• 投稿

Machine Learning A-Z Hands-On Python & R In Data Science

4527
32
2019-12-15 13:36:21
66
48
287
28
Machine Learning A-Z Hands-On Python & R In Data Science
公开课
编程
Machine Learning A-Z
计算机科学
MACHINELEARNING
Iammyself001 发消息
【苏星河】软件界的富二代，阿里千问有多狠？
苏星河牛通
视频选集
（1/256）
自动连播
001 Applications of Machine Learning
03:24
002 Why Machine Learning is the Future
06:39
003 Installing R and R Studio (MAC & Windows)
05:42
005 Installing Python and Anaconda (MAC & Windows)
07:32
007 Welcome to Part 1 - Data Preprocessing
01:36
008 Get the dataset
06:59
009 Importing the Libraries
05:21
010 Importing the Dataset
11:56
012 Missing Data
15:59
013 Categorical Data
18:02
014 Splitting the Dataset into the Training set and Test set
17:38
015 Feature Scaling
15:37
016 And here is our Data Preprocessing Template!
08:49
018 How to get the dataset
03:20
019 Dataset + Business Problem Description
02:57
021 Simple Linear Regression Intuition - Step 2
03:10
022 Simple Linear Regression in Python - Step 1
09:57
023 Simple Linear Regression in Python - Step 2
08:21
024 Simple Linear Regression in Python - Step 3
06:44
025 Simple Linear Regression in Python - Step 4
14:51
026 Simple Linear Regression in R - Step 1
04:41
027 Simple Linear Regression in R - Step 2
06:00
028 Simple Linear Regression in R - Step 3
03:40
029 Simple Linear Regression in R - Step 4
15:57
030 How to get the dataset
03:20
031 Dataset + Business Problem Description
03:45
032 Multiple Linear Regression Intuition - Step 1
01:04
033 Multiple Linear Regression Intuition - Step 2
01:01
034 Multiple Linear Regression Intuition - Step 3
07:22
035 Multiple Linear Regression Intuition - Step 4
02:12
036 Multiple Linear Regression Intuition - Step 5
15:42
037 Multiple Linear Regression in Python - Step 1
15:58
038 Multiple Linear Regression in Python - Step 2
02:58
039 Multiple Linear Regression in Python - Step 3
05:29
040 Multiple Linear Regression in Python - Backward Elimination - Preparation
13:15
041 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
12:41
042 Multiple Linear Regression in Python - Backward Elimination - Homework Solut
09:12
043 Multiple Linear Regression in R - Step 1
07:52
044 Multiple Linear Regression in R - Step 2
10:27
045 Multiple Linear Regression in R - Step 3
04:28
046 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
17:52
047 Multiple Linear Regression in R - Backward Elimination - Homework Solution
07:35
048 Polynomial Regression Intuition
05:10
049 How to get the dataset
03:20
050 Polynomial Regression in Python - Step 1
11:40
051 Polynomial Regression in Python - Step 2
11:46
052 Polynomial Regression in Python - Step 3
19:59
053 Polynomial Regression in Python - Step 4
05:47
054 Python Regression Template
11:00
055 Polynomial Regression in R - Step 1
09:14
056 Polynomial Regression in R - Step 2
09:59
057 Polynomial Regression in R - Step 3
19:56
058 Polynomial Regression in R - Step 4
09:37
059 R Regression Template
11:59
060 How to get the dataset
03:20
061 SVR in Python
19:58
062 SVR in R
11:45
063 Decision Tree Regression Intuition
11:07
064 How to get the dataset
03:20
065 Decision Tree Regression in Python
14:47
066 Decision Tree Regression in R
19:55
067 Random Forest Regression Intuition
06:45
068 How to get the dataset
03:20
069 Random Forest Regression in Python
16:46
070 Random Forest Regression in R
17:44
071 R-Squared Intuition
05:12
072 Adjusted R-Squared Intuition
09:58
073 Evaluating Regression Models Performance - Homework's Final Part
08:55
074 Interpreting Linear Regression Coefficients
09:17
077 Logistic Regression Intuition
17:08
078 How to get the dataset
03:20
079 Logistic Regression in Python - Step 1
05:48
080 Logistic Regression in Python - Step 2
03:25
081 Logistic Regression in Python - Step 3
02:36
082 Logistic Regression in Python - Step 4
04:34
083 Logistic Regression in Python - Step 5
19:41
084 Python Classification Template
03:54
085 Logistic Regression in R - Step 1
06:00
086 Logistic Regression in R - Step 2
03:00
087 Logistic Regression in R - Step 3
05:24
088 Logistic Regression in R - Step 4
02:49
089 Logistic Regression in R - Step 5
19:25
090 R Classification Template
04:18
091 K-Nearest Neighbor Intuition
04:54
092 How to get the dataset
03:20
093 K-NN in Python
14:11
094 K-NN in R
15:48
095 SVM Intuition
09:50
096 How to get the dataset
03:20
097 SVM in Python
12:25
098 SVM in R
12:10
099 Kernel SVM Intuition
03:18
100 Mapping to a higher dimension
07:51
101 The Kernel Trick
12:21
102 Types of Kernel Functions
03:48
103 How to get the dataset
03:20
104 Kernel SVM in Python
17:53
105 Kernel SVM in R
16:35
106 Bayes Theorem
20:26
107 Naive Bayes Intuition
14:04
108 Naive Bayes Intuition (Challenge Reveal)
06:05
109 Naive Bayes Intuition (Extras)
09:43
110 How to get the dataset
03:20
111 Naive Bayes in Python
09:15
112 Naive Bayes in R
14:54
113 Decision Tree Classification Intuition
08:09
114 How to get the dataset
03:20
115 Decision Tree Classification in Python
12:36
116 Decision Tree Classification in R
19:49
117 Random Forest Classification Intuition
04:30
118 How to get the dataset
03:20
119 Random Forest Classification in Python
19:55
120 Random Forest Classification in R
19:57
121 False Positives & False Negatives
07:59
122 Confusion Matrix
04:58
123 Accuracy Paradox
02:14
