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The Data Science Course 2020 Complete Data Science Bootcamp

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The Data Science Course 2020 Complete Data Science Bootcamp

文件列表
  1. 16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4160.46MB
  2. 1. Part 1 Introduction/2. What Does the Course Cover.mp462.25MB
  3. 2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp481.42MB
  4. 2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp453.56MB
  5. 2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp464.51MB
  6. 2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4108.98MB
  7. 2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp467.74MB
  8. 3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4126.88MB
  9. 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp481.18MB
  10. 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4138.31MB
  11. 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp429.93MB
  12. 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp475.51MB
  13. 5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp422.04MB
  14. 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp489.94MB
  15. 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp429.54MB
  16. 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4111.66MB
  17. 5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp442.78MB
  18. 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp499.33MB
  19. 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4125.15MB
  20. 5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp436.81MB
  21. 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4103.52MB
  22. 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp454.38MB
  23. 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp472.85MB
  24. 9. Part 2 Probability/1. The Basic Probability Formula.mp485.91MB
  25. 9. Part 2 Probability/3. Computing Expected Values.mp475.68MB
  26. 9. Part 2 Probability/5. Frequency.mp461.74MB
  27. 9. Part 2 Probability/7. Events and Their Complements.mp459.16MB
  28. 10. Probability - Combinatorics/1. Fundamentals of Combinatorics.mp416.22MB
  29. 10. Probability - Combinatorics/3. Permutations and How to Use Them.mp442.73MB
  30. 10. Probability - Combinatorics/5. Simple Operations with Factorials.mp436.12MB
  31. 10. Probability - Combinatorics/7. Solving Variations with Repetition.mp434.01MB
  32. 10. Probability - Combinatorics/9. Solving Variations without Repetition.mp443.14MB
  33. 10. Probability - Combinatorics/11. Solving Combinations.mp457.35MB
  34. 10. Probability - Combinatorics/13. Symmetry of Combinations.mp440.3MB
  35. 10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp433.15MB
  36. 10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.mp441.3MB
  37. 10. Probability - Combinatorics/19. A Recap of Combinatorics.mp438.5MB
  38. 10. Probability - Combinatorics/20. A Practical Example of Combinatorics.mp4134.31MB
  39. 11. Probability - Bayesian Inference/1. Sets and Events.mp453.46MB
  40. 11. Probability - Bayesian Inference/3. Ways Sets Can Interact.mp447.42MB
  41. 11. Probability - Bayesian Inference/5. Intersection of Sets.mp426.97MB
  42. 11. Probability - Bayesian Inference/7. Union of Sets.mp457.19MB
  43. 11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.mp425.39MB
  44. 11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.mp434.79MB
  45. 11. Probability - Bayesian Inference/13. The Conditional Probability Formula.mp445.87MB
  46. 11. Probability - Bayesian Inference/15. The Law of Total Probability.mp434.93MB
  47. 11. Probability - Bayesian Inference/16. The Additive Rule.mp426.97MB
  48. 11. Probability - Bayesian Inference/18. The Multiplication Law.mp449.02MB
  49. 11. Probability - Bayesian Inference/20. Bayes' Law.mp449.94MB
  50. 11. Probability - Bayesian Inference/22. A Practical Example of Bayesian Inference.mp4145.13MB
  51. 12. Probability - Distributions/1. Fundamentals of Probability Distributions.mp473.4MB
  52. 12. Probability - Distributions/3. Types of Probability Distributions.mp491.58MB
  53. 12. Probability - Distributions/5. Characteristics of Discrete Distributions.mp422.7MB
  54. 12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.mp424.4MB
  55. 12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.mp434.14MB
  56. 12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.mp468.83MB
  57. 12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.mp455.75MB
  58. 12. Probability - Distributions/15. Characteristics of Continuous Distributions.mp484.12MB
  59. 12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.mp448.25MB
  60. 12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.mp447.91MB
  61. 12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.mp427.18MB
  62. 12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp426.34MB
  63. 12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.mp440.23MB
  64. 12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.mp447.05MB
  65. 12. Probability - Distributions/29. A Practical Example of Probability Distributions.mp4157.83MB
  66. 13. Probability - Probability in Other Fields/1. Probability in Finance.mp499.06MB
  67. 13. Probability - Probability in Other Fields/2. Probability in Statistics.mp477.28MB
  68. 13. Probability - Probability in Other Fields/3. Probability in Data Science.mp463.49MB
  69. 14. Part 3 Statistics/1. Population and Sample.mp458.11MB
  70. 15. Statistics - Descriptive Statistics/1. Types of Data.mp472.52MB
