FreeAllCourse Com Udemy - The Data Science Course 2020 Complete Data Science Bootcamp

1. 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.mp449.03MB
2. 01 Part 1 Introduction/002 What Does the Course Cover.mp462.25MB
3. 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.mp481.41MB
4. 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.mp453.55MB
5. 02 The Field of Data Science - The Various Data Science Disciplines/006 Business Analytics Data Analytics and Data Science An Introduction.mp464.51MB
6. 02 The Field of Data Science - The Various Data Science Disciplines/007 Continuing with BI ML and AI.mp4108.98MB
7. 02 The Field of Data Science - The Various Data Science Disciplines/008 A Breakdown of our Data Science Infographic.mp467.74MB
8. 03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data Big Data BI Traditional Data Science and ML.mp4126.87MB
9. 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.mp481.18MB
10. 05 The Field of Data Science - Popular Data Science Techniques/011 Techniques for Working with Traditional Data.mp4138.3MB
11. 05 The Field of Data Science - Popular Data Science Techniques/012 Real Life Examples of Traditional Data.mp429.93MB
12. 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.mp475.5MB
13. 05 The Field of Data Science - Popular Data Science Techniques/014 Real Life Examples of Big Data.mp422.03MB
14. 05 The Field of Data Science - Popular Data Science Techniques/015 Business Intelligence (BI) Techniques.mp489.94MB
15. 05 The Field of Data Science - Popular Data Science Techniques/016 Real Life Examples of Business Intelligence (BI).mp429.54MB
16. 05 The Field of Data Science - Popular Data Science Techniques/017 Techniques for Working with Traditional Methods.mp4111.65MB
17. 05 The Field of Data Science - Popular Data Science Techniques/018 Real Life Examples of Traditional Methods.mp442.78MB
18. 05 The Field of Data Science - Popular Data Science Techniques/019 Machine Learning (ML) Techniques.mp499.32MB
19. 05 The Field of Data Science - Popular Data Science Techniques/020 Types of Machine Learning.mp4125.14MB
20. 05 The Field of Data Science - Popular Data Science Techniques/021 Real Life Examples of Machine Learning (ML).mp436.81MB
21. 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.mp4103.51MB
22. 07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.mp454.38MB
23. 08 The Field of Data Science - Debunking Common Misconceptions/024 Debunking Common Misconceptions.mp472.85MB
24. 09 Part 2 Probability/025 The Basic Probability Formula.mp485.91MB
25. 09 Part 2 Probability/026 Computing Expected Values.mp475.68MB
26. 09 Part 2 Probability/027 Frequency.mp461.73MB
27. 09 Part 2 Probability/028 Events and Their Complements.mp459.15MB
28. 10 Probability - Combinatorics/029 Fundamentals of Combinatorics.mp416.21MB
29. 10 Probability - Combinatorics/030 Permutations and How to Use Them.mp442.72MB
30. 10 Probability - Combinatorics/031 Simple Operations with Factorials.mp436.11MB
31. 10 Probability - Combinatorics/032 Solving Variations with Repetition.mp434MB
32. 10 Probability - Combinatorics/033 Solving Variations without Repetition.mp443.14MB
33. 10 Probability - Combinatorics/034 Solving Combinations.mp457.34MB
34. 10 Probability - Combinatorics/035 Symmetry of Combinations.mp440.3MB
35. 10 Probability - Combinatorics/036 Solving Combinations with Separate Sample Spaces.mp433.15MB
36. 10 Probability - Combinatorics/037 Combinatorics in Real-Life The Lottery.mp441.29MB
37. 10 Probability - Combinatorics/038 A Recap of Combinatorics.mp438.49MB
38. 10 Probability - Combinatorics/039 A Practical Example of Combinatorics.mp4134.31MB
39. 11 Probability - Bayesian Inference/040 Sets and Events.mp453.46MB
40. 11 Probability - Bayesian Inference/041 Ways Sets Can Interact.mp447.42MB
41. 11 Probability - Bayesian Inference/042 Intersection of Sets.mp426.96MB
42. 11 Probability - Bayesian Inference/043 Union of Sets.mp457.19MB
43. 11 Probability - Bayesian Inference/044 Mutually Exclusive Sets.mp425.39MB
44. 11 Probability - Bayesian Inference/045 Dependence and Independence of Sets.mp434.78MB
45. 11 Probability - Bayesian Inference/046 The Conditional Probability Formula.mp445.86MB
46. 11 Probability - Bayesian Inference/047 The Law of Total Probability.mp434.93MB
47. 11 Probability - Bayesian Inference/048 The Additive Rule.mp426.97MB
48. 11 Probability - Bayesian Inference/049 The Multiplication Law.mp449.02MB
49. 11 Probability - Bayesian Inference/050 Bayes Law.mp449.93MB
50. 11 Probability - Bayesian Inference/051 A Practical Example of Bayesian Inference.mp4145.12MB
51. 12 Probability - Distributions/052 Fundamentals of Probability Distributions.mp473.4MB
52. 12 Probability - Distributions/053 Types of Probability Distributions.mp471.06MB
53. 12 Probability - Distributions/054 Characteristics of Discrete Distributions.mp422.7MB
54. 12 Probability - Distributions/055 Discrete Distributions The Uniform Distribution.mp424.39MB
55. 12 Probability - Distributions/056 Discrete Distributions The Bernoulli Distribution.mp434.13MB
56. 12 Probability - Distributions/057 Discrete Distributions The Binomial Distribution.mp468.83MB
57. 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.mp455.75MB
58. 12 Probability - Distributions/059 Characteristics of Continuous Distributions.mp484.12MB
59. 12 Probability - Distributions/060 Continuous Distributions The Normal Distribution.mp448.24MB
60. 12 Probability - Distributions/061 Continuous Distributions The Standard Normal Distribution.mp447.9MB
61. 12 Probability - Distributions/062 Continuous Distributions The Students T Distribution.mp427.18MB
62. 12 Probability - Distributions/063 Continuous Distributions The Chi-Squared Distribution.mp426.34MB
63. 12 Probability - Distributions/064 Continuous Distributions The Exponential Distribution.mp440.23MB
64. 12 Probability - Distributions/065 Continuous Distributions The Logistic Distribution.mp447.05MB
65. 12 Probability - Distributions/066 A Practical Example of Probability Distributions.mp4157.82MB
66. 13 Probability - Probability in Other Fields/067 Probability in Finance.mp499.06MB
67. 13 Probability - Probability in Other Fields/068 Probability in Statistics.mp477.28MB
68. 13 Probability - Probability in Other Fields/069 Probability in Data Science.mp463.49MB
69. 14 Part 3 Statistics/070 Population and Sample.mp458.11MB
70. 15 Statistics - Descriptive Statistics/071 Types of Data.mp472.52MB
71. 15 Statistics - Descriptive Statistics/072 Levels of Measurement.mp454.38MB
72. 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.mp436.64MB
73. 15 Statistics - Descriptive Statistics/075 Numerical Variables - Frequency Distribution Table.mp425.85MB
74. 15 Statistics - Descriptive Statistics/077 The Histogram.mp413.78MB
75. 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.mp439.8MB
76. 15 Statistics - Descriptive Statistics/081 Mean median and mode.mp437.12MB
77. 15 Statistics - Descriptive Statistics/083 Skewness.mp419.4MB
78. 15 Statistics - Descriptive Statistics/085 Variance.mp450.95MB
79. 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.mp445.12MB
80. 15 Statistics - Descriptive Statistics/089 Covariance.mp427.48MB
81. 15 Statistics - Descriptive Statistics/091 Correlation Coefficient.mp429.38MB
82. 16 Statistics - Practical Example Descriptive Statistics/093 Practical Example Descriptive Statistics.mp4160.46MB
83. 17 Statistics - Inferential Statistics Fundamentals/095 Introduction.mp415.5MB
84. 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.mp461.59MB
85. 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.mp449.85MB
86. 17 Statistics - Inferential Statistics Fundamentals/098 The Standard Normal Distribution.mp422.5MB
87. 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.mp462.88MB
88. 17 Statistics - Inferential Statistics Fundamentals/101 Standard error.mp422.77MB
89. 17 Statistics - Inferential Statistics Fundamentals/102 Estimators and Estimates.mp447.83MB
90. 18 Statistics - Inferential Statistics Confidence Intervals/103 What are Confidence Intervals.mp449.98MB
91. 18 Statistics - Inferential Statistics Confidence Intervals/104 Confidence Intervals Population Variance Known Z-score.mp478.2MB
92. 18 Statistics - Inferential Statistics Confidence Intervals/106 Confidence Interval Clarifications.mp457.03MB
93. 18 Statistics - Inferential Statistics Confidence Intervals/107 Students T Distribution.mp435.43MB
94. 18 Statistics - Inferential Statistics Confidence Intervals/108 Confidence Intervals Population Variance Unknown T-score.mp432.2MB
95. 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.mp447.23MB
96. 18 Statistics - Inferential Statistics Confidence Intervals/111 Confidence intervals. Two means. Dependent samples.mp470.47MB
97. 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).mp428.75MB