124 CAP Curve
11:17
125 CAP Curve Analysis
06:20
128 K-Means Clustering Intuition
14:18
129 K-Means Random Initialization Trap
07:49
130 K-Means Selecting The Number Of Clusters
11:53
131 How to get the dataset
03:20
132 K-Means Clustering in Python
17:56
133 K-Means Clustering in R
11:48
134 Hierarchical Clustering Intuition
08:49
135 Hierarchical Clustering How Dendrograms Work
08:49
136 Hierarchical Clustering Using Dendrograms
11:23
137 How to get the dataset
03:20
138 HC in Python - Step 1
04:59
139 HC in Python - Step 2
06:34
140 HC in Python - Step 3
05:29
141 HC in Python - Step 4
04:30
142 HC in Python - Step 5
04:07
143 HC in R - Step 1
03:46
144 HC in R - Step 2
05:25
145 HC in R - Step 3
03:20
146 HC in R - Step 4
02:47
147 HC in R - Step 5
02:34
150 Apriori Intuition
18:14
151 How to get the dataset
03:20
152 Apriori in R - Step 1
19:54
153 Apriori in R - Step 2
14:26
154 Apriori in R - Step 3
19:19
155 Apriori in Python - Step 1
17:59
156 Apriori in Python - Step 2
14:39
157 Apriori in Python - Step 3
12:07
158 Eclat Intuition
06:06
159 How to get the dataset
03:20
160 Eclat in R
10:10
162 The Multi-Armed Bandit Problem
15:37
163 Upper Confidence Bound (UCB) Intuition
14:54
164 How to get the dataset
03:20
165 Upper Confidence Bound in Python - Step 1
14:43
166 Upper Confidence Bound in Python - Step 2
18:10
167 Upper Confidence Bound in Python - Step 3
18:48
168 Upper Confidence Bound in Python - Step 4
03:55
169 Upper Confidence Bound in R - Step 1
13:40
170 Upper Confidence Bound in R - Step 2
16:00
171 Upper Confidence Bound in R - Step 3
17:39
172 Upper Confidence Bound in R - Step 4
03:19
173 Thompson Sampling Intuition
19:13
174 Algorithm Comparison_ UCB vs Thompson Sampling
08:13
175 How to get the dataset
03:20
176 Thompson Sampling in Python - Step 1
19:47
177 Thompson Sampling in Python - Step 2
03:44
178 Thompson Sampling in R - Step 1
19:02
179 Thompson Sampling in R - Step 2
03:28
181 How to get the dataset
03:20
182 Natural Language Processing in Python - Step 1
12:44
183 Natural Language Processing in Python - Step 2
10:56
184 Natural Language Processing in Python - Step 3
01:42
185 Natural Language Processing in Python - Step 4
12:11
186 Natural Language Processing in Python - Step 5
07:17
187 Natural Language Processing in Python - Step 6
03:05
188 Natural Language Processing in Python - Step 7
07:24
189 Natural Language Processing in Python - Step 8
16:58
190 Natural Language Processing in Python - Step 9
06:00
191 Natural Language Processing in Python - Step 10
09:57
193 Natural Language Processing in R - Step 1
16:36
194 Natural Language Processing in R - Step 2
08:40
195 Natural Language Processing in R - Step 3
06:29
196 Natural Language Processing in R - Step 4
02:59
197 Natural Language Processing in R - Step 5
02:06
198 Natural Language Processing in R - Step 6
05:50
199 Natural Language Processing in R - Step 7
03:28
200 Natural Language Processing in R - Step 8
05:22
201 Natural Language Processing in R - Step 9
12:51
202 Natural Language Processing in R - Step 10
17:32
205 What is Deep Learning_
12:35
206 Plan of attack
02:53
207 The Neuron
16:26
208 The Activation Function
08:30
209 How do Neural Networks work_
12:49
210 How do Neural Networks learn_
13:00
211 Gradient Descent
10:14
212 Stochastic Gradient Descent
08:46
213 Backpropagation
05:23
214 How to get the dataset
03:20
215 Business Problem Description
05:00
216 ANN in Python - Step 1 - Installing Theano_ Tensorflow and Keras
13:00
217 ANN in Python - Step 2
18:17
218 ANN in Python - Step 3
03:15
219 ANN in Python - Step 4
02:22
220 ANN in Python - Step 5
12:21
221 ANN in Python - Step 6
02:45
222 ANN in Python - Step 7
03:33
223 ANN in Python - Step 8
06:56
224 ANN in Python - Step 9
06:22
225 ANN in Python - Step 10
06:47
226 ANN in R - Step 1
17:18
227 ANN in R - Step 2
06:31
228 ANN in R - Step 3
12:31
229 ANN in R - Step 4 (Last step)
14:08
230 Plan of attack
03:33
231 What are convolutional neural networks_
15:50
232 Step 1 - Convolution Operation
16:39
233 Step 1(b) - ReLU Layer
06:42
234 Step 2 - Pooling
14:14
235 Step 3 - Flattening
01:54
236 Step 4 - Full Connection
19:26
237 Summary
04:21
238 Softmax & Cross-Entropy
18:21
239 How to get the dataset
03:20
240 CNN in Python - Step 1
12:46
241 CNN in Python - Step 2
03:01
242 CNN in Python - Step 3
01:06
243 CNN in Python - Step 4
12:52
244 CNN in Python - Step 5
04:59
245 CNN in Python - Step 6
05:01
246 CNN in Python - Step 7
05:58
247 CNN in Python - Step 8
02:50
248 CNN in Python - Step 9
19:45
249 CNN in Python - Step 10
08:29
252 How to get the dataset
03:20
253 PCA in Python - Step 1
11:47
254 PCA in Python - Step 2
08:06
255 PCA in Python - Step 3
09:49
256 PCA in R - Step 1
12:09
257 PCA in R - Step 2
11:23
258 PCA in R - Step 3
13:43
259 How to get the dataset
03:20
260 LDA in Python
18:11
261 LDA in R
20:01
262 How to get the dataset
03:20
263 Kernel PCA in Python
14:28
264 Kernel PCA in R
20:31
266 How to get the dataset
03:20
267 k-Fold Cross Validation in Python
13:47
268 k-Fold Cross Validation in R
19:30
269 Grid Search in Python - Step 1
15:10
270 Grid Search in Python - Step 2
11:05
271 Grid Search in R
14:00
272 How to get the dataset
03:20
273 XGBoost in Python - Step 1
09:32
274 XGBoost in Python - Step 2
12:44
275 XGBoost in R
18:15