  71. 15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp454.38MB
  72. 15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp438.46MB
  73. 15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp425.85MB
  74. 15. Statistics - Descriptive Statistics/11. The Histogram.mp413.78MB
  75. 15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp439.81MB
  76. 15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp437.12MB
  77. 15. Statistics - Descriptive Statistics/19. Skewness.mp419.4MB
  78. 15. Statistics - Descriptive Statistics/22. Variance.mp450.96MB
  79. 15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp445.12MB
  80. 15. Statistics - Descriptive Statistics/27. Covariance.mp427.49MB
  81. 15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp429.39MB
  82. 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp449.03MB
  83. 17. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp415.5MB
  84. 17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp461.59MB
  85. 17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp449.86MB
  86. 17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp422.51MB
  87. 17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp462.88MB
  88. 17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp422.77MB
  89. 17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp447.83MB
  90. 18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp449.98MB
  91. 18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp478.2MB
  92. 18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp457.03MB
  93. 18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp435.44MB
  94. 18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp432.21MB
  95. 18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp459.16MB
  96. 18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp470.47MB
  97. 18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp428.76MB
  98. 18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp426.82MB
  99. 18. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).mp419.94MB
  100. 19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4102.66MB
  101. 20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp492.05MB
  102. 20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp482.62MB
  103. 20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp443.94MB
  104. 20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp454.23MB
  105. 20. Statistics - Hypothesis Testing/10. p-value.mp455.87MB
  106. 20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp440.24MB
  107. 20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp450.37MB
  108. 20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp433.94MB
  109. 20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp436.4MB
  110. 21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp469.48MB
  111. 22. Part 4 Introduction to Python/1. Introduction to Programming.mp458.54MB
  112. 22. Part 4 Introduction to Python/3. Why Python.mp475.07MB
  113. 22. Part 4 Introduction to Python/5. Why Jupyter.mp444.31MB
  114. 22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp451MB
  115. 22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp413.8MB
  116. 22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp430.58MB
  117. 22. Part 4 Introduction to Python/11. Python 2 vs Python 3.mp411.27MB
  118. 23. Python - Variables and Data Types/1. Variables.mp425.31MB
  119. 23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp417.06MB
  120. 23. Python - Variables and Data Types/5. Python Strings.mp450.65MB
  121. 24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp418.92MB
  122. 24. Python - Basic Python Syntax/3. The Double Equality Sign.mp45.99MB
  123. 24. Python - Basic Python Syntax/5. How to Reassign Values.mp44.01MB
  124. 24. Python - Basic Python Syntax/7. Add Comments.mp411.27MB
  125. 24. Python - Basic Python Syntax/9. Understanding Line Continuation.mp42.36MB
  126. 24. Python - Basic Python Syntax/10. Indexing Elements.mp45.94MB
  127. 24. Python - Basic Python Syntax/12. Structuring with Indentation.mp413.15MB
  128. 25. Python - Other Python Operators/1. Comparison Operators.mp410.18MB
  129. 25. Python - Other Python Operators/3. Logical and Identity Operators.mp430.06MB
  130. 26. Python - Conditional Statements/1. The IF Statement.mp423.23MB
  131. 26. Python - Conditional Statements/3. The ELSE Statement.mp423.28MB
  132. 26. Python - Conditional Statements/4. The ELIF Statement.mp453.34MB
  133. 26. Python - Conditional Statements/5. A Note on Boolean Values.mp419.99MB
  134. 27. Python - Python Functions/1. Defining a Function in Python.mp414.76MB
  135. 27. Python - Python Functions/2. How to Create a Function with a Parameter.mp438.1MB
  136. 27. Python - Python Functions/3. Defining a Function in Python - Part II.mp425.25MB
  137. 27. Python - Python Functions/4. How to Use a Function within a Function.mp48.14MB
  138. 27. Python - Python Functions/5. Conditional Statements and Functions.mp415.68MB
  139. 27. Python - Python Functions/6. Functions Containing a Few Arguments.mp414.71MB
  140. 27. Python - Python Functions/7. Built-in Functions in Python.mp422.01MB
  141. 28. Python - Sequences/1. Lists.mp437.8MB
  142. 28. Python - Sequences/3. Using Methods.mp437.59MB
  143. 28. Python - Sequences/5. List Slicing.mp430.76MB
  144. 28. Python - Sequences/6. Tuples.mp429.5MB
  145. 28. Python - Sequences/7. Dictionaries.mp441.69MB
  146. 29. Python - Iterations/1. For Loops.mp423.59MB
  147. 29. Python - Iterations/3. While Loops and Incrementing.mp428.43MB
  148. 29. