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[] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp

  • 收录时间:2019-06-13 23:32:41
  • 文件大小:14GB
  • 下载次数:15
  • 最近下载:2020-09-15 08:08:06
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文件列表

  1. 16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 160MB
  2. 12. Probability Distributions/29. A Practical Example of Probability Distributions.mp4 158MB
  3. 11. Bayesian Inference/22. A Practical Example of Bayesian Inference.mp4 155MB
  4. 40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4 144MB
  5. 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 138MB
  6. 10. Combinatorics/20. A Practical Example of Combinatorics.mp4 134MB
  7. 3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 127MB
  8. 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 125MB
  9. 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 124MB
  10. 53. Software Integration/5. Taking a Closer Look at APIs.mp4 116MB
  11. 20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 113MB
  12. 2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 109MB
  13. 53. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4 104MB
  14. 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 104MB
  15. 51. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 103MB
  16. 19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 103MB
  17. 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 99MB
  18. 13. Probability in Other Fields/1. Probability in Finance.mp4 99MB
  19. 35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp4 97MB
  20. 12. Probability Distributions/3. Types of Probability Distributions.mp4 92MB
  21. 20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4 92MB
  22. 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 90MB
  23. 51. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 88MB
  24. 36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 87MB
  25. 9. Part 2 Probability/1. The Basic Probability Formula.mp4 86MB
  26. 2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4 81MB
  27. 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 81MB
  28. 55. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 81MB
  29. 12. Probability Distributions/15. Characteristics of Continuous Distributions.mp4 80MB
  30. 18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 78MB
  31. 13. Probability in Other Fields/2. Probability in Statistics.mp4 77MB
  32. 51. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4 76MB
  33. 9. Part 2 Probability/3. Computing Expected Values.mp4 76MB
  34. 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 76MB
  35. 22. Part 4 Introduction to Python/3. Why Python.mp4 75MB
  36. 55. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 75MB
  37. 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 74MB
  38. 12. Probability Distributions/1. Fundamentals of Probability Distributions.mp4 73MB
  39. 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 73MB
  40. 53. Software Integration/9. Software Integration - Explained.mp4 73MB
  41. 15. Statistics - Descriptive Statistics/1. Types of Data.mp4 73MB
  42. 37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 72MB
  43. 18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp4 70MB
  44. 21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 69MB
  45. 53. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4 69MB
  46. 2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 68MB
  47. 12. Probability Distributions/11. Discrete Distributions The Binomial Distribution.mp4 66MB
  48. 2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 65MB
  49. 13. Probability in Other Fields/3. Probability in Data Science.mp4 63MB
  50. 17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4 63MB
  51. 50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 63MB
  52. 1. Part 1 Introduction/2. What Does the Course Cover.mp4 62MB
  53. 55. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4 62MB
  54. 55. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4 62MB
  55. 9. Part 2 Probability/5. Frequency.mp4 62MB
  56. 17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 62MB
  57. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 61MB
  58. 53. Software Integration/7. Communication between Software Products through Text Files.mp4 60MB
  59. 11. Bayesian Inference/20. Bayes' Law.mp4 60MB
  60. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 59MB
  61. 58. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4 59MB
  62. 18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp4 59MB
  63. 9. Part 2 Probability/7. Events and Their Complements.mp4 59MB
  64. 52. Deep Learning - Conclusion/3. An overview of CNNs.mp4 59MB
  65. 22. Part 4 Introduction to Python/1. Introduction to Programming.mp4 59MB
  66. 12. Probability Distributions/13. Discrete Distributions The Poisson Distribution.mp4 58MB
  67. 14. Part 3 Statistics/1. Population and Sample.mp4 58MB
  68. 35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp4 58MB
  69. 32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.mp4 57MB
  70. 10. Combinatorics/11. Solving Combinations.mp4 57MB
  71. 55. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 57MB
  72. 11. Bayesian Inference/7. Union of Sets.mp4 57MB
  73. 18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp4 57MB
  74. 58. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4 57MB
  75. 50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 56MB
  76. 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 56MB
  77. 35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp4 56MB
  78. 20. Statistics - Hypothesis Testing/10. p-value.mp4 56MB
  79. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp4 56MB
  80. 42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 56MB
  81. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp4 55MB
  82. 15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 54MB
  83. 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 54MB
  84. 57. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4 54MB
  85. 20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 54MB
  86. 2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4 54MB
  87. 11. Bayesian Inference/1. Sets and Events.mp4 53MB
  88. 37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 53MB
  89. 51. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4 53MB
  90. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp4 53MB
  91. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp4 52MB
  92. 54. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 52MB
  93. 38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 52MB
  94. 22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp4 51MB