汇编语言（王爽）
Iammyself001
5.3万 293

【全500集】目前B站最全最细的Python零基础全套教程（包含爬虫+数据分析），7天从入门到精通Python，学完即可就业！看完这一套Python教程就够了！
pycharm官方教程
1.5万 4

【高清双语】公认最好的【吴恩达Python数据分析课程】谁学谁赚到！Python for Data Analytics | 附课件、代码
从零玩转AI
382 14

【北京大学公开课】高等代数 - 40.PKU高等代数40
Alonus
1247 0

《折线统计图》新课标 余老师（课堂实录+PPT课件+希沃课件+教案+逐字稿）☆
明华老师
2027 0

膜拜！居然两小时即可上手Transformer，计算机大佬从工作流程到模型训练用大白话带你吃透！
转行AI
609 2

（黎珊彤）八年级舞蹈热身研究课
11113467
5012 0

Energy crisis： The very long winter
Iammyself001
2 0

想学PLC，不买设备不报班，只需电脑和AI，半天PLC编程就能入门，后期想深入学PLC，可以让Al制定学习计划，学指令，学项目，零成本学会PLC只是时间问题。
70后陌雪
7105 0

416_hd_building_modern_frameworks
Iammyself001
2 0

Python API 入门，1 分钟搞懂请求与响应
水哥澎湃
8089 2

Apple Special Event(2009/09/09)
Iammyself001
2 0

论文和学术
Iammyself001
1 0

The World Cup： Not quite over the goal line
Iammyself001
2 0

The play’s the thing
Iammyself001
1 0

The gaming market： CoD and chips
Iammyself001
1 0

[WebUI] [ipadapter] 人脸面部复刻(SDXL版)
Iammyself001
6 0

Amazon’s new health care push
Iammyself001
2 0

707_hd_whats_new_in_foundation_networking
Iammyself001
5 0

703_hd_whats_new_in_the_accelerate_framework
Iammyself001
1 0
展开
万元大奖，助你守护生态！
客服
顶部
赛事库 课堂 2021拜年纪