GigaCourse com Udemy - The Data Science Course 2020 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.mp462.25MB
3. 10. Probability - Combinatorics/1. Fundamentals of Combinatorics.mp416.22MB
4. 10. Probability - Combinatorics/11. Solving Combinations.mp457.34MB
5. 10. Probability - Combinatorics/13. Symmetry of Combinations.mp440.3MB
6. 10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp433.15MB
7. 10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.mp441.29MB
8. 10. Probability - Combinatorics/19. A Recap of Combinatorics.mp438.5MB
9. 10. Probability - Combinatorics/20. A Practical Example of Combinatorics.mp4134.31MB
10. 10. Probability - Combinatorics/3. Permutations and How to Use Them.mp442.72MB
11. 10. Probability - Combinatorics/5. Simple Operations with Factorials.mp436.12MB
12. 10. Probability - Combinatorics/7. Solving Variations with Repetition.mp434MB
13. 10. Probability - Combinatorics/9. Solving Variations without Repetition.mp443.14MB
14. 11. Probability - Bayesian Inference/1. Sets and Events.mp453.47MB
15. 11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.mp434.78MB
16. 11. Probability - Bayesian Inference/13. The Conditional Probability Formula.mp445.87MB
17. 11. Probability - Bayesian Inference/15. The Law of Total Probability.mp434.94MB
18. 11. Probability - Bayesian Inference/16. The Additive Rule.mp426.97MB
19. 11. Probability - Bayesian Inference/18. The Multiplication Law.mp449.02MB
20. 11. Probability - Bayesian Inference/20. Bayes' Law.mp449.93MB
21. 11. Probability - Bayesian Inference/22. A Practical Example of Bayesian Inference.mp4145.12MB
22. 11. Probability - Bayesian Inference/3. Ways Sets Can Interact.mp447.42MB
23. 11. Probability - Bayesian Inference/5. Intersection of Sets.mp426.97MB
24. 11. Probability - Bayesian Inference/7. Union of Sets.mp457.19MB
25. 11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.mp425.4MB
26. 12. Probability - Distributions/1. Fundamentals of Probability Distributions.mp473.41MB
27. 12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.mp468.83MB
28. 12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.mp455.75MB
29. 12. Probability - Distributions/15. Characteristics of Continuous Distributions.mp484.12MB
30. 12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.mp448.24MB
31. 12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.mp447.9MB
32. 12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.mp427.19MB
33. 12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp426.34MB
34. 12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.mp440.23MB
35. 12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.mp447.06MB
36. 12. Probability - Distributions/29. A Practical Example of Probability Distributions.mp4157.82MB
37. 12. Probability - Distributions/3. Types of Probability Distributions.mp471.06MB
38. 12. Probability - Distributions/5. Characteristics of Discrete Distributions.mp422.7MB
39. 12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.mp424.39MB
40. 12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.mp434.14MB
41. 13. Probability - Probability in Other Fields/1. Probability in Finance.mp499.06MB
42. 13. Probability - Probability in Other Fields/2. Probability in Statistics.mp477.28MB
43. 13. Probability - Probability in Other Fields/3. Probability in Data Science.mp463.49MB
44. 14. Part 3 Statistics/1. Population and Sample.mp458.11MB
45. 15. Statistics - Descriptive Statistics/1. Types of Data.mp472.52MB
46. 15. Statistics - Descriptive Statistics/11. The Histogram.mp413.78MB
47. 15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp439.81MB
48. 15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp437.13MB
49. 15. Statistics - Descriptive Statistics/19. Skewness.mp419.4MB
50. 15. Statistics - Descriptive Statistics/22. Variance.mp450.95MB
51. 15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp445.12MB
52. 15. Statistics - Descriptive Statistics/27. Covariance.mp427.49MB
53. 15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp454.38MB
54. 15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp429.39MB
55. 15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp436.64MB
56. 15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp425.86MB
57. 16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4160.46MB
58. 17. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp415.5MB
59. 17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp422.78MB
60. 17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp447.83MB
61. 17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp461.59MB
62. 17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp449.86MB
63. 17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp422.51MB
64. 17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp462.89MB
65. 18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp449.99MB
66. 18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp447.24MB
67. 18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp470.47MB
68. 18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent Samples (Part 1).mp428.76MB
69. 18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent Samples (Part 2).mp426.83MB
70. 18. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent Samples (Part 3).mp419.94MB
71. 18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; Z-score.mp478.2MB
72. 18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp457.03MB
73. 18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp435.44MB
74. 18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; T-score.mp432.2MB
75. 19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4102.66MB
76. 2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so Many.mp481.41MB
77. 2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp453.55MB
78. 2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp464.51MB