  95. 49. Deep Learning - Preprocessing/3. Standardization.mp4 51MB
  96. 15. Statistics - Descriptive Statistics/22. Variance.mp4 51MB
  97. 20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 50MB
  98. 18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 50MB
  99. 17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 50MB
  100. 40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp4 50MB
  101. 32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.mp4 50MB
  102. 40. Part 6 Mathematics/15. Dot Product of Matrices.mp4 49MB
  103. 34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp4 49MB
  104. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4 49MB
  105. 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 49MB
  106. 12. Probability Distributions/17. Continuous Distributions The Normal Distribution.mp4 48MB
  107. 12. Probability Distributions/19. Continuous Distributions The Standard Normal Distribution.mp4 48MB
  108. 17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4 48MB
  109. 55. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 48MB
  110. 44. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4 48MB
  111. 12. Probability Distributions/27. Continuous Distributions The Logistic Distribution.mp4 47MB
  112. 50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4 47MB
  113. 35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp4 46MB
  114. 11. Bayesian Inference/13. The Conditional Probability Formula.mp4 46MB
  115. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp4 46MB
  116. 11. Bayesian Inference/3. Ways Sets Can Interact.mp4 45MB
  117. 15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4 45MB
  118. 42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 45MB
  119. 52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 45MB
  120. 32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.mp4 45MB
  121. 32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.mp4 45MB
  122. 39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 45MB
  123. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4 44MB
  124. 22. Part 4 Introduction to Python/5. Why Jupyter.mp4 44MB
  125. 38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4 44MB
  126. 20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 44MB
  127. 50. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4 44MB
  128. 10. Combinatorics/9. Solving Variations without Repetition.mp4 43MB
  129. 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 43MB
  130. 42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 43MB
  131. 11. Bayesian Inference/18. The Multiplication Law.mp4 43MB
  132. 5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 43MB
  133. 10. Combinatorics/3. Permutations and How to Use Them.mp4 43MB
  134. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp4 43MB
  135. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp4 42MB
  136. 51. Deep Learning - Business Case Example/8. Business Case Optimization.mp4 42MB
  137. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 41MB
  138. 32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.mp4 41MB
  139. 54. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4 41MB
  140. 58. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4 41MB
  141. 32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.mp4 41MB
  142. 55. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 41MB
  143. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4 40MB
  144. 20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 40MB
  145. 12. Probability Distributions/25. Continuous Distributions The Exponential Distribution.mp4 40MB
  146. 15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4 40MB
  147. 52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4 40MB
  148. 55. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 40MB
  149. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4 40MB
  150. 51. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4 39MB
  151. 42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 39MB
  152. 10. Combinatorics/17. Combinatorics in Real-Life The Lottery.mp4 39MB
  153. 54. Case Study - What's Next in the Course/2. The Business Task.mp4 39MB
  154. 34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp4 39MB
  155. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp4 39MB
  156. 55. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 39MB
  157. 10. Combinatorics/13. Symmetry of Combinations.mp4 39MB
  158. 10. Combinatorics/19. A Recap of Combinatorics.mp4 39MB
  159. 44. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 38MB
  160. 15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 38MB
  161. 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 38MB
  162. 42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 38MB
  163. 40. Part 6 Mathematics/13. Transpose of a Matrix.mp4 38MB
  164. 38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4 38MB
  165. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4 37MB
  166. 44. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4 37MB
  167. 42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 37MB
  168. 15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4 37MB
  169. 5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 37MB
  170. 20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp4 36MB
  171. 51. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.vtt 36MB
  172. 51. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4 36MB
  173. 37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 36MB
  174. 10. Combinatorics/5. Simple Operations with Factorials.mp4 36MB
  175. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp4 36MB
  176. 18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp4 35MB
  177. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 35MB
  178. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 35MB
  179. 11. Bayesian Inference/15. The Law of Total Probability.mp4 35MB
  180. 34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp4 35MB
  181. 11. Bayesian Inference/11. Dependence and Independence of Sets.mp4 35MB
  182. 34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp4 35MB
  183. 36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 35MB
  184. 12. Probability Distributions/9. Discrete Distributions The Bernoulli Distribution.mp4 34MB
  185. 10. Combinatorics/7. Solving Variations with Repetition.mp4 34MB
  186. 40. Part 6 Mathematics/3. Scalars and Vectors.mp4 34MB
  187. 30. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 34MB
  188. 40. Part 6 Mathematics/1. What is a matrix.mp4 34MB
  189. 26. Python - Conditional Statements/4. The ELIF Statement.mp4 33MB
  190. 10. Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp4 33MB
