FreeCourseSite com Udemy - The Data Science Course 2019 Complete Data Science Bootcamp

1. 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp449.03MB
2. 1. Part 1 Introduction/2. What Does the Course Cover.mp462.26MB
3. 10. Statistics - Descriptive Statistics/1. Types of Data.mp472.52MB
4. 10. Statistics - Descriptive Statistics/11. The Histogram.mp413.78MB
5. 10. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp439.81MB
6. 10. Statistics - Descriptive Statistics/17. Mean, median and mode.mp437.12MB
7. 10. Statistics - Descriptive Statistics/19. Skewness.mp419.41MB
8. 10. Statistics - Descriptive Statistics/22. Variance.mp450.95MB
9. 10. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp445.12MB
10. 10. Statistics - Descriptive Statistics/27. Covariance.mp427.48MB
11. 10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp454.38MB
12. 10. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp429.38MB
13. 10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp438.46MB
14. 10. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp425.85MB
15. 11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4160.46MB
16. 12. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp415.5MB
17. 12. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp422.78MB
18. 12. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp447.83MB
19. 12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp461.59MB
20. 12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp449.86MB
21. 12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp422.51MB
22. 12. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp462.88MB
23. 13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp449.98MB
24. 13. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp459.16MB
25. 13. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp470.48MB
26. 13. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp428.76MB
27. 13. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp426.82MB
28. 13. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).mp419.93MB
29. 13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp478.21MB
30. 13. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp457.03MB
31. 13. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp435.43MB
32. 13. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp432.21MB
33. 14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4102.66MB
34. 15. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp492.05MB
35. 15. Statistics - Hypothesis Testing/10. p-value.mp455.87MB
36. 15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp440.24MB
37. 15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp450.38MB
38. 15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp429.96MB
39. 15. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp436.39MB
40. 15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4112.61MB
41. 15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp443.93MB
42. 15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp454.22MB
43. 16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp469.48MB
44. 17. Part 3 Introduction to Python/1. Introduction to Programming.mp458.54MB
45. 17. Part 3 Introduction to Python/3. Why Python.mp475.08MB
46. 17. Part 3 Introduction to Python/5. Why Jupyter.mp444.32MB
47. 17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp454.41MB
48. 17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp413.79MB
49. 17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp430.58MB
50. 18. Python - Variables and Data Types/1. Variables.mp426.61MB
51. 18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp417.07MB
52. 18. Python - Variables and Data Types/5. Python Strings.mp430.77MB
53. 19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp418.92MB
54. 19. Python - Basic Python Syntax/10. Indexing Elements.mp45.93MB
55. 19. Python - Basic Python Syntax/12. Structuring with Indentation.mp46.82MB
56. 19. Python - Basic Python Syntax/3. The Double Equality Sign.mp45.99MB
57. 19. Python - Basic Python Syntax/5. How to Reassign Values.mp44MB
58. 19. Python - Basic Python Syntax/7. Add Comments.mp45MB
59. 19. Python - Basic Python Syntax/9. Understanding Line Continuation.mp42.36MB
60. 2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp481.41MB
61. 2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp453.55MB
62. 2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp464.51MB
63. 2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4108.98MB
64. 2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp467.74MB
65. 20. Python - Other Python Operators/1. Comparison Operators.mp410.17MB
66. 20. Python - Other Python Operators/3. Logical and Identity Operators.mp430.05MB
67. 21. Python - Conditional Statements/1. The IF Statement.mp413.63MB
68. 21. Python - Conditional Statements/3. The ELSE Statement.mp413.58MB
69. 21. Python - Conditional Statements/4. The ELIF Statement.mp433.15MB
70. 21. Python - Conditional Statements/5. A Note on Boolean Values.mp411.26MB
71. 22. Python - Python Functions/1. Defining a Function in Python.mp47.75MB
72. 22. Python - Python Functions/2. How to Create a Function with a Parameter.mp423.87MB
73. 22. Python - Python Functions/3. Defining a Function in Python - Part II.mp414.78MB
74. 22. Python - Python Functions/4. How to Use a Function within a Function.mp48.14MB
75. 22. Python - Python Functions/5. Conditional Statements and Functions.mp415.68MB
76. 22. Python - Python Functions/6. Functions Containing a Few Arguments.mp47.58MB
77. 22. Python - Python Functions/7. Built-in Functions in Python.mp422.02MB
78. 23. Python - Sequences/1. Lists.mp421.99MB
79. 23. Python - Sequences/3. Using Methods.mp421.95MB
80. 23. Python - Sequences/5. List Slicing.mp430.77MB
81. 23. Python - Sequences/6. Tuples.mp416.68MB
82. 23. Python - Sequences/7. Dictionaries.mp425.04MB
83. 24. Python - Iterations/1. For Loops.mp411.79MB
84. 24. Python - Iterations/3. While Loops and Incrementing.mp415.44MB
85. 24. Python - Iterations/4. Lists with the range() Function.mp411.43MB
86. 24. Python - Iterations/6. Conditional Statements and Loops.mp416.09MB
87. 24. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp49.48MB
88. 24. Python - Iterations/8. How to Iterate over Dictionaries.mp416.98MB
89. 25. Python - Advanced Python Tools/1. Object Oriented Programming.mp433.59MB
90. 25. Python - Advanced Python Tools/3. Modules and Packages.mp48.5MB
91. 25. Python - Advanced Python Tools/5. What is the Standard Library.mp418.03MB
92. 25. Python - Advanced Python Tools/7. Importing Modules in Python.mp419.94MB
93. 26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp417.33MB
94. 27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp457.37MB
95. 27. Advanced Statistical Methods - Linear regression/10. Using Seaborn for Graphs.mp412.25MB
96. 27. Advanced Statistical Methods - Linear regression/11. How to Interpret the Regression Table.mp444.64MB
97. 27. Advanced Statistical Methods - Linear regression/13. Decomposition of Variability.mp449.66MB
98. 27. Advanced Statistical Methods - Linear regression/15. What is the OLS.mp428.31MB
