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Python机器学习视频教程,时候有一定Python基础的学员学习,难得的机器学习视频教程- W, ?6 l1 t7 f% O& M
课程目录
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" T' N" w D* A j" L" b课时01.课程介绍(主题与大纲).flv0 u& Q: l( L- {) w b B- i
课时02.机器学习概述.flv8 x9 k" c. O, }9 o
课时03.使用Anaconda安装python环境.flv, K8 |: n& _$ J2 {- C
课时04.课程数据,代码,PPT(在参考资料界面).swf
- O; t( j/ l- T7 s课时05.科学计算库Numpy.flv0 k+ M) S5 ^9 M
课时06.Numpy基础结构.flv
, I* |: y' T! l课时07.Numpy矩阵基础.flv_d.flv0 X5 L: X# d) h1 C8 R
课时08.Numpy常用函数.flv_d.flv
# N7 C" \; c2 @9 J! e9 o课时09.矩阵常用操作.flv_d.flv, d5 E# p4 Q8 N
课时10.不同复制操作对比.flv_d.flv' Y. ~- ~' Z0 Q" G( Z6 X& l
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课时11.Pandas数据读取.flv
1 n8 L; b/ a" N: a课时12.Pandas索引与计算.flv_d.flv, r/ \" N9 @% {) `1 n
课时13.Pandas数据预处理实例.flv_d.flv" O6 s- O4 c1 R3 J* ^
课时14.Pandas常用预处理方法.flv_d.flv
) A/ Y' ^6 J# m; ]/ L( z& e课时15.Pandas自定义函数.flv_d.flv
% Z7 V9 G2 B( Z课时16.Series结构.flv_d.flv& e# x5 ^$ o5 Q; s- k5 P7 `( Y
/ b' M q7 [$ j7 d$ v课时17.折线图绘制.flv
. p0 K; v/ z. ~ [课时18.子图操作.flv_d.flv
G# m* X4 U! j8 V& T1 r2 k+ ~课时19.条形图与散点图.flv_d.flv+ n- Q4 I! w" {3 c' b- a3 q, L
课时20.柱形图与盒图.flv_d.flv
; Y& L/ Q: R& _课时21.细节设置.flv_d.flv
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课时22.Seaborn简介.flv
8 n" v3 r; x/ o$ M课时23.整体布局风格设置.flv_d.flv
: L/ ?( B; Y- `5 Z4 p2 n- b7 e课时24.风格细节设置.flv_d.flv
& y6 l/ ~) M( B" V课时25.调色板.flv_d.flv
# R$ }9 [* C; V% H5 T8 D课时26.调色板.flv_d.flv
& ]8 X6 |" Y7 g `, P& _: U课时27.调色板颜色设置.flv_d.flv
* J7 k5 m& I- C课时28.单变量分析绘图.flv_d.flv$ z! M5 B6 C* R! n" h
课时29.回归分析绘图.flv_d.flv
7 a6 Q& D: T; N1 T+ h. K课时30.多变量分析绘图.flv_d.flv
4 @, A8 h8 v& {课时31.分类属性绘图.flv_d.flv
! Y. s0 }0 O1 b$ l/ {/ c课时32.Facetgrid使用方法.flv_d.flv
0 R% f3 F( T. c: {课时33.Facetgrid绘制多变量.flv_d.flv1 M& c* ]# U f
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课时34.热度图绘制.flv_d.flv" B l: X/ ^' a% n" j
课时35.回归算法综述.flv_d.flv
" q' q D$ m- t. z. w( g" Y3 k课时36.回归误差原理推导.flv_d.flv
% g# M$ N& |2 u0 r+ p+ C! ^课时37.回归算法如何得出最优解.flv_d.flv
' P. k2 l2 @! Y" ?& ^4 d课时38.基于公式推导完成简易线性回归.flv_d.flv) S ]* V$ U, y+ p \5 B
课时39.逻辑回归与梯度下降.flv_d.flv' _0 o8 N$ {5 K( w" d) C
) C6 C: f& N& s' ?6 g! E课时40.使用梯度下降求解回归问题.flv_d.flv
- p5 l1 f5 ~: |4 I课时41.决策树算法综述.flv_d.flv/ O' E2 j) ~ O7 J1 U/ q# m
课时42.决策树熵原理.flv_d.flv7 L* Q" f" v. r, ]1 @# x+ g* h
课时43.决策树构造实例.flv_d.flv
7 S5 o4 d) ?8 @4 g9 N+ ^课时44.信息增益原理.flv_d.flv0 b% {1 r$ `6 I2 U+ n" |4 h% W
课时45.信息增益率的作用.flv_d.flv
! H" s/ T9 s! ]2 v) u' X2 }课时46.决策树剪枝策略.flv_d.flv6 T% l2 ?' W7 ]9 b7 |$ g
课时47.随机森林模型.flv_d.flv
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课时48.决策树参数详解.flv_d.flv! Z' i* ]5 g* W. e7 }9 G5 G
课时49.贝叶斯算法概述.flv_d.flv' Q+ E$ P6 u) P0 F
课时50.贝叶斯推导实例.flv_d.flv
% z. x) Y3 v8 `5 f! C课时51.贝叶斯拼写纠错实例.flv_d.flv0 i/ D- m6 y9 }8 I$ L
课时52.垃圾邮件过滤实例.flv_d.flv
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( p% k: {" l0 f1 @4 p课时53.贝叶斯实现拼写检查器.flv_d.flv
& T7 C2 ]2 s5 y1 n# ~" E) a9 u2 {课时54.支持向量机要解决的问题.flv_d.flv# e5 m1 V2 Z$ f+ k$ i" K/ d
课时55.支持向量机目标函数.flv_d.flv' k/ C2 h, o5 q; ]- W3 ?