98. 18 Statistics - Inferential Statistics Confidence Intervals/115 Confidence intervals. Two means. Independent Samples (Part 2).mp426.82MB
99. 18 Statistics - Inferential Statistics Confidence Intervals/117 Confidence intervals. Two means. Independent Samples (Part 3).mp419.93MB
100. 19 Statistics - Practical Example Inferential Statistics/118 Practical Example Inferential Statistics.mp4102.66MB
101. 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.mp492.04MB
102. 20 Statistics - Hypothesis Testing/122 Rejection Region and Significance Level.mp482.61MB
103. 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.mp443.93MB
104. 20 Statistics - Hypothesis Testing/124 Test for the Mean. Population Variance Known.mp454.22MB
105. 20 Statistics - Hypothesis Testing/126 p-value.mp455.87MB
106. 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.mp440.24MB
107. 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.mp450.37MB
108. 20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).mp433.94MB
109. 20 Statistics - Hypothesis Testing/133 Test for the mean. Independent Samples (Part 2).mp436.39MB
110. 21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.mp469.48MB
111. 22 Part 4 Introduction to Python/137 Introduction to Programming.mp458.54MB
112. 22 Part 4 Introduction to Python/138 Why Python.mp475.07MB
113. 22 Part 4 Introduction to Python/139 Why Jupyter.mp444.31MB
114. 22 Part 4 Introduction to Python/140 Installing Python and Jupyter.mp450.99MB
115. 22 Part 4 Introduction to Python/141 Understanding Jupyters Interface - the Notebook Dashboard.mp413.79MB
116. 22 Part 4 Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.mp430.58MB
117. 22 Part 4 Introduction to Python/143 Python 2 vs Python 3.mp411.27MB
118. 23 Python - Variables and Data Types/144 Variables.mp425.3MB
119. 23 Python - Variables and Data Types/145 Numbers and Boolean Values in Python.mp417.06MB
120. 23 Python - Variables and Data Types/146 Python Strings.mp450.64MB
121. 24 Python - Basic Python Syntax/147 Using Arithmetic Operators in Python.mp418.92MB
122. 24 Python - Basic Python Syntax/148 The Double Equality Sign.mp45.99MB
123. 24 Python - Basic Python Syntax/149 How to Reassign Values.mp44MB
124. 24 Python - Basic Python Syntax/150 Add Comments.mp411.26MB
125. 24 Python - Basic Python Syntax/151 Understanding Line Continuation.mp42.35MB
126. 24 Python - Basic Python Syntax/152 Indexing Elements.mp45.93MB
127. 24 Python - Basic Python Syntax/153 Structuring with Indentation.mp413.14MB
128. 25 Python - Other Python Operators/154 Comparison Operators.mp410.17MB
129. 25 Python - Other Python Operators/155 Logical and Identity Operators.mp430.05MB
130. 26 Python - Conditional Statements/156 The IF Statement.mp423.23MB
131. 26 Python - Conditional Statements/157 The ELSE Statement.mp423.28MB
132. 26 Python - Conditional Statements/158 The ELIF Statement.mp453.33MB
133. 26 Python - Conditional Statements/159 A Note on Boolean Values.mp419.99MB
134. 27 Python - Python Functions/160 Defining a Function in Python.mp414.75MB
135. 27 Python - Python Functions/161 How to Create a Function with a Parameter.mp438.1MB
136. 27 Python - Python Functions/162 Defining a Function in Python - Part II.mp425.24MB
137. 27 Python - Python Functions/163 How to Use a Function within a Function.mp48.13MB
138. 27 Python - Python Functions/164 Conditional Statements and Functions.mp415.68MB
139. 27 Python - Python Functions/165 Functions Containing a Few Arguments.mp414.71MB
140. 27 Python - Python Functions/166 Built-in Functions in Python.mp422.01MB
141. 28 Python - Sequences/167 Lists.mp437.79MB
142. 28 Python - Sequences/168 Using Methods.mp437.59MB
143. 28 Python - Sequences/169 List Slicing.mp430.76MB
144. 28 Python - Sequences/170 Tuples.mp429.49MB
145. 28 Python - Sequences/171 Dictionaries.mp441.68MB
146. 29 Python - Iterations/172 For Loops.mp423.59MB
147. 29 Python - Iterations/173 While Loops and Incrementing.mp428.43MB
148. 29 Python - Iterations/174 Lists with the range() Function.mp425.79MB
149. 29 Python - Iterations/175 Conditional Statements and Loops.mp427.76MB
150. 29 Python - Iterations/176 Conditional Statements Functions and Loops.mp49.48MB
151. 29 Python - Iterations/177 How to Iterate over Dictionaries.mp429.65MB
152. 30 Python - Advanced Python Tools/178 Object Oriented Programming.mp433.59MB
153. 30 Python - Advanced Python Tools/179 Modules and Packages.mp48.5MB