79. 2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4108.98MB
80. 2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp467.74MB
81. 20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp492.04MB
82. 20. Statistics - Hypothesis Testing/10. p-value.mp455.87MB
83. 20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp440.25MB
84. 20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp450.37MB
85. 20. Statistics - Hypothesis Testing/16. Test for the mean. Independent Samples (Part 1).mp433.95MB
86. 20. Statistics - Hypothesis Testing/18. Test for the mean. Independent Samples (Part 2).mp436.39MB
87. 20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp482.61MB
88. 20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp443.94MB
89. 20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp454.22MB
90. 21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp469.48MB
91. 22. Part 4 Introduction to Python/1. Introduction to Programming.mp458.54MB
92. 22. Part 4 Introduction to Python/11. Python 2 vs Python 3.mp411.27MB
93. 22. Part 4 Introduction to Python/3. Why Python.mp475.07MB
94. 22. Part 4 Introduction to Python/5. Why Jupyter.mp444.31MB
95. 22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp450.99MB
96. 22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp413.8MB
97. 22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp430.59MB
98. 23. Python - Variables and Data Types/1. Variables.mp425.3MB
99. 23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp417.06MB
100. 23. Python - Variables and Data Types/5. Python Strings.mp450.64MB
101. 24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp418.92MB
102. 24. Python - Basic Python Syntax/10. Indexing Elements.mp45.93MB
103. 24. Python - Basic Python Syntax/12. Structuring with Indentation.mp413.14MB
104. 24. Python - Basic Python Syntax/3. The Double Equality Sign.mp46MB
105. 24. Python - Basic Python Syntax/5. How to Reassign Values.mp44MB
106. 24. Python - Basic Python Syntax/7. Add Comments.mp411.27MB
107. 24. Python - Basic Python Syntax/9. Understanding Line Continuation.mp42.36MB
108. 25. Python - Other Python Operators/1. Comparison Operators.mp410.17MB
109. 25. Python - Other Python Operators/3. Logical and Identity Operators.mp430.05MB
110. 26. Python - Conditional Statements/1. The IF Statement.mp423.23MB
111. 26. Python - Conditional Statements/3. The ELSE Statement.mp423.29MB
112. 26. Python - Conditional Statements/4. The ELIF Statement.mp453.33MB
113. 26. Python - Conditional Statements/5. A Note on Boolean Values.mp420MB
114. 27. Python - Python Functions/1. Defining a Function in Python.mp414.76MB
115. 27. Python - Python Functions/2. How to Create a Function with a Parameter.mp438.1MB
116. 27. Python - Python Functions/3. Defining a Function in Python - Part II.mp425.24MB
117. 27. Python - Python Functions/4. How to Use a Function within a Function.mp48.14MB
118. 27. Python - Python Functions/5. Conditional Statements and Functions.mp415.68MB
119. 27. Python - Python Functions/6. Functions Containing a Few Arguments.mp414.72MB
120. 27. Python - Python Functions/7. Built-in Functions in Python.mp422.02MB
121. 28. Python - Sequences/1. Lists.mp437.79MB
122. 28. Python - Sequences/3. Using Methods.mp437.59MB
123. 28. Python - Sequences/5. List Slicing.mp430.77MB
124. 28. Python - Sequences/6. Tuples.mp429.5MB
125. 28. Python - Sequences/7. Dictionaries.mp441.68MB
126. 29. Python - Iterations/1. For Loops.mp423.6MB
127. 29. Python - Iterations/3. While Loops and Incrementing.mp428.44MB
128. 29. Python - Iterations/4. Lists with the range() Function.mp425.8MB
129. 29. Python - Iterations/6. Conditional Statements and Loops.mp427.76MB
130. 29. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp49.48MB
131. 29. Python - Iterations/8. How to Iterate over Dictionaries.mp429.65MB
132. 3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4126.87MB
133. 30. Python - Advanced Python Tools/1. Object Oriented Programming.mp433.6MB
134. 30. Python - Advanced Python Tools/3. Modules and Packages.mp48.51MB
135. 30. Python - Advanced Python Tools/5. What is the Standard Library.mp418.04MB
136. 30. Python - Advanced Python Tools/7. Importing Modules in Python.mp419.93MB
137. 31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp417.32MB
138. 32. Advanced Statistical Methods - Linear Regression with StatsModels/1. The Linear Regression Model.mp457.37MB
139. 32. Advanced Statistical Methods - Linear Regression with StatsModels/10. Using Seaborn for Graphs.mp412.24MB
140. 32. Advanced Statistical Methods - Linear Regression with StatsModels/11. How to Interpret the Regression Table.mp444.64MB
141. 32. Advanced Statistical Methods - Linear Regression with StatsModels/13. Decomposition of Variability.mp449.66MB
142. 32. Advanced Statistical Methods - Linear Regression with StatsModels/15. What is the OLS.mp428.31MB
143. 32. Advanced Statistical Methods - Linear Regression with StatsModels/17. R-Squared.mp441.04MB
144. 32. Advanced Statistical Methods - Linear Regression with StatsModels/3. Correlation vs Regression.mp414.74MB
145. 32. Advanced Statistical Methods - Linear Regression with StatsModels/5. Geometrical Representation of the Linear Regression Model.mp45.13MB
146. 32. Advanced Statistical Methods - Linear Regression with StatsModels/7. Python Packages Installation.mp440.58MB
147. 32. Advanced Statistical Methods - Linear Regression with StatsModels/8. First Regression in Python.mp444.57MB
148. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.mp421.52MB
149. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp435.68MB
150. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp442.7MB
151. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp431.51MB
152. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp428.71MB
153. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp455.67MB
154. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp424.7MB
155. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp454.83MB
156. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/6. Test for Significance of the Model (F-Test).mp416.42MB
157. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.mp421.85MB
158. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/9. A1 Linearity.mp412.61MB
159. 34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp427.25MB
160. 34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp429.51MB
161. 34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with P-values.mp412.3MB
162. 34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp439.08MB
163. 34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp434.9MB
164. 34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp425.97MB
165. 34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp416.95MB
166. 34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp449.18MB
167. 34. Advanced Statistical Methods - Linear Regression with sklearn/2. How are Going to Approach this Section.mp419.42MB
168. 34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp434.77MB
169. 34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp432.01MB
170. 34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.mp420.08MB
171. 34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp430.88MB
172. 35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp497.08MB
173. 35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp446MB
174. 35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp423.7MB
175. 35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp456.04MB
176. 35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp457.88MB
177. 36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp427.07MB
178. 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp438.43MB
179. 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp432.85MB
180. 36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp422.29MB
181. 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp432.28MB
182. 36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp434.69MB
183. 36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp486.49MB
184. 36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp417.11MB
185. 36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp423.05MB
186. 36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp430.55MB
187. 36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp432.29MB
188. 37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp453.42MB
189. 37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp471.53MB
190. 37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp436.15MB
191. 37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp414.56MB
192. 38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp427.29MB
193. 38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp49.94MB
194. 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp443.01MB
195. 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp456.11MB
196. 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp474.45MB
197. 38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp451.82MB
198. 38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp421.23MB
199. 38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp444.13MB
200. 38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp437.7MB
201. 38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp430.11MB
202. 39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp444.57MB
203. 39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp429.06MB
204. 39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp429.62MB
205. 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason Behind These Disciplines.mp481.18MB
206. 40. Part 6 Mathematics/1. What is a Matrix.mp433.6MB
207. 40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp432.61MB
208. 40. Part 6 Mathematics/12. Errors when Adding Matrices.mp411.17MB
209. 40. Part 6 Mathematics/13. Transpose of a Matrix.mp438.07MB
210. 40. Part 6 Mathematics/14. Dot Product.mp424MB
211. 40. Part 6 Mathematics/15. Dot Product of Matrices.mp449.43MB
212. 40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4144.33MB
213. 40. Part 6 Mathematics/3. Scalars and Vectors.mp433.85MB
214. 40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp449.79MB
215. 40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp426.67MB
216. 40. Part 6 Mathematics/8. What is a Tensor.mp422.53MB
217. 41. Part 7 Deep Learning/1. What to Expect from this Part.mp431.11MB
218. 42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp442.92MB
219. 42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp438.31MB
220. 42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp422.64MB
221. 42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp417.92MB
222. 42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp423.28MB
223. 42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp437.25MB
224. 42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp455.62MB
225. 42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp439.42MB
226. 42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp428.72MB
227. 42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp445.1MB
228. 42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp428.45MB
229. 42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp425.11MB
230. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp420.59MB
231. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp434.94MB
232. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp424.41MB
233. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp461.13MB
234. 44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.mp438.76MB
235. 44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.mp433.51MB
236. 44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.mp421.99MB
237. 44. Deep Learning - TensorFlow 2.0 Introduction/4. A Note on TensorFlow 2 Syntax.mp46.75MB
238. 44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.mp416.4MB
239. 44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.mp434.69MB
240. 44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.mp430.28MB
241. 44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.mp422.91MB
242. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp412.5MB
243. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp429.54MB
244. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp459.36MB
245. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp427.69MB
246. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp425.1MB
247. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp425.92MB
248. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp434.95MB
249. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation Picture.mp419.5MB
250. 46. Deep Learning - Overfitting/1. What is Overfitting.mp431.09MB
251. 46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp425.07MB
252. 46. Deep Learning - Overfitting/3. What is Validation.mp432.72MB
253. 46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp425.19MB
254. 46. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp420.71MB
255. 46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp424.18MB
256. 47. Deep Learning - Initialization/1. What is Initialization.mp421.76MB
257. 47. Deep Learning - Initialization/2. Types of Simple Initializations.mp414.31MB
258. 47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp417.15MB
259. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp428.69MB
260. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp411.02MB
261. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp416.44MB
262. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp429.08MB
263. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp49.12MB
264. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp426.36MB
265. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp422.35MB
266. 49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp427.79MB
267. 49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp411.84MB
268. 49. Deep Learning - Preprocessing/3. Standardization.mp450.99MB
269. 49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp418.6MB
270. 49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp428.94MB
271. 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4138.3MB
272. 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4111.65MB
273. 5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp442.79MB
274. 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp499.32MB
275. 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4125.14MB
276. 5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp436.81MB
277. 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp429.93MB
278. 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp475.5MB
279. 5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp422.03MB
280. 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp489.94MB
281. 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp429.54MB
282. 50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.mp413.39MB
283. 50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp440.96MB
284. 50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp429.53MB
285. 50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp418.67MB
286. 50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.mp416.32MB
287. 50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp429.04MB
288. 50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.mp441.53MB
289. 50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.mp428.23MB
290. 50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.mp413.91MB
291. 51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.mp466.28MB
292. 51. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp410.8MB
293. 51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp47.3MB
294. 51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.mp430.44MB
295. 51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.mp484.33MB
296. 51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.mp417.57MB
297. 51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.mp431.19MB
298. 51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.mp449.81MB
299. 52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp439.75MB
300. 52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp420.12MB
301. 52. Deep Learning - Conclusion/4. An overview of CNNs.mp458.8MB
302. 52. Deep Learning - Conclusion/5. An Overview of RNNs.mp425.26MB
303. 52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp444.77MB
304. 53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.mp411.36MB
305. 53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.mp447.7MB
306. 53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.mp417.41MB
307. 53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.mp420.35MB
308. 53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp438.5MB
309. 53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.mp432.52MB
310. 53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.mp437.4MB
311. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp417.82MB
312. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp422.58MB
313. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp418.91MB
314. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/4. MNIST Model Outline.mp456.39MB
315. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp425.87MB
316. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp443.91MB
317. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp412.85MB
318. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/8. MNIST Learning.mp446.69MB
319. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp462.77MB
320. 55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting Acquainted with the Dataset.mp487.65MB
321. 55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp411.2MB
322. 55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp436.39MB
323. 55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.mp412.21MB
324. 55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.mp439.41MB
325. 55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.mp4103.41MB
326. 55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.mp476.34MB
327. 55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.mp453.13MB
328. 55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.mp441.53MB
329. 55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.mp425.75MB
330. 56. Software Integration/1. Exploring the Problem with a Machine Learning Mindset.mp427.55MB
331. 56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp469.03MB
332. 56. Software Integration/10. Interpreting the Coefficients of the Logistic Regression.mp440.4MB
333. 56. Software Integration/11. Backward Elimination or How to Simplify Your Model.mp439.56MB
334. 56. Software Integration/12. Testing the Model We Created.mp449.06MB
335. 56. Software Integration/13. Saving the Model and Preparing it for Deployment.mp437.46MB
336. 56. Software Integration/16. Preparing the Deployment of the Model through a Module.mp444.48MB
337. 56. Software Integration/2. Creating the Targets for the Logistic Regression.mp445.79MB
338. 56. Software Integration/3. Selecting the Inputs for the Logistic Regression.mp416.75MB
339. 56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4104.08MB
340. 56. Software Integration/4. Standardizing the Data.mp420.6MB
341. 56. Software Integration/5. Splitting the Data for Training and Testing.mp452.76MB
342. 56. Software Integration/5. Taking a Closer Look at APIs.mp4115.59MB
343. 56. Software Integration/6. Fitting the Model and Assessing its Accuracy.mp441.62MB
344. 56. Software Integration/7. Communication between Software Products through Text Files.mp460.34MB
345. 56. Software Integration/7. Creating a Summary Table with the Coefficients and Intercept.mp438.87MB
346. 56. Software Integration/8. Interpreting the Coefficients for Our Problem.mp452.37MB
347. 56. Software Integration/9. Software Integration - Explained.mp463.69MB
348. 56. Software Integration/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.19MB
349. 57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp452.3MB
350. 57. Case Study - What's Next in the Course/2. The Business Task.mp439.15MB
351. 57. Case Study - What's Next in the Course/3. Introducing the Data Set.mp440.87MB
352. 58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp440.57MB
353. 58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp481.11MB
354. 58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp413.75MB
355. 58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp474.6MB
356. 58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp438.73MB
357. 58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp423.16MB
358. 58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp414.02MB
359. 58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp425.67MB
360. 58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp457.28MB
361. 58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp447.79MB
362. 58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp427.97MB
363. 58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp461.9MB
364. 58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp429.51MB
365. 58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp439.59MB
366. 58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp421.63MB
367. 58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp427.86MB
368. 58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp420.19MB
369. 58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp461.76MB
370. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp427.55MB
371. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp440.4MB
372. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp439.56MB
373. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp449.06MB
374. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp437.46MB
375. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp444.48MB
376. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp445.79MB
377. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp416.75MB
378. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp420.6MB
379. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp452.76MB
380. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp441.62MB
381. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp438.87MB
382. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp452.37MB
383. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.19MB
384. 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4103.51MB
385. 60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp425.49MB
386. 60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp454.26MB
387. 61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp456.55MB
388. 61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp459.33MB
389. 61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp440.63MB
390. 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp454.38MB
391. 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp472.85MB
392. 9. Part 2 Probability/1. The Basic Probability Formula.mp485.91MB
393. 9. Part 2 Probability/3. Computing Expected Values.mp475.68MB
394. 9. Part 2 Probability/5. Frequency.mp461.73MB
395. 9. Part 2 Probability/7. Events and Their Complements.mp459.16MB