课时56.支持向量机目标函数求解.flv_d.flv& {; |5 v! T" o. @; V% N
课时57.支持向量机求解实例.flv_d.flv+ v f' d& A3 X) R) @
课时58.支持向量机软间隔问题.flv_d.flv" m5 ` D6 Y, i( w
课时59.支持向量核变换.flv_d.flv
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课时60.SMO算法求解支持向量机.flv_d.flv
" ^( \) k2 j% f+ T6 N课时61.初识神经网络.flv_d.flv
5 U& ^2 x. W" R( X4 B( c' @课时62.计算机视觉所面临的挑战.flv_d.flv! d5 e0 A/ D, ^
课时63.K近邻尝试图像分类.flv_d.flv
2 b2 f7 p3 S" U( p' L& {课时64.超参数的作用.flv_d.flv
% e& c. Q7 E; D: w! g课时65.线性分类原理.flv_d.flv
, N2 _6 B" Y; U }5 [ M* Y课时66.神经网络-损失函数.flv_d.flv3 l8 t; T: p& l/ H! G3 a
课时67.神经网络-正则化惩罚项.flv_d.flv
3 O" w8 M! d! u' m课时68.神经网络-softmax分类器.flv_d.flv$ d/ v6 g/ S" s2 q
课时69.神经网络-最优化形象解读.flv_d.flv. ~+ d3 X4 J; Q/ ?1 C2 r2 M
课时70.神经网络-梯度下降细节问题.flv_d.flv
1 h' }2 Y: {9 h5 W2 G3 u课时71.神经网络-反向传播.flv_d.flv
) s: J/ {9 V( E5 e8 x7 U) ^8 L课时72.神经网络架构.flv_d.flv/ V$ x; V z! d7 l% r1 q: K: Y9 x0 M
课时73.神经网络实例演示.flv_d.flv
9 t( |% D+ B' [: K课时74.神经网络过拟合解决方案.flv_d.flv
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课时75.感受神经网络的强大.flv_d.flv( E3 I5 E2 x1 j$ Q. ^" J. b
课时76.集成算法思想.flv_d.flv
8 M* g6 \: q5 S D/ {课时77.xgboost基本原理.flv_d.flv
& T: x D; ^" d8 |9 Y$ S& Y课时78.xgboost目标函数推导.flv_d.flv
- Q& i5 J" H% x7 u2 y+ R1 T课时79.xgboost求解实例.flv_d.flv
* y& j9 @ ^! L/ h课时80.xgboost安装.flv_d.flv
6 g, h% d" ]' z9 ?; |课时81.xgboost实战演示.flv_d.flv
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. O, J) b& W+ X: P! U% @+ q' q课时82.Adaboost算法概述.flv_d.flv
+ ~' _& L! d0 ^) z3 }课时83.自然语言处理与深度学习加微信ff1318860.flv_d.flv
8 W( w- ^8 ^# M% s课时84.语言模型.flv_d.flv
. Y* `) X" I( X3 D课时85.-N-gram模型.flv_d.flv; A; w1 {, I; t" _. Q3 u9 r' l- o/ {
课时86.词向量.flv_d.flv, d- x! ?6 P. x3 U9 C$ d; ~4 U7 ]9 F
课时87.神经网络模型.flv_d.flv
& d+ b6 s- p# L. X3 j课时88.Hierarchical.Softmax.flv_d.flv
! D& d! T8 ?2 E课时89.CBOW模型实例.flv_d.flv
- A6 Q' D9 l: a6 ~6 @) |, O5 B1 p( g- c课时90.CBOW求解目标.flv_d.flv
) u% n; g' t1 m* o9 O! U) N T课时91.梯度上升求解.flv_d.flv# E; X& ~: G! E6 X6 y5 z
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课时92.负采样模型.flv_d.flv
- ^) r( p" }) W3 N5 e5 B课时93.无监督聚类问题.flv_d.flv& s) i s# p. _; W7 h
课时94.聚类结果与离群点分析.flv_d.flv' W, q4 n+ Z. F B3 Y6 R- f
课时95.K-means聚类案例对NBA球员进行评估.flv_d.flv
. n# ?- e! N' B9 H% t: S0 e# ?) ~课时96.使用Kmeans进行图像压缩.flv_d.flv& I1 E& x. }/ |: g4 t5 O- V# b, B