154. 30 Python - Advanced Python Tools/180 What is the Standard Library.mp418.03MB
155. 30 Python - Advanced Python Tools/181 Importing Modules in Python.mp419.93MB
156. 31 Part 5 Advanced Statistical Methods in Python/182 Introduction to Regression Analysis.mp417.32MB
157. 32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.mp457.37MB
158. 32 Advanced Statistical Methods - Linear Regression with StatsModels/184 Correlation vs Regression.mp414.73MB
159. 32 Advanced Statistical Methods - Linear Regression with StatsModels/185 Geometrical Representation of the Linear Regression Model.mp45.12MB
160. 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.mp440.58MB
161. 32 Advanced Statistical Methods - Linear Regression with StatsModels/187 First Regression in Python.mp444.56MB
162. 32 Advanced Statistical Methods - Linear Regression with StatsModels/189 Using Seaborn for Graphs.mp412.24MB
163. 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.mp444.64MB
164. 32 Advanced Statistical Methods - Linear Regression with StatsModels/191 Decomposition of Variability.mp449.66MB
165. 32 Advanced Statistical Methods - Linear Regression with StatsModels/192 What is the OLS.mp428.31MB
166. 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.mp441.03MB
167. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/194 Multiple Linear Regression.mp421.52MB
168. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Adjusted R-Squared.mp454.83MB
169. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/197 Test for Significance of the Model (F-Test).mp416.42MB
170. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/198 OLS Assumptions.mp421.85MB
171. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/199 A1 Linearity.mp412.6MB
172. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/200 A2 No Endogeneity.mp435.67MB
173. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.mp442.7MB
174. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.mp431.51MB
175. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 A5 No Multicollinearity.mp428.7MB
176. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/204 Dealing with Categorical Data - Dummy Variables.mp455.66MB
177. 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/206 Making Predictions with the Linear Regression.mp424.69MB
178. 34 Advanced Statistical Methods - Linear Regression with sklearn/207 What is sklearn and How is it Different from Other Packages.mp427.25MB
179. 34 Advanced Statistical Methods - Linear Regression with sklearn/208 How are Going to Approach this Section.mp419.41MB
180. 34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn.mp434.77MB
181. 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp432MB
182. 34 Advanced Statistical Methods - Linear Regression with sklearn/213 Multiple Linear Regression with sklearn.mp420.07MB
183. 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.mp430.88MB
184. 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).mp429.51MB
185. 34 Advanced Statistical Methods - Linear Regression with sklearn/218 Creating a Summary Table with P-values.mp412.3MB
186. 34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Scaling (Standardization).mp439.08MB
187. 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Feature Selection through Standardization of Weights.mp434.89MB
188. 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.mp425.96MB
189. 34 Advanced Statistical Methods - Linear Regression with sklearn/224 Underfitting and Overfitting.mp416.95MB
190. 34 Advanced Statistical Methods - Linear Regression with sklearn/225 Train - Test Split Explained.mp449.17MB
191. 35 Advanced Statistical Methods - Practical Example Linear Regression/226 Practical Example Linear Regression (Part 1).mp497.08MB
192. 35 Advanced Statistical Methods - Practical Example Linear Regression/227 Practical Example Linear Regression (Part 2).mp446MB
193. 35 Advanced Statistical Methods - Practical Example Linear Regression/229 Practical Example Linear Regression (Part 3).mp423.69MB
194. 35 Advanced Statistical Methods - Practical Example Linear Regression/231 Practical Example Linear Regression (Part 4).mp456.04MB
195. 35 Advanced Statistical Methods - Practical Example Linear Regression/233 Practical Example Linear Regression (Part 5).mp457.88MB
196. 36 Advanced Statistical Methods - Logistic Regression/235 Introduction to Logistic Regression.mp427.06MB
197. 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.mp434.69MB
198. 36 Advanced Statistical Methods - Logistic Regression/237 Logistic vs Logit Function.mp486.49MB
199. 36 Advanced Statistical Methods - Logistic Regression/238 Building a Logistic Regression.mp417.1MB
200. 36 Advanced Statistical Methods - Logistic Regression/240 An Invaluable Coding Tip.mp423.05MB
201. 36 Advanced Statistical Methods - Logistic Regression/241 Understanding Logistic Regression Tables.mp430.55MB
202. 36 Advanced Statistical Methods - Logistic Regression/243 What do the Odds Actually Mean.mp432.28MB
203. 36 Advanced Statistical Methods - Logistic Regression/244 Binary Predictors in a Logistic Regression.mp438.43MB
204. 36 Advanced Statistical Methods - Logistic Regression/246 Calculating the Accuracy of the Model.mp432.85MB
205. 36 Advanced Statistical Methods - Logistic Regression/248 Underfitting and Overfitting.mp422.29MB
206. 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.mp432.27MB
207. 37 Advanced Statistical Methods - Cluster Analysis/251 Introduction to Cluster Analysis.mp453.42MB
208. 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.mp471.53MB
209. 37 Advanced Statistical Methods - Cluster Analysis/253 Difference between Classification and Clustering.mp436.15MB
210. 37 Advanced Statistical Methods - Cluster Analysis/254 Math Prerequisites.mp414.55MB
211. 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.mp427.28MB
212. 38 Advanced Statistical Methods - K-Means Clustering/256 A Simple Example of Clustering.mp451.82MB
213. 38 Advanced Statistical Methods - K-Means Clustering/258 Clustering Categorical Data.mp421.23MB
214. 38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.mp444.13MB
215. 38 Advanced Statistical Methods - K-Means Clustering/262 Pros and Cons of K-Means Clustering.mp437.7MB
216. 38 Advanced Statistical Methods - K-Means Clustering/263 To Standardize or not to Standardize.mp430.1MB
217. 38 Advanced Statistical Methods - K-Means Clustering/264 Relationship between Clustering and Regression.mp49.93MB
218. 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).mp443.01MB
219. 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).mp456.11MB
220. 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.mp474.45MB
221. 39 Advanced Statistical Methods - Other Types of Clustering/270 Types of Clustering.mp444.57MB
222. 39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.mp429.06MB
223. 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.mp429.62MB
224. 40 Part 6 Mathematics/273 What is a Matrix.mp433.59MB
225. 40 Part 6 Mathematics/274 Scalars and Vectors.mp433.85MB
226. 40 Part 6 Mathematics/275 Linear Algebra and Geometry.mp449.79MB
227. 40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.mp426.67MB
228. 40 Part 6 Mathematics/277 What is a Tensor.mp422.52MB
229. 40 Part 6 Mathematics/278 Addition and Subtraction of Matrices.mp432.61MB
230. 40 Part 6 Mathematics/279 Errors when Adding Matrices.mp411.17MB
231. 40 Part 6 Mathematics/280 Transpose of a Matrix.mp438.07MB
232. 40 Part 6 Mathematics/281 Dot Product.mp423.99MB
233. 40 Part 6 Mathematics/282 Dot Product of Matrices.mp449.43MB
234. 40 Part 6 Mathematics/283 Why is Linear Algebra Useful.mp4144.33MB
235. 41 Part 7 Deep Learning/284 What to Expect from this Part.mp431.1MB
236. 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.mp442.92MB
237. 42 Deep Learning - Introduction to Neural Networks/286 Training the Model.mp428.71MB
238. 42 Deep Learning - Introduction to Neural Networks/287 Types of Machine Learning.mp445.1MB
239. 42 Deep Learning - Introduction to Neural Networks/288 The Linear Model (Linear Algebraic Version).mp428.44MB
240. 42 Deep Learning - Introduction to Neural Networks/289 The Linear Model with Multiple Inputs.mp425.11MB
241. 42 Deep Learning - Introduction to Neural Networks/290 The Linear model with Multiple Inputs and Multiple Outputs.mp438.31MB
242. 42 Deep Learning - Introduction to Neural Networks/291 Graphical Representation of Simple Neural Networks.mp422.64MB
243. 42 Deep Learning - Introduction to Neural Networks/292 What is the Objective Function.mp417.91MB
244. 42 Deep Learning - Introduction to Neural Networks/293 Common Objective Functions L2-norm Loss.mp423.27MB
245. 42 Deep Learning - Introduction to Neural Networks/294 Common Objective Functions Cross-Entropy Loss.mp437.24MB
246. 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.mp455.62MB