99. 27. Advanced Statistical Methods - Linear regression/17. R-Squared.mp441.03MB
100. 27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.mp414.73MB
101. 27. Advanced Statistical Methods - Linear regression/5. Geometrical Representation of the Linear Regression Model.mp45.13MB
102. 27. Advanced Statistical Methods - Linear regression/7. Python Packages Installation.mp440.58MB
103. 27. Advanced Statistical Methods - Linear regression/8. First Regression in Python.mp444.56MB
104. 28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.mp421.52MB
105. 28. Advanced Statistical Methods - Multiple Linear Regression/11. A2 No Endogeneity.mp435.68MB
106. 28. Advanced Statistical Methods - Multiple Linear Regression/13. A3 Normality and Homoscedasticity.mp442.7MB
107. 28. Advanced Statistical Methods - Multiple Linear Regression/14. A4 No Autocorrelation.mp431.51MB
108. 28. Advanced Statistical Methods - Multiple Linear Regression/16. A5 No Multicollinearity.mp428.71MB
109. 28. Advanced Statistical Methods - Multiple Linear Regression/18. Dealing with Categorical Data - Dummy Variables.mp455.66MB
110. 28. Advanced Statistical Methods - Multiple Linear Regression/20. Making Predictions with the Linear Regression.mp424.7MB
111. 28. Advanced Statistical Methods - Multiple Linear Regression/3. Adjusted R-Squared.mp454.84MB
112. 28. Advanced Statistical Methods - Multiple Linear Regression/6. Test for Significance of the Model (F-Test).mp416.42MB
113. 28. Advanced Statistical Methods - Multiple Linear Regression/7. OLS Assumptions.mp421.85MB
114. 28. Advanced Statistical Methods - Multiple Linear Regression/9. A1 Linearity.mp412.6MB
115. 29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp427.07MB
116. 29. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp438.43MB
117. 29. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp432.85MB
118. 29. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp422.3MB
119. 29. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp432.28MB
120. 29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp434.69MB
121. 29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp486.49MB
122. 29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp417.11MB
123. 29. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp423.06MB
124. 29. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp430.55MB
125. 29. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp432.28MB
126. 3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4126.88MB
127. 30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp453.42MB
128. 30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp471.53MB
129. 30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp436.16MB
130. 30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp414.56MB
131. 31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp427.29MB
132. 31. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp49.93MB
133. 31. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp443.01MB
134. 31. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp456.12MB
135. 31. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp474.45MB
136. 31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp451.82MB
137. 31. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp421.24MB
138. 31. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp444.13MB
139. 31. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp437.7MB
140. 31. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp430.11MB
141. 32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp444.57MB
142. 32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp429.07MB
143. 32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp429.62MB
144. 33. Part 5 Mathematics/1. What is a matrix.mp433.59MB
145. 33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.mp432.61MB
146. 33. Part 5 Mathematics/12. Errors when Adding Matrices.mp411.18MB
147. 33. Part 5 Mathematics/13. Transpose of a Matrix.mp438.07MB
148. 33. Part 5 Mathematics/14. Dot Product.mp423.99MB
149. 33. Part 5 Mathematics/15. Dot Product of Matrices.mp449.43MB
150. 33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4144.34MB
151. 33. Part 5 Mathematics/3. Scalars and Vectors.mp433.86MB
152. 33. Part 5 Mathematics/5. Linear Algebra and Geometry.mp449.79MB
153. 33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp426.68MB
154. 33. Part 5 Mathematics/8. What is a Tensor.mp422.52MB
155. 34. Part 6 Deep Learning/1. What to Expect from this Part.mp431.1MB
156. 35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp442.93MB
157. 35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp438.31MB
158. 35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp422.65MB
159. 35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp417.92MB
160. 35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp423.28MB
161. 35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp437.24MB
162. 35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp455.62MB
163. 35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp439.42MB
164. 35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp428.71MB
165. 35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp445.1MB
166. 35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp428.44MB
167. 35. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp425.11MB
168. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp420.59MB
169. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp434.94MB
170. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp424.4MB
171. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp461.13MB
172. 37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp414.55MB
173. 37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp447.69MB
174. 37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp417.42MB
175. 37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp420.34MB
176. 37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp438.5MB
177. 37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp432.51MB
178. 37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp437.4MB
179. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp412.5MB
180. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp429.54MB
181. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp459.36MB
182. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp427.69MB
183. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp425.1MB
184. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp425.92MB
185. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp434.95MB
186. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp419.51MB
187. 39. Deep Learning - Overfitting/1. What is Overfitting.mp431.08MB
188. 39. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp425.07MB
189. 39. Deep Learning - Overfitting/3. What is Validation.mp432.72MB
190. 39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp425.19MB
191. 39. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp420.7MB
192. 39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp424.18MB
193. 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp481.18MB
194. 40. Deep Learning - Initialization/1. What is Initialization.mp421.76MB
195. 40. Deep Learning - Initialization/2. Types of Simple Initializations.mp414.31MB
196. 40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp417.14MB
197. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp428.69MB
198. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp411.01MB
199. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp416.44MB
200. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp429.08MB
201. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp49.11MB
202. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp426.35MB
203. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp422.35MB
204. 42. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp427.78MB
205. 42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp411.84MB
206. 42. Deep Learning - Preprocessing/3. Standardization.mp450.98MB
207. 42. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp418.6MB
208. 42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp428.94MB
209. 43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp417.82MB
210. 43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp422.59MB
211. 43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp418.91MB
212. 43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp456.38MB
213. 43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp425.87MB
214. 43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp443.9MB
215. 43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp412.85MB
216. 43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp446.69MB
217. 43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp462.78MB
218. 44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp487.65MB
219. 44. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp411.21MB
220. 44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp436.38MB
221. 44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp412.21MB
222. 44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp439.41MB
223. 44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4103.41MB
224. 44. Deep Learning - Business Case Example/6. Creating a Data Provider.mp476.34MB
225. 44. Deep Learning - Business Case Example/7. Business Case Model Outline.mp453.12MB
226. 44. Deep Learning - Business Case Example/8. Business Case Optimization.mp441.52MB
227. 44. Deep Learning - Business Case Example/9. Business Case Interpretation.mp425.74MB
228. 45. Deep Learning - Conclusion/1. Summary on What You've Learned.mp439.76MB
229. 45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp420.13MB
230. 45. Deep Learning - Conclusion/3. An overview of CNNs.mp458.8MB
231. 45. Deep Learning - Conclusion/5. An Overview of RNNs.mp425.26MB
232. 45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp444.77MB
233. 46. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp469.03MB
234. 46. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4104.08MB
235. 46. Software Integration/5. Taking a Closer Look at APIs.mp4115.59MB
236. 46. Software Integration/7. Communication between Software Products through Text Files.mp460.34MB
237. 46. Software Integration/9. Software Integration - Explained.mp472.64MB
238. 47. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp452.31MB
239. 47. Case Study - What's Next in the Course/2. The Business Task.mp439.16MB
240. 47. Case Study - What's Next in the Course/3. Introducing the Data Set.mp440.86MB
241. 48. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp440.58MB
242. 48. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp481.11MB
243. 48. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp413.74MB
244. 48. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp474.6MB
245. 48. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp438.74MB
246. 48. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp423.15MB
247. 48. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp414.02MB
248. 48. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp425.67MB
249. 48. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp457.28MB
250. 48. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp447.8MB
251. 48. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp427.97MB
252. 48. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp461.91MB
253. 48. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp429.51MB
254. 48. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp439.59MB
255. 48. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp421.64MB
256. 48. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp427.85MB
257. 48. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp420.18MB
258. 48. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp461.76MB
259. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp427.54MB
260. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp440.4MB
261. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp439.57MB
262. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp449.06MB
263. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp437.46MB
264. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp444.48MB
265. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp445.79MB
266. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp416.75MB
267. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp420.59MB
268. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp452.76MB
269. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp441.62MB
270. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp438.88MB
271. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp452.38MB
272. 49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.19MB
273. 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4138.31MB
274. 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4123.51MB
275. 5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp442.78MB
276. 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp499.32MB
277. 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4125.15MB
278. 5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp436.81MB
279. 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp429.94MB
280. 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp475.5MB
281. 5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp422.04MB
282. 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp489.94MB
283. 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp429.54MB
284. 50. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp425.48MB
285. 50. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp454.25MB
286. 51. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp456.55MB
287. 51. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp459.33MB
288. 51. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp440.63MB
289. 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4103.52MB
290. 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp454.39MB
291. 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp472.85MB
292. 9. Part 2 Statistics/1. Population and Sample.mp458.11MB