课时97.K近邻算法原理.flv_d.flv
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课时100.PCA实例.flv_d.flv% @# f6 z \/ C& B
课时101.SVD奇异值分解原理.flv_d.flv1 f& T: l' J2 J. T h3 Z
课时98.K近邻算法代码实现.flv_d.flv: }& ^- n0 q0 v+ S8 k
课时99.PCA基本原理.flv_d.flv
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, `+ K2 \1 e3 O; k( }6 y q. [课时102.SVD推荐系统应用实例.flv_d.flv
4 t9 Q; C* W0 U. _课时103.使用python库分析汽车油耗效率.flv$ ?" ]) t& s: j, h, x! K, ? p
课时104.使用scikit-learn库建立回归模型.flv_d.flv- {! c' e% B7 A; Y* z+ q: k, D
课时105.使用逻辑回归改进模型效果.flv_d.flv
5 D) y9 U) z/ T8 p; W/ Z课时106..模型效果衡量标准.flv_d.flv
' M" |0 U6 C# z( U课时107.ROC指标与测试集的价值.flv_d.flv8 y2 ~' f. V: r/ h+ x' y. \' D8 C
课时108.交叉验证.flv_d.flv/ A$ r5 _. q' Z7 N8 i+ o
" c2 Z: s, ~1 T: K( x" S课时109.多类别问题.flv_d.flv
! B: r: H' W2 I3 M1 \课时110.Kobe.Bryan生涯数据读取与简介.flv( B7 c M# r% Q0 j9 }
课时111.特征数据可视化展示.flv_d.flv4 t, x# E* p" B0 E
课时112.数据预处理.flv_d.flv: ^2 A, o' U, X7 S, e1 o% a
% a# Y5 m6 T& R% S9 ~" U; u课时113.使用Scikit-learn建立模型.flv_d.flv
: j6 r. r9 [, z7 b. N! R9 |1 t课时114.船员数据分析.flv6 p: j4 G# s+ t/ g9 _; I
课时115.数据预处理.flv_d.flv
! g- Y2 A, L3 O! k- ], s课时116.使用回归算法进行预测.flv_d.flv, ^" Q& k: o# \; I) \
课时117.使用随机森林改进模型.flv_d.flv
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课时118.随机森林特征重要性分析.flv_d.flv
# M8 a* h! A/ t3 y) G/ c课时119.案例背景和目标.flv_d.flv9 R. l; l- Q! n
课时120.样本不均衡解决方案.flv_d.flv
! Z# w& A1 o8 E课时121.下采样策略.flv_d.flv
5 M* e3 ]4 m$ T+ ~, O- X课时122.交叉验证.flv_d.flv
; O0 r$ T! S1 h6 R/ L课时123.模型评估方法.flv_d.flv
& C x D6 a9 g/ J* @8 j" h! o. r课时124.正则化惩罚.flv_d.flv0 x' y2 T7 V* W
课时125.逻辑回归模型.flv_d.flv
( N+ z7 j2 Z( O3 g0 M. f4 y课时126.混淆矩阵.flv_d.flv
7 [& |- g3 n4 f% [* K- {! L6 U课时127.逻辑回归阈值对结果的影响.flv_d.flv
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课时128.SMOTE样本生成策略.flv_d.flv/ U- D3 d* j0 d3 A: d
课时129.文本分析与关键词提取.flv_d.flv
# g9 L: L( | g6 _课时130.相似度计算.flv_d.flv
! M+ A5 P6 A J% R课时131.新闻数据与任务简介.flv_d.flv2 ]4 w" `, R8 e: \- j& x
课时132.TF-IDF关键词提取.flv_d.flv
7 b( G; ^7 v6 p( h) U9 W& k9 |课时133.LDA建模.flv_d.flv- T8 X1 H: f, ?/ h @7 R
9 l1 E9 n7 Y3 V' b$ }课时134.基于贝叶斯算法进行新闻分类.flv_d.flv
" f8 @1 J% z4 m8 ]( T% P9 g# _课时135.章节简介.flv
6 [2 c* T2 |# d9 K7 M/ D$ X课时136.Pandas生成时间序列.flv_d.flv
& U* \9 S* O' l+ J; ~! j8 L课时137.Pandas数据重采样.flv_d.flv
- V# l4 h* O* S6 X/ ~5 G; q课时138.Pandas滑动窗口.flv_d.flv