247. 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.mp439.42MB
248. 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/297 Basic NN Example (Part 1).mp420.59MB
249. 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).mp434.94MB
250. 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/299 Basic NN Example (Part 3).mp424.4MB
251. 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/300 Basic NN Example (Part 4).mp461.13MB
252. 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.mp438.76MB
253. 44 Deep Learning - TensorFlow 2.0 Introduction/303 TensorFlow Outline and Comparison with Other Libraries.mp433.51MB
254. 44 Deep Learning - TensorFlow 2.0 Introduction/304 TensorFlow 1 vs TensorFlow 2.mp421.99MB
255. 44 Deep Learning - TensorFlow 2.0 Introduction/305 A Note on TensorFlow 2 Syntax.mp46.75MB
256. 44 Deep Learning - TensorFlow 2.0 Introduction/306 Types of File Formats Supporting TensorFlow.mp416.4MB
257. 44 Deep Learning - TensorFlow 2.0 Introduction/307 Outlining the Model with TensorFlow 2.mp434.69MB
258. 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.mp430.27MB
259. 44 Deep Learning - TensorFlow 2.0 Introduction/309 Customizing a TensorFlow 2 Model.mp422.91MB
260. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/311 What is a Layer.mp412.5MB
261. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/312 What is a Deep Net.mp429.53MB
262. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.mp459.36MB
263. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/314 Non-Linearities and their Purpose.mp427.68MB
264. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/315 Activation Functions.mp425.09MB
265. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/316 Activation Functions Softmax Activation.mp425.92MB
266. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/317 Backpropagation.mp434.95MB
267. 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/318 Backpropagation Picture.mp419.5MB
268. 46 Deep Learning - Overfitting/320 What is Overfitting.mp431.08MB
269. 46 Deep Learning - Overfitting/321 Underfitting and Overfitting for Classification.mp425.07MB
270. 46 Deep Learning - Overfitting/322 What is Validation.mp432.71MB
271. 46 Deep Learning - Overfitting/323 Training Validation and Test Datasets.mp425.19MB
272. 46 Deep Learning - Overfitting/324 N-Fold Cross Validation.mp420.7MB
273. 46 Deep Learning - Overfitting/325 Early Stopping or When to Stop Training.mp424.17MB
274. 47 Deep Learning - Initialization/326 What is Initialization.mp421.76MB
275. 47 Deep Learning - Initialization/327 Types of Simple Initializations.mp414.31MB
276. 47 Deep Learning - Initialization/328 State-of-the-Art Method - (Xavier) Glorot Initialization.mp417.14MB
277. 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.mp428.68MB
278. 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/330 Problems with Gradient Descent.mp411.01MB
279. 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/331 Momentum.mp416.43MB
280. 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/332 Learning Rate Schedules or How to Choose the Optimal Learning Rate.mp429.08MB
281. 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/333 Learning Rate Schedules Visualized.mp49.11MB
282. 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/334 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp426.35MB
283. 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/335 Adam (Adaptive Moment Estimation).mp422.35MB
284. 49 Deep Learning - Preprocessing/336 Preprocessing Introduction.mp427.78MB
285. 49 Deep Learning - Preprocessing/337 Types of Basic Preprocessing.mp411.84MB
286. 49 Deep Learning - Preprocessing/338 Standardization.mp450.98MB
287. 49 Deep Learning - Preprocessing/339 Preprocessing Categorical Data.mp418.6MB
288. 49 Deep Learning - Preprocessing/340 Binary and One-Hot Encoding.mp428.94MB
289. 50 Deep Learning - Classifying on the MNIST Dataset/341 MNIST The Dataset.mp413.38MB
290. 50 Deep Learning - Classifying on the MNIST Dataset/342 MNIST How to Tackle the MNIST.mp418.66MB
291. 50 Deep Learning - Classifying on the MNIST Dataset/343 MNIST Importing the Relevant Packages and Loading the Data.mp416.32MB
292. 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.mp429.04MB
293. 50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.mp441.52MB
294. 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.mp428.23MB
