• Урок 1. 00:05:07
    A Practical Example: What You Will Learn in This Course
  • Урок 2. 00:03:35
    What Does the Course Cover
  • Урок 3. 00:05:22
    Data Science and Business Buzzwords: Why are there so Many?
  • Урок 4. 00:03:51
    What is the difference between Analysis and Analytics
  • Урок 5. 00:08:27
    Business Analytics, Data Analytics, and Data Science: An Introduction
  • Урок 6. 00:09:32
    Continuing with BI, ML, and AI
  • Урок 7. 00:04:04
    A Breakdown of our Data Science Infographic
  • Урок 8. 00:07:20
    Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
  • Урок 9. 00:04:45
    The Reason Behind These Disciplines
  • Урок 10. 00:08:14
    Techniques for Working with Traditional Data
  • Урок 11. 00:01:45
    Real Life Examples of Traditional Data
  • Урок 12. 00:04:27
    Techniques for Working with Big Data
  • Урок 13. 00:01:33
    Real Life Examples of Big Data
  • Урок 14. 00:06:46
    Business Intelligence (BI) Techniques
  • Урок 15. 00:01:43
    Real Life Examples of Business Intelligence (BI)
  • Урок 16. 00:09:09
    Techniques for Working with Traditional Methods
  • Урок 17. 00:02:46
    Real Life Examples of Traditional Methods
  • Урок 18. 00:06:56
    Machine Learning (ML) Techniques
  • Урок 19. 00:08:14
    Types of Machine Learning
  • Урок 20. 00:02:12
    Real Life Examples of Machine Learning (ML)
  • Урок 21. 00:05:52
    Necessary Programming Languages and Software Used in Data Science
  • Урок 22. 00:03:30
    Finding the Job - What to Expect and What to Look for
  • Урок 23. 00:04:11
    Debunking Common Misconceptions
  • Урок 24. 00:07:10
    The Basic Probability Formula
  • Урок 25. 00:05:30
    Computing Expected Values
  • Урок 26. 00:05:01
    Frequency
  • Урок 27. 00:05:27
    Events and Their Complements
  • Урок 28. 00:01:05
    Fundamentals of Combinatorics
  • Урок 29. 00:03:22
    Permutations and How to Use Them
  • Урок 30. 00:03:36
    Simple Operations with Factorials
  • Урок 31. 00:03:00
    Solving Variations with Repetition
  • Урок 32. 00:03:49
    Solving Variations without Repetition
  • Урок 33. 00:04:52
    Solving Combinations
  • Урок 34. 00:03:27
    Symmetry of Combinations
  • Урок 35. 00:02:53
    Solving Combinations with Separate Sample Spaces
  • Урок 36. 00:03:13
    Combinatorics in Real-Life: The Lottery
  • Урок 37. 00:02:56
    A Recap of Combinatorics
  • Урок 38. 00:10:54
    A Practical Example of Combinatorics
  • Урок 39. 00:04:26
    Sets and Events
  • Урок 40. 00:03:46
    Ways Sets Can Interact
  • Урок 41. 00:02:07
    Intersection of Sets
  • Урок 42. 00:04:52
    Union of Sets
  • Урок 43. 00:02:10
    Mutually Exclusive Sets
  • Урок 44. 00:03:02
    Dependence and Independence of Sets
  • Урок 45. 00:04:17
    The Conditional Probability Formula
  • Урок 46. 00:03:04
    The Law of Total Probability
  • Урок 47. 00:02:22
    The Additive Rule
  • Урок 48. 00:04:06
    The Multiplication Law
  • Урок 49. 00:05:45
    Bayes' Law
  • Урок 50. 00:14:53
    A Practical Example of Bayesian Inference
  • Урок 51. 00:06:30
    Fundamentals of Probability Distributions
  • Урок 52. 00:07:33
    Types of Probability Distributions
  • Урок 53. 00:02:01
    Characteristics of Discrete Distributions
  • Урок 54. 00:02:14
    Discrete Distributions: The Uniform Distribution
  • Урок 55. 00:03:27
    Discrete Distributions: The Bernoulli Distribution
  • Урок 56. 00:07:05
    Discrete Distributions: The Binomial Distribution