# r8 T9 N2 B7 ^, k! J课时139.数据平稳性与差分法.flv_d.flv
1 X2 l! ]/ R* _; w5 \; ~! ]- ~" q, ^课时140.ARIMA模型.flv_d.flv
8 }% B0 V, z5 e课时141.相关函数评估方法.flv_d.flv
" N* \1 [/ `* A5 w" X/ r( l课时142.建立ARIMA模型.flv_d.flv+ Y5 B, T! x8 D
课时143.参数选择.flv_d.flv( Y9 B4 L+ D! }) \, @& r9 x- K3 i
课时144.股票预测案例.flv_d.flv+ U0 m- W3 t3 o6 y* Z0 Q7 T
课时145.使用tsfresh库进行分类任务.flv_d.flv
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课时146.维基百科词条EDA.flv_d.flv' s* N% b. O, l6 Q: _* {
课时147.使用Gensim库构造词向量.flv_d.flv
9 R2 ]& c8 K$ e: ~, m: ~课时148.维基百科中文数据处理.flv_d.flv
3 l; _. C" k; t8 a3 y- }. O课时149.Gensim构造word2vec模型.flv_d.flv
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课时150.测试模型相似度结果.flv_d.flv7 O- S8 ~! W2 ^4 X; q9 P: ~
课时151.数据清洗过滤无用特征.flv_d.flv( L& f! R9 W, {- j5 Z1 C
课时152.数据预处理.flv_d.flv
+ W2 ]% c [9 C# J' u2 E% y4 _课时153.获得最大利润的条件与做法.flv_d.flv
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9 {; o2 n9 C. d {3 F课时154.预测结果并解决样本不均衡问题.flv_d.flv
" }% }9 g% [4 }1 D课时155.数据背景介绍.flv_d.flv
$ G3 y7 H' d' n, A8 Z课时156.数据预处理.flv_d.flv
) Q: d5 [0 E7 X. Y$ p课时157.尝试多种分类器效果.flv_d.flv
" x4 u& m6 Q* Y课时158.结果衡量指标的意义.flv_d.flv# U8 k. f, m: ]7 v! _
: _( v! [+ Z$ P ~1 c7 K3 r& \课时159.应用阈值得出结果.flv_d.flv
5 T/ {: q8 [" B A课时160.内容简介.flv_d.flv
$ }3 E5 Z9 d7 X7 q7 b课时161.数据背景介绍.flv
2 g) h, S1 y; }& m1 r课时162.数据读取与预处理.flv_d.flv' a% P6 E/ u7 L' E) \$ N5 X" ?
课时163.数据切分模块.flv_d.flv
9 T; l- s5 e+ N4 \% U! {+ |0 P! P课时164.缺失值可视化分析.flv_d.flv, P3 i2 {2 W9 O# H& U/ c
课时165.特征可视化展示.flv_d.flv5 h1 Q$ L7 u/ P! J4 l H0 h, M
课时166.多特征之间关系分析.flv_d.flv& D$ Q7 z' Z) G! M
课时167.报表可视化分析.flv_d.flv
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课时168.红牌和肤色的关系.flv_d.flv
: d) |4 K$ ]8 b4 d9 V: `/ ?& F/ _0 z课时169.数据背景简介.flv_d.flv
' l5 s, W( _* k! C+ K1 @" F课时170.数据切片分析.flv_d.flv0 \+ T# s7 K M
课时171.单变量分析.flv_d.flv
( m8 D+ a/ n2 t0 T课时172.峰度与偏度.flv_d.flv: e5 H* a9 r! z
课时173.数据对数变换.flv_d.flv( ^3 O% I& m9 q7 c/ O2 |2 v
课时174.数据分析维度.flv_d.flv
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- `7 }8 j8 Z8 r" H8 e课时175.变量关系可视化展示.flv_d.flv
( n) u$ F0 Z8 F( K2 T7 @课时176.建立特征工程.flv_d.flv
. V Q0 G0 s% g% [3 z' _课时177.特征数据预处理.flv_d.flv" ] ^$ `; T" o! p. z8 Y6 v* w
课时178.应用聚类算法得出异常IP点.flv_d.flv
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