295. 50 Deep Learning - Classifying on the MNIST Dataset/349 MNIST Select the Loss and the Optimizer.mp413.9MB
296. 50 Deep Learning - Classifying on the MNIST Dataset/350 MNIST Learning.mp440.96MB
297. 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.mp429.52MB
298. 51 Deep Learning - Business Case Example/353 Business Case Exploring the Dataset and Identifying Predictors.mp466.27MB
299. 51 Deep Learning - Business Case Example/354 Business Case Outlining the Solution.mp47.3MB
300. 51 Deep Learning - Business Case Example/355 Business Case Balancing the Dataset.mp430.43MB
301. 51 Deep Learning - Business Case Example/356 Business Case Preprocessing the Data.mp484.33MB
302. 51 Deep Learning - Business Case Example/358 Business Case Load the Preprocessed Data.mp417.57MB
303. 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.mp431.18MB
304. 51 Deep Learning - Business Case Example/361 Business Case Setting an Early Stopping Mechanism.mp449.81MB
305. 51 Deep Learning - Business Case Example/363 Business Case Testing the Model.mp410.79MB
306. 52 Deep Learning - Conclusion/365 Summary on What Youve Learned.mp439.75MB
307. 52 Deep Learning - Conclusion/366 Whats Further out there in terms of Machine Learning.mp420.12MB
308. 52 Deep Learning - Conclusion/368 An overview of CNNs.mp458.79MB
309. 52 Deep Learning - Conclusion/369 An Overview of RNNs.mp425.26MB
310. 52 Deep Learning - Conclusion/370 An Overview of non-NN Approaches.mp444.77MB
311. 53 Appendix Deep Learning - TensorFlow 1 Introduction/372 How to Install TensorFlow 1.mp411.35MB
312. 53 Appendix Deep Learning - TensorFlow 1 Introduction/374 TensorFlow Intro.mp447.69MB
313. 53 Appendix Deep Learning - TensorFlow 1 Introduction/375 Actual Introduction to TensorFlow.mp417.41MB
314. 53 Appendix Deep Learning - TensorFlow 1 Introduction/376 Types of File Formats supporting Tensors.mp420.34MB
315. 53 Appendix Deep Learning - TensorFlow 1 Introduction/377 Basic NN Example with TF Inputs Outputs Targets Weights Biases.mp438.49MB
316. 53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.mp432.51MB
317. 53 Appendix Deep Learning - TensorFlow 1 Introduction/379 Basic NN Example with TF Model Output.mp437.39MB
318. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/381 MNIST What is the MNIST Dataset.mp417.82MB
319. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/382 MNIST How to Tackle the MNIST.mp422.58MB
320. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/383 MNIST Relevant Packages.mp418.9MB
321. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.mp456.38MB
322. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/385 MNIST Loss and Optimization Algorithm.mp425.86MB
323. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/386 Calculating the Accuracy of the Model.mp443.9MB
324. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/387 MNIST Batching and Early Stopping.mp412.85MB
325. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/388 MNIST Learning.mp446.68MB
326. 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.mp462.77MB
327. 55 Appendix Deep Learning - TensorFlow 1 Business Case/392 Business Case Getting Acquainted with the Dataset.mp487.65MB
328. 55 Appendix Deep Learning - TensorFlow 1 Business Case/393 Business Case Outlining the Solution.mp412.21MB
329. 55 Appendix Deep Learning - TensorFlow 1 Business Case/394 The Importance of Working with a Balanced Dataset.mp439.41MB
330. 55 Appendix Deep Learning - TensorFlow 1 Business Case/395 Business Case Preprocessing.mp4103.41MB
331. 55 Appendix Deep Learning - TensorFlow 1 Business Case/397 Creating a Data Provider.mp476.34MB
332. 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.mp453.12MB
333. 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.mp441.52MB
334. 55 Appendix Deep Learning - TensorFlow 1 Business Case/400 Business Case Interpretation.mp425.74MB
335. 55 Appendix Deep Learning - TensorFlow 1 Business Case/401 Business Case Testing the Model.mp411.2MB
336. 55 Appendix Deep Learning - TensorFlow 1 Business Case/402 Business Case A Comment on the Homework.mp436.38MB
337. 56 Software Integration/404 What are Data Servers Clients Requests and Responses.mp469.03MB
338. 56 Software Integration/405 What are Data Connectivity APIs and Endpoints.mp4104.08MB
339. 56 Software Integration/406 Taking a Closer Look at APIs.mp4115.59MB
340. 56 Software Integration/407 Communication between Software Products through Text Files.mp460.34MB