  191. 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 33MB
  192. 46. Deep Learning - Overfitting/3. What is Validation.mp4 33MB
  193. 40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp4 33MB
  194. 44. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 33MB
  195. 36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4 32MB
  196. 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 32MB
  197. 18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp4 32MB
  198. 34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 32MB
  199. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp4 32MB
  200. 41. Part 7 Deep Learning/1. What to Expect from this Part.mp4 31MB
  201. 46. Deep Learning - Overfitting/1. What is Overfitting.mp4 31MB
  202. 34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp4 31MB
  203. 28. Python - Sequences/5. List Slicing.mp4 31MB
  204. 23. Python - Variables and Data Types/5. Python Strings.mp4 31MB
  205. 22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 31MB
  206. 36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4 31MB
  207. 38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4 30MB
  208. 25. Python - Other Python Operators/3. Logical and Identity Operators.mp4 30MB
  209. 20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 30MB
  210. 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 30MB
  211. 39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 30MB
  212. 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 30MB
  213. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 30MB
  214. 55. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 30MB
  215. 34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp4 30MB
  216. 15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4 29MB
  217. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 29MB
  218. 39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 29MB
  219. 49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 29MB
  220. 18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4 29MB
  221. 42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 29MB
  222. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp4 29MB
  223. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4 29MB
  224. 42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 28MB
  225. 32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.mp4 28MB
  226. 55. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 28MB
  227. 55. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4 28MB
  228. 49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 28MB
  229. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 28MB
  230. 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp4 28MB
  231. 15. Statistics - Descriptive Statistics/27. Covariance.mp4 27MB
  232. 38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 27MB
  233. 34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp4 27MB
  234. 12. Probability Distributions/21. Continuous Distributions The Students' T Distribution.mp4 27MB
  235. 36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 27MB
  236. 11. Bayesian Inference/5. Intersection of Sets.mp4 27MB
  237. 18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp4 27MB
  238. 40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 27MB
  239. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 26MB
  240. 12. Probability Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp4 26MB
  241. 34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp4 26MB
  242. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 26MB
  243. 50. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4 26MB
  244. 15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4 26MB
  245. 11. Bayesian Inference/16. The Additive Rule.mp4 26MB
  246. 51. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4 26MB
  247. 55. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 26MB
  248. 57. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4 25MB
  249. 11. Bayesian Inference/9. Mutually Exclusive Sets.mp4 25MB
  250. 23. Python - Variables and Data Types/1. Variables.mp4 25MB
  251. 52. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 25MB
  252. 46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4 25MB
  253. 42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4 25MB
  254. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4 25MB
  255. 46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4 25MB
  256. 28. Python - Sequences/7. Dictionaries.mp4 25MB
  257. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp4 25MB
  258. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4 24MB
  259. 12. Probability Distributions/7. Discrete Distributions The Uniform Distribution.mp4 24MB
  260. 46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 24MB
  261. 40. Part 6 Mathematics/14. Dot Product.mp4 24MB
  262. 27. Python - Python Functions/2. How to Create a Function with a Parameter.mp4 24MB
  263. 35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp4 24MB
  264. 42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 23MB
  265. 55. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp4 23MB
  266. 36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4 23MB
  267. 17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp4 23MB
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  508. 55. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.vtt 9KB
  509. 50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.vtt 9KB
  510. 55. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.vtt 9KB
  511. 58. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.vtt 9KB
  512. 13. Probability in Other Fields/1. Probability in Finance.vtt 9KB
  513. 18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.vtt 9KB
  514. 58. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.vtt 8KB
  515. 34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.vtt 8KB
  516. 12. Probability Distributions/3. Types of Probability Distributions.vtt 8KB
  517. 36. Advanced Statistical Methods - Logistic Regression/16.1 Bank_data_testing.csv.csv 8KB
  518. 38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.vtt 8KB
  519. 38. Advanced Statistical Methods - K-Means Clustering/3.1 Countries_exercise.csv.csv 8KB
  520. 38. Advanced Statistical Methods - K-Means Clustering/7.1 Countries_exercise.csv.csv 8KB
  521. 40. Part 6 Mathematics/15. Dot Product of Matrices.vtt 8KB
  522. 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).vtt 8KB
  523. 50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.vtt 8KB
  524. 3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt 8KB
  525. 9. Part 2 Probability/1. The Basic Probability Formula.vtt 8KB