  • Урок 57. 00:05:28
    Discrete Distributions: The Poisson Distribution
  • Урок 58. 00:07:13
    Characteristics of Continuous Distributions
  • Урок 59. 00:04:09
    Continuous Distributions: The Normal Distribution
  • Урок 60. 00:04:26
    Continuous Distributions: The Standard Normal Distribution
  • Урок 61. 00:02:30
    Continuous Distributions: The Students' T Distribution
  • Урок 62. 00:02:23
    Continuous Distributions: The Chi-Squared Distribution
  • Урок 63. 00:03:16
    Continuous Distributions: The Exponential Distribution
  • Урок 64. 00:04:08
    Continuous Distributions: The Logistic Distribution
  • Урок 65. 00:15:04
    A Practical Example of Probability Distributions
  • Урок 66. 00:07:47
    Probability in Finance
  • Урок 67. 00:06:19
    Probability in Statistics
  • Урок 68. 00:04:48
    Probability in Data Science
  • Урок 69. 00:04:03
    Population and Sample
  • Урок 70. 00:04:34
    Types of Data
  • Урок 71. 00:03:44
    Levels of Measurement
  • Урок 72. 00:04:53
    Categorical Variables - Visualization Techniques
  • Урок 73. 00:03:10
    Numerical Variables - Frequency Distribution Table
  • Урок 74. 00:02:15
    The Histogram
  • Урок 75. 00:04:45
    Cross Tables and Scatter Plots
  • Урок 76. 00:04:21
    Mean, median and mode
  • Урок 77. 00:02:38
    Skewness
  • Урок 78. 00:05:56
    Variance
  • Урок 79. 00:04:41
    Standard Deviation and Coefficient of Variation
  • Урок 80. 00:03:24
    Covariance
  • Урок 81. 00:03:18
    Correlation Coefficient
  • Урок 82. 00:16:16
    Practical Example: Descriptive Statistics
  • Урок 83. 00:01:01
    Introduction
  • Урок 84. 00:04:34
    What is a Distribution
  • Урок 85. 00:03:55
    The Normal Distribution
  • Урок 86. 00:03:31
    The Standard Normal Distribution
  • Урок 87. 00:04:21
    Central Limit Theorem
  • Урок 88. 00:01:28
    Standard error
  • Урок 89. 00:03:08
    Estimators and Estimates
  • Урок 90. 00:02:42
    What are Confidence Intervals?
  • Урок 91. 00:08:02
    Confidence Intervals; Population Variance Known; Z-score
  • Урок 92. 00:04:39
    Confidence Interval Clarifications
  • Урок 93. 00:03:23
    Student's T Distribution
  • Урок 94. 00:04:37
    Confidence Intervals; Population Variance Unknown; T-score
  • Урок 95. 00:04:53
    Margin of Error
  • Урок 96. 00:06:05
    Confidence intervals. Two means. Dependent samples
  • Урок 97. 00:04:32
    Confidence intervals. Two means. Independent Samples (Part 1)
  • Урок 98. 00:03:58
    Confidence intervals. Two means. Independent Samples (Part 2)
  • Урок 99. 00:01:28
    Confidence intervals. Two means. Independent Samples (Part 3)
  • Урок 100. 00:10:07
    Practical Example: Inferential Statistics
  • Урок 101. 00:05:53
    Null vs Alternative Hypothesis
  • Урок 102. 00:07:06
    Rejection Region and Significance Level
  • Урок 103. 00:04:15
    Type I Error and Type II Error
  • Урок 104. 00:06:35
    Test for the Mean. Population Variance Known
  • Урок 105. 00:04:14
    p-value
  • Урок 106. 00:04:49
    Test for the Mean. Population Variance Unknown
  • Урок 107. 00:05:19
    Test for the Mean. Dependent Samples
  • Урок 108. 00:04:23
    Test for the mean. Independent Samples (Part 1)
  • Урок 109. 00:04:27
    Test for the mean. Independent Samples (Part 2)
  • Урок 110. 00:07:17
    Practical Example: Hypothesis Testing
  • Урок 111. 00:05:05
    Introduction to Programming
  • Урок 112. 00:05:12
    Why Python?