341. 56 Software Integration/408 Software Integration - Explained.mp463.69MB
342. 57 Case Study - Whats Next in the Course/409 Game Plan for this Python SQL and Tableau Business Exercise.mp452.3MB
343. 57 Case Study - Whats Next in the Course/410 The Business Task.mp439.15MB
344. 57 Case Study - Whats Next in the Course/411 Introducing the Data Set.mp440.86MB
345. 58 Case Study - Preprocessing the Absenteeism_data/413 Importing the Absenteeism Data in Python.mp423.15MB
346. 58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.mp461.9MB
347. 58 Case Study - Preprocessing the Absenteeism_data/415 Introduction to Terms with Multiple Meanings.mp427.85MB
348. 58 Case Study - Preprocessing the Absenteeism_data/417 Using a Statistical Approach towards the Solution to the Exercise.mp420.18MB
349. 58 Case Study - Preprocessing the Absenteeism_data/418 Dropping a Column from a DataFrame in Python.mp461.76MB
350. 58 Case Study - Preprocessing the Absenteeism_data/421 Analyzing the Reasons for Absence.mp440.57MB
351. 58 Case Study - Preprocessing the Absenteeism_data/422 Obtaining Dummies from a Single Feature.mp481.11MB
352. 58 Case Study - Preprocessing the Absenteeism_data/426 More on Dummy Variables A Statistical Perspective.mp413.74MB
353. 58 Case Study - Preprocessing the Absenteeism_data/427 Classifying the Various Reasons for Absence.mp474.6MB
354. 58 Case Study - Preprocessing the Absenteeism_data/428 Using .concat() in Python.mp438.73MB
355. 58 Case Study - Preprocessing the Absenteeism_data/431 Reordering Columns in a Pandas DataFrame in Python.mp414.01MB
356. 58 Case Study - Preprocessing the Absenteeism_data/434 Creating Checkpoints while Coding in Jupyter.mp425.67MB
357. 58 Case Study - Preprocessing the Absenteeism_data/437 Analyzing the Dates from the Initial Data Set.mp457.28MB
358. 58 Case Study - Preprocessing the Absenteeism_data/438 Extracting the Month Value from the Date Column.mp447.79MB
359. 58 Case Study - Preprocessing the Absenteeism_data/439 Extracting the Day of the Week from the Date Column.mp427.96MB
360. 58 Case Study - Preprocessing the Absenteeism_data/441 Analyzing Several Straightforward Columns for this Exercise.mp429.51MB
361. 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.mp439.59MB
362. 58 Case Study - Preprocessing the Absenteeism_data/443 Final Remarks of this Section.mp421.63MB
363. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/445 Exploring the Problem with a Machine Learning Mindset.mp427.54MB
364. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/446 Creating the Targets for the Logistic Regression.mp445.79MB
365. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/447 Selecting the Inputs for the Logistic Regression.mp416.75MB
366. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/448 Standardizing the Data.mp420.59MB
367. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/449 Splitting the Data for Training and Testing.mp452.76MB
368. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/450 Fitting the Model and Assessing its Accuracy.mp441.62MB
369. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.mp438.87MB
370. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/452 Interpreting the Coefficients for Our Problem.mp452.37MB
371. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/453 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.19MB
372. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/454 Interpreting the Coefficients of the Logistic Regression.mp440.4MB
373. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/455 Backward Elimination or How to Simplify Your Model.mp439.56MB
374. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.mp449.06MB
375. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.mp437.45MB
376. 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.mp444.48MB
377. 60 Case Study - Loading the absenteeism_module/462 Deploying the absenteeism_module - Part I.mp425.48MB
378. 60 Case Study - Loading the absenteeism_module/463 Deploying the absenteeism_module - Part II.mp454.25MB
379. 61 Case Study - Analyzing the Predicted Outputs in Tableau/466 Analyzing Age vs Probability in Tableau.mp456.55MB
380. 61 Case Study - Analyzing the Predicted Outputs in Tableau/468 Analyzing Reasons vs Probability in Tableau.mp459.33MB
381. 61 Case Study - Analyzing the Predicted Outputs in Tableau/470 Analyzing Transportation Expense vs Probability in Tableau.mp440.63MB