FreeCourseSite com Udemy - The Data Science Course 2022 Complete Data Science Bootcamp

1. 01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course.mp413.08MB
2. 01 - Part 1_ Introduction/002 What Does the Course Cover.mp449.69MB
3. 02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords_ Why are there so Many_.mp454.72MB
4. 02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp48.01MB
5. 02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp449.96MB
6. 02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp435.94MB
7. 02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp433.95MB
8. 03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp421.73MB
9. 04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp412.41MB
10. 05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4105.52MB
11. 05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp413.92MB
12. 05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp460.48MB
13. 05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data.mp44.21MB
14. 05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp451.34MB
15. 05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp419.35MB
16. 05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp474.75MB
17. 05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp421.17MB
18. 05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp447.78MB
19. 05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp461.78MB
20. 05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp422.44MB
21. 06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.mp419.54MB
22. 07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp49.48MB
23. 08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp416.43MB
24. 09 - Part 2_ Probability/001 The Basic Probability Formula.mp429.13MB
25. 09 - Part 2_ Probability/002 Computing Expected Values.mp429.24MB
26. 09 - Part 2_ Probability/003 Frequency.mp436.39MB
27. 09 - Part 2_ Probability/004 Events and Their Complements.mp411.4MB
28. 10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp43.21MB
29. 10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp413.97MB
30. 10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp413.98MB
31. 10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp413.75MB
32. 10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp414.76MB
33. 10 - Probability - Combinatorics/006 Solving Combinations.mp418.99MB
34. 10 - Probability - Combinatorics/007 Symmetry of Combinations.mp413.51MB
35. 10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp412.87MB
36. 10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery.mp416.16MB
37. 10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp412MB
38. 10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp442.24MB
39. 11 - Probability - Bayesian Inference/001 Sets and Events.mp417.44MB
40. 11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp419.02MB
41. 11 - Probability - Bayesian Inference/003 Intersection of Sets.mp48.78MB
42. 11 - Probability - Bayesian Inference/004 Union of Sets.mp419.47MB
43. 11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets.mp45.25MB
44. 11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp411.98MB
45. 11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp416.33MB
46. 11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp411.39MB
47. 11 - Probability - Bayesian Inference/009 The Additive Rule.mp410.89MB
48. 11 - Probability - Bayesian Inference/010 The Multiplication Law.mp419.8MB
49. 11 - Probability - Bayesian Inference/011 Bayes' Law.mp420.94MB
50. 11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4125.49MB
51. 12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp419.28MB
52. 12 - Probability - Distributions/002 Types of Probability Distributions.mp428.69MB
53. 12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp49.25MB
54. 12 - Probability - Distributions/004 Discrete Distributions_ The Uniform Distribution.mp410.08MB
55. 12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution.mp414.76MB
56. 12 - Probability - Distributions/006 Discrete Distributions_ The Binomial Distribution.mp424.94MB
57. 12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution.mp414.62MB
58. 12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp428.87MB
59. 12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution.mp419.67MB
60. 12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution.mp420.72MB
61. 12 - Probability - Distributions/011 Continuous Distributions_ The Students' T Distribution.mp45.44MB
62. 12 - Probability - Distributions/012 Continuous Distributions_ The Chi-Squared Distribution.mp410.95MB
63. 12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution.mp415.76MB
64. 12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution.mp415.95MB
65. 12 - Probability - Distributions/015 A Practical Example of Probability Distributions.mp4138.31MB
66. 13 - Probability - Probability in Other Fields/001 Probability in Finance.mp439.66MB
67. 13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp414.26MB
68. 13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp423.94MB
69. 14 - Part 3_ Statistics/001 Population and Sample.mp410.89MB
70. 15 - Statistics - Descriptive Statistics/001 Types of Data.mp442.47MB
71. 15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp431.44MB
72. 15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp436.65MB
73. 15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp412.8MB
74. 15 - Statistics - Descriptive Statistics/007 The Histogram.mp43.85MB
75. 15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp419.7MB
76. 15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp417.53MB
77. 15 - Statistics - Descriptive Statistics/013 Skewness.mp49.92MB
78. 15 - Statistics - Descriptive Statistics/015 Variance.mp420.21MB
79. 15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation.mp420.14MB
80. 15 - Statistics - Descriptive Statistics/019 Covariance.mp418.41MB
81. 15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp419.38MB
82. 16 - Statistics - Practical Example_ Descriptive Statistics/001 Practical Example_ Descriptive Statistics.mp437.17MB
83. 17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp42.93MB
84. 17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp416.9MB
85. 17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp416.16MB
86. 17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp48.62MB
87. 17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp422.86MB
88. 17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp413.33MB
89. 17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp416.13MB
90. 18 - Statistics - Inferential Statistics_ Confidence Intervals/001 What are Confidence Intervals_.mp428.38MB
91. 18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp452.21MB
92. 18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications.mp418.56MB
93. 18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution.mp413.33MB
94. 18 - Statistics - Inferential Statistics_ Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp411.58MB
95. 18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error.mp422.66MB
96. 18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp445.04MB
97. 18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp412MB
98. 18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp413.05MB
99. 18 - Statistics - Inferential Statistics_ Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp44.17MB
100. 19 - Statistics - Practical Example_ Inferential Statistics/001 Practical Example_ Inferential Statistics.mp422.1MB
101. 20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp480.83MB
102. 20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp438.2MB
103. 20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp418.17MB
104. 20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp436.96MB
105. 20 - Statistics - Hypothesis Testing/007 p-value.mp433.08MB
106. 20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp419.72MB
107. 20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp432.8MB
108. 20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).mp47.57MB
109. 20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp424.47MB
110. 21 - Statistics - Practical Example_ Hypothesis Testing/001 Practical Example_ Hypothesis Testing.mp416.3MB
111. 22 - Part 4_ Introduction to Python/001 Introduction to Programming.mp414.33MB
112. 22 - Part 4_ Introduction to Python/002 Why Python_.mp411.77MB
113. 22 - Part 4_ Introduction to Python/003 Why Jupyter_.mp47.96MB
114. 22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter.mp432.86MB
115. 22 - Part 4_ Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp44.39MB
116. 22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp415.38MB
117. 23 - Python - Variables and Data Types/001 Variables.mp48.93MB
118. 23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp44.61MB
119. 23 - Python - Variables and Data Types/003 Python Strings.mp419.74MB
120. 24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp47.28MB
121. 24 - Python - Basic Python Syntax/002 The Double Equality Sign.mp42.72MB
122. 24 - Python - Basic Python Syntax/003 How to Reassign Values.mp41.86MB
123. 24 - Python - Basic Python Syntax/004 Add Comments.mp42.41MB
124. 24 - Python - Basic Python Syntax/005 Understanding Line Continuation.mp41014.51KB
125. 24 - Python - Basic Python Syntax/006 Indexing Elements.mp42.36MB
126. 24 - Python - Basic Python Syntax/007 Structuring with Indentation.mp42.8MB
127. 25 - Python - Other Python Operators/001 Comparison Operators.mp43.12MB
128. 25 - Python - Other Python Operators/002 Logical and Identity Operators.mp419MB
129. 26 - Python - Conditional Statements/001 The IF Statement.mp45.33MB
130. 26 - Python - Conditional Statements/002 The ELSE Statement.mp45.25MB
131. 26 - Python - Conditional Statements/003 The ELIF Statement.mp414.25MB
132. 26 - Python - Conditional Statements/004 A Note on Boolean Values.mp43.26MB
133. 27 - Python - Python Functions/001 Defining a Function in Python.mp43.23MB
134. 27 - Python - Python Functions/002 How to Create a Function with a Parameter.mp48.29MB
135. 27 - Python - Python Functions/003 Defining a Function in Python - Part II.mp46.45MB
136. 27 - Python - Python Functions/004 How to Use a Function within a Function.mp43.25MB
137. 27 - Python - Python Functions/005 Conditional Statements and Functions.mp46.04MB
138. 27 - Python - Python Functions/006 Functions Containing a Few Arguments.mp42.24MB
139. 27 - Python - Python Functions/007 Built-in Functions in Python.mp48.5MB
140. 28 - Python - Sequences/001 Lists.mp420.5MB
141. 28 - Python - Sequences/002 Using Methods.mp423.42MB
142. 28 - Python - Sequences/003 List Slicing.mp419.17MB
143. 28 - Python - Sequences/004 Tuples.mp49.5MB
144. 28 - Python - Sequences/005 Dictionaries.mp424.91MB
145. 29 - Python - Iterations/001 For Loops.mp423.58MB
146. 29 - Python - Iterations/002 While Loops and Incrementing.mp420.2MB
147. 29 - Python - Iterations/003 Lists with the range() Function.mp414.5MB
148. 29 - Python - Iterations/004 Conditional Statements and Loops.mp421.94MB
149. 29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp42.91MB
150. 29 - Python - Iterations/006 How to Iterate over Dictionaries.mp416.46MB
151. 30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp48.42MB
152. 30 - Python - Advanced Python Tools/002 Modules and Packages.mp41.71MB
153. 30 - Python - Advanced Python Tools/003 What is the Standard Library_.mp44.87MB
154. 30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp48.53MB
155. 31 - Part 5_ Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp42.92MB
156. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp413.16MB
157. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp43.75MB
158. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/003 Geometrical Representation of the Linear Regression Model.mp41.75MB
159. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp423.7MB
160. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp429.63MB
161. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs.mp47.37MB
162. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table.mp428.72MB
163. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp48.62MB
164. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS_.mp422.44MB
165. 32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp410.79MB
166. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp45.54MB
167. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp434.22MB
168. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test).mp45.9MB
169. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions.mp45.12MB
170. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1_ Linearity.mp42.66MB
171. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2_ No Endogeneity.mp48.99MB
172. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity.mp427.39MB
173. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4_ No Autocorrelation.mp47.67MB
174. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5_ No Multicollinearity.mp47.36MB
175. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables.mp435.09MB
176. 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.mp416.36MB
177. 34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.mp46.24MB
178. 34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section_.mp44.03MB
179. 34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp431.65MB
180. 34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp428.88MB
181. 34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp49.81MB
182. 34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp416.92MB
183. 34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp415.68MB
184. 34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values.mp46.45MB
185. 34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization).mp420.37MB
186. 34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp427.16MB
187. 34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp418.34MB
188. 34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting.mp45.69MB
189. 34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp435.58MB
190. 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/001 Practical Example_ Linear Regression (Part 1).mp484.84MB
191. 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2).mp431.9MB
192. 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/004 Practical Example_ Linear Regression (Part 3).mp46.91MB
193. 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/006 Practical Example_ Linear Regression (Part 4).mp429.84MB
194. 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/008 Practical Example_ Linear Regression (Part 5).mp450.42MB
195. 36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp44.41MB
196. 36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp421.91MB
197. 36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp443.96MB
198. 36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp48.61MB
199. 36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp416.77MB
200. 36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp412.89MB
201. 36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp411.38MB
202. 36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp418.47MB
203. 36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp420.28MB
204. 36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp47.25MB
205. 36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp421.6MB
206. 37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp410.66MB
207. 37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp435.12MB
208. 37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp49.53MB
209. 37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp44.47MB
210. 38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp410.53MB
211. 38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp426.08MB
212. 38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data.mp410.35MB
213. 38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters.mp419.79MB
214. 38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp410.93MB
215. 38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp410.5MB
216. 38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression.mp42.42MB
217. 38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp421.16MB
218. 38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp434.08MB
219. 38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_.mp436.49MB
220. 39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp47.57MB
221. 39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp417.34MB
222. 39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.mp425.71MB
223. 40 - Part 6_ Mathematics/001 What is a Matrix_.mp411.7MB
224. 40 - Part 6_ Mathematics/002 Scalars and Vectors.mp48.39MB
225. 40 - Part 6_ Mathematics/003 Linear Algebra and Geometry.mp413.56MB
226. 40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp419.01MB
227. 40 - Part 6_ Mathematics/005 What is a Tensor_.mp411.61MB
228. 40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices.mp422.08MB
229. 40 - Part 6_ Mathematics/007 Errors when Adding Matrices.mp43.34MB
230. 40 - Part 6_ Mathematics/008 Transpose of a Matrix.mp420.49MB
231. 40 - Part 6_ Mathematics/009 Dot Product.mp411.36MB
232. 40 - Part 6_ Mathematics/010 Dot Product of Matrices.mp426.42MB
233. 40 - Part 6_ Mathematics/011 Why is Linear Algebra Useful_.mp486.18MB
234. 41 - Part 7_ Deep Learning/001 What to Expect from this Part_.mp47.56MB
235. 42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp410.37MB
236. 42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp47.57MB
237. 42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp49.81MB
238. 42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp47.87MB
239. 42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp47.77MB
240. 42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp416.23MB
241. 42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp46.35MB
242. 42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function_.mp46.03MB
243. 42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions_ L2-norm Loss.mp44.47MB
