一些。

再之,机器学习任务所期望的目标(结果)往往是动态的,比如说某些机器学习任务要求你给一个预测疾病的模型(注意:其实你这个机器学习的任务是“给出模型”,通过这个模型再预测具体的疾病实例是后来的事情了,于是对于计算这个模型,往往再机器学习任务的几个阶段对其的要求是不同的,随着机器学习对于训练样例的分析和计算,可能你这个模型时时刻刻都在变,以前那个模型预测不发病的测试样例可能用现在这个模型变得要发病了);而非机器学习任务却不然,比如排序,不管你的数据怎么变,你排序的“模型”还是基于布尔或者多值的决策的,是不会变的。讨论这一点,是为了说明,有一些看来不是“模糊不确定问题”的问题为什么也要用机器学习来解决,预测疾病是模糊的,但是比如搜索引擎的搜索排名(所谓的Rank问题)其实看来应该是一个简单的“排序”问题,但是却有很多公司用机器学习做,原因是对于解决这种“网页网站排序”的模型是在变的,归根结底还是期望的目标在变得。

2)要机器学习所使用的方法;

由于上一点谈到元方法的不确定,所以,虽然机器学习要解决的任务与智慧生物解决的问题相似,但未必机器学习算法底层上所使用的计算思维和智慧生物是相似的,当然,机器学习和智慧生物在解决问题的宏观层面是一致的,比如强化学习,监督和非监督学习,这些思维是一致的。机器学习算法在底层上可能更多得用到统计学或者概率分布上的一些基本方法。比如贝叶斯,比如所谓的一些分类方法大多数是基于训练样例的概率分布来计算的。

3)机器学习和其他领域的交叉;

简单的说:机器学习可以大约的解释成数据挖掘+人工智能,数据挖掘所描述的是机器学习偏统计,偏数学的一部分;人工智能说的是机器学习偏仿生的一部分。

目录:

01.Practical Machine Learning Tutorial with Python Intro p.1.mp4

02.Regression Intro - Practical Machine Learning Tutorial with Python p.2-JcI5Vnw0b2c.mp4

03.Regression Features and Labels - Practical Machine Learning Tutorial with Python p.3-lN5jesocJjk.mp4

04.Regression Training and Testing - Practical Machine Learning Tutorial with Python p.4-r4mwkS2T9aI.mp4

05.Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5-QLVMqwpOLPk.mp4

06.Pickling and Scaling - Practical Machine Learning Tutorial with Python p.6-za5s7RB_VLw.mp4

07.Regression How it Works - Practical Machine Learning Tutorial with Python p.7-V59bYfIomVk.mp4

08.How to program the Best Fit Slope - Practical Machine Learning Tutorial with Python p.8-SvmueyhSkgQ.mp4

09.How to program the Best Fit Line - Practical Machine Learning Tutorial with Python p.9-KLGfMGsgP34.mp4

10.R Squared Theory - Practical Machine Learning Tutorial with Python p.10--fgYp74SNtk.mp4

11.Programming R Squared - Practical Machine Learning Tutorial with Python p.11-QUyAFokOmow.mp4

12.Testing Assumptions - Practical Machine Learning Tutorial with Python p.12-Kpxwl2u-Wgk.mp4

13.Classification w_ K Nearest Neighbors Intro - Practical Machine Learning Tutorial with Python p.13-44jq6ano5n0.mp4

14.K Nearest Neighbors Application - Practical Machine Learning Tutorial with Python p.14-1i0zu9jHN6U.mp4

15.Euclidean Distance - Practical Machine Learning Tutorial with Python p.15-hl3bQySs8sM.mp4

16.Creating Our K Nearest Neighbors Algorithm - Practical Machine Learning with Python p.16-n3RqsMz3-0A.mp4

17.Writing our own K Nearest Neighbors in Code - Practical Machine Learning Tutorial with Python p.17-GWHG3cS2PKc.mp4

18.Applying our K Nearest Neighbors Algorithm - Practical Machine Learning Tutorial with Python p.18-3XPhmnf96s0.mp4

19.Final thoughts on K Nearest Neighbors - Practical Machine Learning Tutorial with Python p.19-r_D5TTV9-2c.mp4

20.Support Vector Machine Intro and Application - Practical Machine Learning Tutorial with Python p.20.mp4

Machine Learning with Python-subtitle.zip

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