  • Урок 113. 00:03:30
    Why Jupyter?
  • Урок 114. 00:06:50
    Installing Python and Jupyter
  • Урок 115. 00:03:16
    Understanding Jupyter's Interface - the Notebook Dashboard
  • Урок 116. 00:06:16
    Prerequisites for Coding in the Jupyter Notebooks
  • Урок 117. 00:03:38
    Variables
  • Урок 118. 00:03:06
    Numbers and Boolean Values in Python
  • Урок 119. 00:05:41
    Python Strings
  • Урок 120. 00:03:24
    Using Arithmetic Operators in Python
  • Урок 121. 00:01:34
    The Double Equality Sign
  • Урок 122. 00:01:09
    How to Reassign Values
  • Урок 123. 00:01:35
    Add Comments
  • Урок 124. 00:00:50
    Understanding Line Continuation
  • Урок 125. 00:01:19
    Indexing Elements
  • Урок 126. 00:01:45
    Structuring with Indentation
  • Урок 127. 00:02:11
    Comparison Operators
  • Урок 128. 00:05:36
    Logical and Identity Operators
  • Урок 129. 00:03:02
    The IF Statement
  • Урок 130. 00:02:46
    The ELSE Statement
  • Урок 131. 00:05:35
    The ELIF Statement
  • Урок 132. 00:02:14
    A Note on Boolean Values
  • Урок 133. 00:02:03
    Defining a Function in Python
  • Урок 134. 00:03:50
    How to Create a Function with a Parameter
  • Урок 135. 00:02:37
    Defining a Function in Python - Part II
  • Урок 136. 00:01:50
    How to Use a Function within a Function
  • Урок 137. 00:03:07
    Conditional Statements and Functions
  • Урок 138. 00:01:18
    Functions Containing a Few Arguments
  • Урок 139. 00:03:57
    Built-in Functions in Python
  • Урок 140. 00:08:19
    Lists
  • Урок 141. 00:06:55
    Using Methods
  • Урок 142. 00:04:32
    List Slicing
  • Урок 143. 00:06:41
    Tuples
  • Урок 144. 00:08:28
    Dictionaries
  • Урок 145. 00:05:41
    For Loops
  • Урок 146. 00:05:11
    While Loops and Incrementing
  • Урок 147. 00:06:23
    Lists with the range() Function
  • Урок 148. 00:06:31
    Conditional Statements and Loops
  • Урок 149. 00:02:28
    Conditional Statements, Functions, and Loops
  • Урок 150. 00:06:22
    How to Iterate over Dictionaries
  • Урок 151. 00:05:01
    Object Oriented Programming
  • Урок 152. 00:01:06
    Modules and Packages
  • Урок 153. 00:02:48
    What is the Standard Library?
  • Урок 154. 00:04:05
    Importing Modules in Python
  • Урок 155. 00:01:28
    Introduction to Regression Analysis
  • Урок 156. 00:05:51
    The Linear Regression Model
  • Урок 157. 00:01:44
    Correlation vs Regression
  • Урок 158. 00:01:26
    Geometrical Representation of the Linear Regression Model
  • Урок 159. 00:04:40
    Python Packages Installation
  • Урок 160. 00:07:12
    First Regression in Python
  • Урок 161. 00:01:22
    Using Seaborn for Graphs
  • Урок 162. 00:05:48
    How to Interpret the Regression Table
  • Урок 163. 00:03:38
    Decomposition of Variability
  • Урок 164. 00:03:14
    What is the OLS?
  • Урок 165. 00:05:31
    R-Squared
  • Урок 166. 00:02:56
    Multiple Linear Regression
  • Урок 167. 00:06:01
    Adjusted R-Squared
  • Урок 168. 00:02:02
    Test for Significance of the Model (F-Test)
  • Урок 169. 00:02:22
    OLS Assumptions
  • Урок 170. 00:01:51
    A1: Linearity
  • Урок 171. 00:04:10
    A2: No Endogeneity
  • Урок 172. 00:05:48
    A3: Normality and Homoscedasticity
  • Урок 173. 00:03:32
    A4: No Autocorrelation
  • Урок 174. 00:03:27
    A5: No Multicollinearity
  • Урок 175. 00:06:44
    Dealing with Categorical Data - Dummy Variables
  • Урок 176. 00:03:30
    Making Predictions with the Linear Regression
  • Урок 177. 00:02:15
    What is sklearn and How is it Different from Other Packages
  • Урок 178. 00:01:57
    How are we Going to Approach this Section?