244. 42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions_ Cross-Entropy Loss.mp49.68MB
245. 42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm_ 1-Parameter Gradient Descent.mp422.7MB
246. 42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent.mp416.35MB
247. 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1).mp45.14MB
248. 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2).mp415.23MB
249. 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3).mp415.68MB
250. 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp430.06MB
251. 44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0.mp427.34MB
252. 44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp414.94MB
253. 44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2.mp414.95MB
254. 44 - Deep Learning - TensorFlow 2.0_ Introduction/004 A Note on TensorFlow 2 Syntax.mp42.34MB
255. 44 - Deep Learning - TensorFlow 2.0_ Introduction/005 Types of File Formats Supporting TensorFlow.mp47.25MB
256. 44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2.mp426.99MB
257. 44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp413.67MB
258. 44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model.mp416.78MB
259. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/001 What is a Layer_.mp43.47MB
260. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/002 What is a Deep Net_.mp411.06MB
261. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net.mp419.14MB
262. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp49.74MB
263. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/005 Activation Functions.mp48.53MB
264. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/006 Activation Functions_ Softmax Activation.mp48.42MB
265. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation.mp419.49MB
266. 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/008 Backpropagation Picture.mp47.68MB
267. 46 - Deep Learning - Overfitting/001 What is Overfitting_.mp410.5MB
268. 46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp413.53MB
269. 46 - Deep Learning - Overfitting/003 What is Validation_.mp48.14MB
270. 46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp47.74MB
271. 46 - Deep Learning - Overfitting/005 N-Fold Cross Validation.mp45.14MB
272. 46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp48.5MB
273. 47 - Deep Learning - Initialization/001 What is Initialization_.mp417.42MB
274. 47 - Deep Learning - Initialization/002 Types of Simple Initializations.mp45.73MB
275. 47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp44.18MB
276. 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp47.62MB
277. 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp43.51MB
278. 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum.mp45.01MB
279. 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp412.03MB
280. 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized.mp42.34MB
281. 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp48.24MB
282. 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp46.88MB
283. 49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp48.98MB
284. 49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing.mp42.4MB
285. 49 - Deep Learning - Preprocessing/003 Standardization.mp411.95MB
286. 49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data.mp45.34MB
287. 49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp48.36MB
288. 50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST_ The Dataset.mp44.06MB
289. 50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp47.66MB
290. 50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST_ Importing the Relevant Packages and Loading the Data.mp412.24MB
291. 50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.mp422.93MB
292. 50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST_ Preprocess the Data - Shuffle and Batch.mp432.71MB
293. 50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model.mp422.09MB
294. 50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST_ Select the Loss and the Optimizer.mp410.65MB
295. 50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning.mp431.03MB
296. 50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model.mp422.64MB
297. 51 - Deep Learning - Business Case Example/001 Business Case_ Exploring the Dataset and Identifying Predictors.mp451.38MB
298. 51 - Deep Learning - Business Case Example/002 Business Case_ Outlining the Solution.mp42.21MB
299. 51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset.mp426.19MB
300. 51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data.mp473.82MB
301. 51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data.mp413.8MB
302. 51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result.mp427.77MB
303. 51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism.mp443.81MB
304. 51 - Deep Learning - Business Case Example/011 Business Case_ Testing the Model.mp48.19MB
305. 52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp49.66MB
306. 52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp43.71MB
307. 52 - Deep Learning - Conclusion/004 An overview of CNNs.mp430.47MB
308. 52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp46.75MB
309. 52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches.mp415.65MB
310. 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1.mp43.71MB
311. 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro.mp416.56MB
312. 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/005 Actual Introduction to TensorFlow.mp46.17MB
313. 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/006 Types of File Formats, supporting Tensors.mp48.9MB
314. 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.mp428MB
315. 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent.mp415.72MB
316. 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output.mp417.09MB
317. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/001 MNIST_ What is the MNIST Dataset_.mp44.23MB
318. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp47.68MB
319. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/003 MNIST_ Relevant Packages.mp47.88MB
320. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline.mp434.69MB
321. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/005 MNIST_ Loss and Optimization Algorithm.mp411.56MB
322. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp416.64MB
323. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/007 MNIST_ Batching and Early Stopping.mp48.7MB
324. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/008 MNIST_ Learning.mp431.88MB
325. 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/009 MNIST_ Results and Testing.mp438.19MB
326. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/001 Business Case_ Getting Acquainted with the Dataset.mp460.26MB
327. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/002 Business Case_ Outlining the Solution.mp42.89MB
328. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset.mp421.6MB
329. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing.mp474.39MB
330. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider.mp456.23MB
331. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline.mp442.48MB
332. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization.mp426.95MB
333. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/009 Business Case_ Interpretation.mp418.59MB
334. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/010 Business Case_ Testing the Model.mp44.39MB
335. 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework.mp419.64MB
336. 56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp419.17MB
337. 56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_.mp458.83MB
338. 56 - Software Integration/003 Taking a Closer Look at APIs.mp465.29MB
339. 56 - Software Integration/004 Communication between Software Products through Text Files.mp49.28MB
340. 56 - Software Integration/005 Software Integration - Explained.mp441.99MB
341. 57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp415.8MB
342. 57 - Case Study - What's Next in the Course_/002 The Business Task.mp46.8MB
343. 57 - Case Study - What's Next in the Course_/003 Introducing the Data Set.mp415.29MB
344. 58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp418.03MB
345. 58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp454.27MB
346. 58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp418.04MB
347. 58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp49.9MB
348. 58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp441.3MB
349. 58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp427.63MB
350. 58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp463.77MB
351. 58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables_ A Statistical Perspective.mp43.18MB
352. 58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp451.32MB
353. 58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp419.77MB
354. 58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python.mp47.18MB
355. 58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp417.34MB
356. 58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set.mp440.13MB
357. 58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column.mp438.91MB
358. 58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the _Date_ Column.mp49.12MB
359. 58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise.mp412.23MB
360. 58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets_.mp419.69MB
361. 58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp417.04MB
362. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset.mp411.08MB
363. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression.mp432.5MB
364. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression.mp44.64MB
365. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp415.14MB
366. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp436.12MB
367. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy.mp435.29MB
368. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept.mp426.98MB
369. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem.mp434.41MB
370. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp428.02MB
371. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression.mp415.22MB
372. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model.mp431.96MB
373. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp431.63MB
374. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment.mp425.52MB
375. 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module.mp428.57MB
376. 60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp48.38MB
377. 60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp425.99MB
378. 61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp438.69MB
379. 61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau.mp440.24MB
380. 61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau.mp410.87MB
381. 62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp421.67MB
382. 62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.mp47.85MB
383. 62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp412.26MB
384. 62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp419.4MB
385. 62 - Appendix - Additional Python Tools/005 List Comprehensions.mp443.23MB
386. 62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp433.71MB
387. 63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp422.22MB
388. 63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp416.8MB
389. 63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp45.77MB
390. 63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp415.45MB
391. 63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp426.33MB
392. 63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp413.2MB
393. 63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp410.6MB
394. 63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II.mp417.83MB
395. 63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp429.8MB
396. 63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp437.28MB
397. 63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp423.54MB
398. 63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp420.72MB