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001 المقدمة00:07:50
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002 ملاحظات سريعة00:02:14
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003 بيئة عمل بايثون00:05:34
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004 بيئة عمل R00:01:38
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005 المعالجة المسبقة للبيانات00:05:32
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006 المكتبات في بايثون00:03:57
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007 المكتبات في لغة R100:02:33
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008 استيراد الداتا بالبايثون00:06:32
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009 ستيراد الداتا في R00:03:33
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010 معالجة البيانات المفقودة في بايثون00:04:20
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011 معالجة البيانات المفقودة في R00:08:21
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012 معالجة البيانات النصية بالبايثون00:14:26
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013 معالجة البيانات النصية في R00:06:28
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014 فصل الداتا بالبايثون00:06:05
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015 فصل الداتا في R00:09:15
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016 Scaling in Python00:06:35
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017 Scaling in R00:04:43
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018 علم البيانات و الأعمال00:05:05
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019 شرح simple linear regression00:07:15
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020 شرح simple linear regression 200:03:16
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021 SLR الخطوة الأولى بالبايثون00:12:01
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022 SLR الخطوة الثانية بالبايثون00:05:38
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023 SLR الخطوة الثالثة بالبايثون00:05:10
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024 SLR الخطوة الرابعة بالبايثون00:07:58
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025 SLR inR الخطوة الأولى00:09:41
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026 SLR inR الخطوة الثانية00:03:54
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027 SLR inR الخطوة الثالثة00:05:06
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028 SLR inR الخطوةالرابعة00:10:02
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029 مناقشة الداتاست00:02:59
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030 مقدمة MLR00:04:08
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031 Dummy Variable00:07:19
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032 P Value00:01:53
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033 Building a model00:10:05
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034 MLR in Python Step100:11:01
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035 MLR in Python Step200:03:36
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036 MLR in Python Step300:02:21
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037 التحضير لعمل BE00:05:12
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038 BE00:08:39
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039 Automatic BE00:01:57
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040 MLR in R step100:08:34
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041 MLR in R step 200:04:37
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042 MLR in R step 300:04:17
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043 Automatic BE in R00:08:19
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044 Polynomial Regression00:07:07
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045 مجموعة البيانات00:03:30
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046 Poly Reg in python step100:08:25
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047 Poly Reg in python step 200:09:47
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048 Poly Reg in python step 300:13:51
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049 Poly Reg in python step 400:05:00
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050 Poly Reg in R step 100:06:18
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051 Poly Reg in R step 200:08:36
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052 Poly reg in R step 300:09:55
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053 Poly Reg in R step 400:05:43
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054 Support Vector Regressor00:07:39
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055 SVR in python00:18:16
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056 SVR in R00:13:34
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057 Decision Tree Introduction00:05:40
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058 Decision Tree in python00:08:59
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059 Decision Tree in R00:11:41
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060 Decision Tree Introduction00:05:40
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061 Decision Tree in python00:08:59
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062 Decision Tree in R00:11:41
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063 Random Forest Introduction00:03:43
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064 Random Forest in Python00:17:29
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065 Random Forest in R00:15:47
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066 Which model00:03:09
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067 Continuous Uniform Distributions00:06:51
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068 Binomial Distribution00:10:03
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069 Poisson Distribution00:07:11
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070 Normal Distribution00:05:37
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071 T Distribution00:07:17
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072 Hypothesis Testing00:08:14
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073 Web Scrapping 100:10:47
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074 Web Scrapping 200:10:42
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075 Python Part100:08:54
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076 Python Part200:07:26
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077 Python Part300:08:43
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078 Python Part400:08:27
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079 Kernel SVM in Python00:14:22
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080 Kernel SVM in R00:05:31
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081 Naive Bayes Introduction00:04:48
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082 Naive Bayes in Python00:12:20
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083 Naive Bayes in R00:09:23
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084 DT Classification Introduction00:04:41
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085 DT Classifier in Python00:15:06
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086 DT Classifier in R00:10:47
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087 Random Forest00:02:59
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088 Random Forest classifier in Python00:13:38
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089 Random Forest classifier in R00:12:56
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090 Evaluating Performance00:07:29
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091 كيف أختار المودل؟00:03:26
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092 Clustering Introduction00:04:31
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093 K means Algorithms00:04:29
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094 K means in Python00:24:06
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095 K means in R00:18:54
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096 Hierarchical Clustering introduction00:06:39
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097 HC in Python step 100:08:19
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098 HC in Python step 200:06:49
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099 HC in R step 100:07:16
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100 HC in R step 200:06:34
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101 Apriori Algorithms intro00:04:59
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102 Apriori Algorithms step 1 in Python00:09:10
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103 Apriori Algorithms step 2 in Python00:08:14
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104 Apriori Algorithms step 3 in Python00:07:43
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105 Apriori Algorithms step 1 in Pythonn00:07:12
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106 Apriori Algorithms step 2 in Pythonn00:04:40
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107 Apriori Algorithms step 3 in Pythonn00:03:34
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108 Elcat Model00:02:13
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109 Elcat Model in R00:08:03
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110 Reinforcement Learning00:02:35
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111 Multi Armed Bandit Problem00:08:04
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112 Upper Confidence Bound00:06:58
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113 USB step 1 in Python00:08:03
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114 UCB step 2 in Python00:16:54
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115 UCB step 3 in Python00:08:23
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116 UCB step 4 in Python00:03:10
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117 UCB step 1 in R00:08:54
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118 UCB step 2 in R00:14:52
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119 UCB step 3 in R00:08:16
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120 UCB step 4 in R00:03:03
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121 Thompson Sampling Alg00:03:58
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122 Thompson in Python00:13:17
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123 Thompson in R00:15:05
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124 Natural Language Processing Intro00:07:58
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125 NLP step 1 in Python00:09:04
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126 NLP step 2 in Python00:09:41
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127 NLP step 3 in Python00:01:22
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128 NLP step 4 in Python00:08:41
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129 NLP step 5 in Python00:10:08
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130 NLP step 6 in Python00:07:04
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131 NLP step 7 in Python00:04:11
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132 NLP step 8 in Python00:09:55
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133 NLP step 1 in R00:07:19
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134 NLP step 2 in R00:04:36
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135 NLP step 3 in R00:03:01
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136 NLP step 4 in R00:02:08
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137 NLP step 5 in R00:02:12
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138 NLP step 6 in R00:03:46
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139 NLP step 7 in R00:03:36
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140 NLP step 8 in R00:02:35
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141 NLP step 9 in R00:09:36
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142 NLP step 10 in R00:07:23
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143 Deep Learning100:04:36
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144 Deep Learning 200:08:21
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145 Business Problem Descriptive00:03:22
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146 Artificial Nerual Network Steps00:03:24
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147 The Neuron00:11:41
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148 The Activiation Function00:05:12
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149 NN works00:07:09
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150 NN learrn00:10:52
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151 Gradient Descent00:07:08
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152 Stochostic Gradient Descent00:03:39
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153 backprobacation00:02:49
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154 ANN IN Python step 100:08:57
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155 ANN IN Python step 200:18:10
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156 ANN IN Python step 300:04:20
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157 ANN IN Python step 400:05:37
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158 ANN IN Python step 500:02:32
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159 ANN IN Python step 600:03:06
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160 ANN IN Python step 700:04:10
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161 ANN IN Python step 800:05:06
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162 ANN IN Python step 900:06:25
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163 ANN IN Python step 1000:05:19
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164 ANN in R step 100:08:48
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165 ANN in R step 200:04:15
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166 ANN in R step 300:05:54
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167 ANN in R step 400:07:12
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168 Convloutional Neural Networks00:03:54
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169 what are CNN00:09:27
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170 step1 Convloution00:05:38
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171 step 2 b RelU00:03:42
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172 step 2 Pooling00:05:09
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173 step 3 Flattning00:02:07
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174 step 4 Fully Connected00:07:23
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175 Summary00:03:07
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176 Softmax Cross Entropy00:04:07
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177 CNN in Python step 100:05:33
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178 CNN in Python step 200:05:16
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179 CNN in Python step 300:02:07
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180 CNN in Python step 400:03:46
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181 CNN in Python step 500:02:34
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182 CNN in Python step 600:05:17
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183 CNN in Python step 700:04:01
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184 CNN in Python step 800:03:06
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185 CNN in Python step 900:12:06
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186 CNN in Python step 1000:02:04
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187 CNN in R00:01:16
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188 DR Introduction00:03:20
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189 PCA Intro00:05:07
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190 PCA in Python step 100:07:05
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191 PCA in Python step 200:09:51
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192 PCA in Python step 300:05:44
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193 PCA in R step 100:07:50
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194 PCA in R step 200:07:31
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195 PCA in R step 300:10:05
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196 LDA Intro00:05:03
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197 LDA in Python00:08:30
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198 LDA in R00:13:34
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199 KernelPCA in Python00:11:33
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200 KernelPCA in R00:17:49
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201 Model Selection XGBoost Intro00:05:11
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202 K cross validation in Python00:12:58
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203 K cross validation in R00:24:56
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204 Grid Search in Python step 100:07:34
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205 Grid Search in Python step 200:07:12
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206 Grid Search in R00:10:06
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207 XGBoost in Python step 100:05:42
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208 XGBoost in Python step 200:07:23
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209 XGBoost in R00:10:21
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210 The End00:02:20
معلومات الدورة
- المستوى: كل المستويات
- عدد المواد: 210
- اللغة: عربي
- عدد الطلاب: 16
- آخر تحديث: 24/01/2022
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