  • Урок 179. 00:05:39
    Simple Linear Regression with sklearn
  • Урок 180. 00:04:50
    Simple Linear Regression with sklearn - A StatsModels-like Summary Table
  • Урок 181. 00:03:11
    Multiple Linear Regression with sklearn
  • Урок 182. 00:04:46
    Calculating the Adjusted R-Squared in sklearn
  • Урок 183. 00:04:42
    Feature Selection (F-regression)
  • Урок 184. 00:02:11
    Creating a Summary Table with P-values
  • Урок 185. 00:05:39
    Feature Scaling (Standardization)
  • Урок 186. 00:05:23
    Feature Selection through Standardization of Weights
  • Урок 187. 00:03:54
    Predicting with the Standardized Coefficients
  • Урок 188. 00:02:43
    Underfitting and Overfitting
  • Урок 189. 00:06:55
    Train - Test Split Explained
  • Урок 190. 00:12:00
    Practical Example: Linear Regression (Part 1)
  • Урок 191. 00:06:13
    Practical Example: Linear Regression (Part 2)
  • Урок 192. 00:03:16
    Practical Example: Linear Regression (Part 3)
  • Урок 193. 00:08:11
    Practical Example: Linear Regression (Part 4)
  • Урок 194. 00:07:35
    Practical Example: Linear Regression (Part 5)
  • Урок 195. 00:01:20
    Introduction to Logistic Regression
  • Урок 196. 00:04:43
    A Simple Example in Python
  • Урок 197. 00:04:01
    Logistic vs Logit Function
  • Урок 198. 00:02:49
    Building a Logistic Regression
  • Урок 199. 00:02:27
    An Invaluable Coding Tip
  • Урок 200. 00:04:07
    Understanding Logistic Regression Tables
  • Урок 201. 00:04:31
    What do the Odds Actually Mean
  • Урок 202. 00:04:33
    Binary Predictors in a Logistic Regression
  • Урок 203. 00:03:22
    Calculating the Accuracy of the Model
  • Урок 204. 00:03:44
    Underfitting and Overfitting
  • Урок 205. 00:05:06
    Testing the Model
  • Урок 206. 00:03:42
    Introduction to Cluster Analysis
  • Урок 207. 00:04:32
    Some Examples of Clusters
  • Урок 208. 00:02:33
    Difference between Classification and Clustering
  • Урок 209. 00:03:20
    Math Prerequisites
  • Урок 210. 00:04:42
    K-Means Clustering
  • Урок 211. 00:07:49
    A Simple Example of Clustering
  • Урок 212. 00:02:51
    Clustering Categorical Data
  • Урок 213. 00:06:12
    How to Choose the Number of Clusters
  • Урок 214. 00:03:24
    Pros and Cons of K-Means Clustering
  • Урок 215. 00:04:33
    To Standardize or not to Standardize
  • Урок 216. 00:01:32
    Relationship between Clustering and Regression
  • Урок 217. 00:06:04
    Market Segmentation with Cluster Analysis (Part 1)
  • Урок 218. 00:06:59
    Market Segmentation with Cluster Analysis (Part 2)
  • Урок 219. 00:04:48
    How is Clustering Useful?
  • Урок 220. 00:03:40
    Types of Clustering
  • Урок 221. 00:05:22
    Dendrogram
  • Урок 222. 00:04:35
    Heatmaps
  • Урок 223. 00:03:38
    What is a Matrix?
  • Урок 224. 00:02:59
    Scalars and Vectors
  • Урок 225. 00:03:07
    Linear Algebra and Geometry
  • Урок 226. 00:05:10
    Arrays in Python - A Convenient Way To Represent Matrices
  • Урок 227. 00:03:01
    What is a Tensor?
  • Урок 228. 00:03:37
    Addition and Subtraction of Matrices
  • Урок 229. 00:02:02
    Errors when Adding Matrices
  • Урок 230. 00:05:14
    Transpose of a Matrix
  • Урок 231. 00:03:49
    Dot Product
  • Урок 232. 00:08:24
    Dot Product of Matrices
  • Урок 233. 00:10:11
    Why is Linear Algebra Useful?
  • Урок 234. 00:03:08
    What to Expect from this Part?
  • Урок 235. 00:04:10
    Introduction to Neural Networks
  • Урок 236. 00:02:55
    Training the Model
  • Урок 237. 00:03:44
    Types of Machine Learning
  • Урок 238. 00:03:09
    The Linear Model (Linear Algebraic Version)
  • Урок 239. 00:02:26
    The Linear Model with Multiple Inputs
  • Урок 240. 00:04:26
    The Linear model with Multiple Inputs and Multiple Outputs
  • Урок 241. 00:01:48
    Graphical Representation of Simple Neural Networks
  • Урок 242. 00:01:28
    What is the Objective Function?
  • Урок 243. 00:02:05
    Common Objective Functions: L2-norm Loss
  • Урок 244. 00:03:56
    Common Objective Functions: Cross-Entropy Loss
  • Урок 245. 00:06:34
    Optimization Algorithm: 1-Parameter Gradient Descent
  • Урок 246. 00:06:09
    Optimization Algorithm: n-Parameter Gradient Descent
  • Урок 247. 00:03:07
    Basic NN Example (Part 1)
  • Урок 248. 00:05:00
    Basic NN Example (Part 2)
  • Урок 249. 00:03:26
    Basic NN Example (Part 3)
  • Урок 250. 00:08:16
    Basic NN Example (Part 4)
  • Урок 251. 00:05:03
    How to Install TensorFlow 2.0
  • Урок 252. 00:03:29
    TensorFlow Outline and Comparison with Other Libraries
  • Урок 253. 00:02:34
    TensorFlow 1 vs TensorFlow 2
  • Урок 254. 00:00:59
    A Note on TensorFlow 2 Syntax
  • Урок 255. 00:02:35
    Types of File Formats Supporting TensorFlow
  • Урок 256. 00:05:49
    Outlining the Model with TensorFlow 2
  • Урок 257. 00:04:10
    Interpreting the Result and Extracting the Weights and Bias
  • Урок 258. 00:02:52
    Customizing a TensorFlow 2 Model
  • Урок 259. 00:01:54
    What is a Layer?
  • Урок 260. 00:02:19
    What is a Deep Net?
  • Урок 261. 00:04:59
    Digging into a Deep Net
  • Урок 262. 00:03:00
    Non-Linearities and their Purpose
  • Урок 263. 00:03:38
    Activation Functions
  • Урок 264. 00:03:25
    Activation Functions: Softmax Activation
  • Урок 265. 00:03:13
    Backpropagation
  • Урок 266. 00:03:03
    Backpropagation Picture
  • Урок 267. 00:03:52
    What is Overfitting?
  • Урок 268. 00:01:53
    Underfitting and Overfitting for Classification
  • Урок 269. 00:03:23
    What is Validation?
  • Урок 270. 00:02:31
    Training, Validation, and Test Datasets
  • Урок 271. 00:03:08
    N-Fold Cross Validation
  • Урок 272. 00:04:55
    Early Stopping or When to Stop Training
  • Урок 273. 00:02:33
    What is Initialization?
  • Урок 274. 00:02:48
    Types of Simple Initializations
  • Урок 275. 00:02:46
    State-of-the-Art Method - (Xavier) Glorot Initialization
  • Урок 276. 00:03:25
    Stochastic Gradient Descent
  • Урок 277. 00:02:03
    Problems with Gradient Descent
  • Урок 278. 00:02:31
    Momentum
  • Урок 279. 00:04:26
    Learning Rate Schedules, or How to Choose the Optimal Learning Rate
  • Урок 280. 00:01:33
    Learning Rate Schedules Visualized
  • Урок 281. 00:04:09
    Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
  • Урок 282. 00:02:40
    Adam (Adaptive Moment Estimation)
  • Урок 283. 00:02:52
    Preprocessing Introduction
  • Урок 284. 00:01:18
    Types of Basic Preprocessing
  • Урок 285. 00:04:32
    Standardization
  • Урок 286. 00:02:16
    Preprocessing Categorical Data
  • Урок 287. 00:03:40
    Binary and One-Hot Encoding
  • Урок 288. 00:02:26
    MNIST: The Dataset
  • Урок 289. 00:02:45
    MNIST: How to Tackle the MNIST
  • Урок 290. 00:02:12
    MNIST: Importing the Relevant Packages and Loading the Data
  • Урок 291. 00:04:44
    MNIST: Preprocess the Data - Create a Validation Set and Scale It
  • Урок 292. 00:06:31
    MNIST: Preprocess the Data - Shuffle and Batch
  • Урок 293. 00:04:55
    MNIST: Outline the Model
  • Урок 294. 00:02:06
    MNIST: Select the Loss and the Optimizer
  • Урок 295. 00:05:39
    MNIST: Learning
  • Урок 296. 00:03:57
    MNIST: Testing the Model
  • Урок 297. 00:07:55
    Business Case: Exploring the Dataset and Identifying Predictors
  • Урок 298. 00:01:32
    Business Case: Outlining the Solution
  • Урок 299. 00:03:40
    Business Case: Balancing the Dataset
  • Урок 300. 00:11:33
    Business Case: Preprocessing the Data
  • Урок 301. 00:03:24
    Business Case: Load the Preprocessed Data
  • Урок 302. 00:04:16
    Business Case: Learning and Interpreting the Result
  • Урок 303. 00:05:02
    Business Case: Setting an Early Stopping Mechanism
  • Урок 304. 00:01:24
    Business Case: Testing the Model
  • Урок 305. 00:03:42
    Summary on What You've Learned
  • Урок 306. 00:01:48
    What's Further out there in terms of Machine Learning
  • Урок 307. 00:04:57
    An overview of CNNs
  • Урок 308. 00:02:51
    An Overview of RNNs
  • Урок 309. 00:03:53
    An Overview of non-NN Approaches
  • Урок 310. 00:02:21
    How to Install TensorFlow 1
  • Урок 311. 00:03:47
    TensorFlow Intro
  • Урок 312. 00:01:41
    Actual Introduction to TensorFlow
  • Урок 313. 00:02:39
    Types of File Formats, supporting Tensors
  • Урок 314. 00:06:06
    Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
  • Урок 315. 00:03:42
    Basic NN Example with TF: Loss Function and Gradient Descent
  • Урок 316. 00:06:06
    Basic NN Example with TF: Model Output
  • Урок 317. 00:02:27
    MNIST: What is the MNIST Dataset?
  • Урок 318. 00:02:49
    MNIST: How to Tackle the MNIST
  • Урок 319. 00:01:35
    MNIST: Relevant Packages
  • Урок 320. 00:06:52
    MNIST: Model Outline
  • Урок 321. 00:02:40
    MNIST: Loss and Optimization Algorithm
  • Урок 322. 00:04:19
    Calculating the Accuracy of the Model
  • Урок 323. 00:02:09
    MNIST: Batching and Early Stopping
  • Урок 324. 00:07:36
    MNIST: Learning
  • Урок 325. 00:06:12
    MNIST: Results and Testing
  • Урок 326. 00:07:56
    Business Case: Getting Acquainted with the Dataset
  • Урок 327. 00:01:58
    Business Case: Outlining the Solution
  • Урок 328. 00:03:40
    The Importance of Working with a Balanced Dataset
  • Урок 329. 00:11:36
    Business Case: Preprocessing
  • Урок 330. 00:06:38
    Creating a Data Provider
  • Урок 331. 00:05:36
    Business Case: Model Outline
  • Урок 332. 00:05:11
    Business Case: Optimization
  • Урок 333. 00:02:06
    Business Case: Interpretation
  • Урок 334. 00:02:05
    Business Case: Testing the Model
  • Урок 335. 00:03:52
    Business Case: A Comment on the Homework
  • Урок 336. 00:04:44
    What are Data, Servers, Clients, Requests, and Responses
  • Урок 337. 00:07:06
    What are Data Connectivity, APIs, and Endpoints?
  • Урок 338. 00:08:06
    Taking a Closer Look at APIs
  • Урок 339. 00:04:22
    Communication between Software Products through Text Files
  • Урок 340. 00:05:26
    Software Integration - Explained
  • Урок 341. 00:04:09
    Game Plan for this Python, SQL, and Tableau Business Exercise
  • Урок 342. 00:02:49
    The Business Task
  • Урок 343. 00:03:19
    Introducing the Data Set
  • Урок 344. 00:03:24
    Importing the Absenteeism Data in Python
  • Урок 345. 00:05:54
    Checking the Content of the Data Set
  • Урок 346. 00:03:28
    Introduction to Terms with Multiple Meanings
  • Урок 347. 00:02:18
    Using a Statistical Approach towards the Solution to the Exercise
  • Урок 348. 00:06:28
    Dropping a Column from a DataFrame in Python
  • Урок 349. 00:05:05
    Analyzing the Reasons for Absence
  • Урок 350. 00:08:38
    Obtaining Dummies from a Single Feature
  • Урок 351. 00:01:29
    More on Dummy Variables: A Statistical Perspective
  • Урок 352. 00:08:36
    Classifying the Various Reasons for Absence
  • Урок 353. 00:04:36
    Using .concat() in Python
  • Урок 354. 00:01:44
    Reordering Columns in a Pandas DataFrame in Python
  • Урок 355. 00:02:53
    Creating Checkpoints while Coding in Jupyter
  • Урок 356. 00:07:50
    Analyzing the Dates from the Initial Data Set
  • Урок 357. 00:07:01
    Extracting the Month Value from the "Date" Column
  • Урок 358. 00:03:37
    Extracting the Day of the Week from the "Date" Column
  • Урок 359. 00:03:18
    Analyzing Several "Straightforward" Columns for this Exercise
  • Урок 360. 00:04:39
    Working on "Education", "Children", and "Pets"
  • Урок 361. 00:02:00
    Final Remarks of this Section
  • Урок 362. 00:03:21
    Exploring the Problem with a Machine Learning Mindset
  • Урок 363. 00:06:33
    Creating the Targets for the Logistic Regression
  • Урок 364. 00:02:42
    Selecting the Inputs for the Logistic Regression
  • Урок 365. 00:03:27
    Standardizing the Data
  • Урок 366. 00:06:13
    Splitting the Data for Training and Testing
  • Урок 367. 00:05:40
    Fitting the Model and Assessing its Accuracy
  • Урок 368. 00:05:17
    Creating a Summary Table with the Coefficients and Intercept
  • Урок 369. 00:06:15
    Interpreting the Coefficients for Our Problem
  • Урок 370. 00:04:13
    Standardizing only the Numerical Variables (Creating a Custom Scaler)
  • Урок 371. 00:05:11
    Interpreting the Coefficients of the Logistic Regression
  • Урок 372. 00:04:03
    Backward Elimination or How to Simplify Your Model
  • Урок 373. 00:04:44
    Testing the Model We Created
  • Урок 374. 00:04:07
    Saving the Model and Preparing it for Deployment
  • Урок 375. 00:04:05
    Preparing the Deployment of the Model through a Module
  • Урок 376. 00:03:51
    Deploying the 'absenteeism_module' - Part I
  • Урок 377. 00:06:24
    Deploying the 'absenteeism_module' - Part II
  • Урок 378. 00:08:50
    Analyzing Age vs Probability in Tableau
  • Урок 379. 00:07:50
    Analyzing Reasons vs Probability in Tableau
  • Урок 380. 00:06:01
    Analyzing Transportation Expense vs Probability in Tableau
  • Урок 381. 00:09:03
    Using the .format() Method
  • Урок 382. 00:04:18
    Iterating Over Range Objects
  • Урок 383. 00:06:00
    Introduction to Nested For Loops
  • Урок 384. 00:05:38
    Triple Nested For Loops
  • Урок 385. 00:08:31
    List Comprehensions
  • Урок 386. 00:07:01
    Anonymous (Lambda) Functions
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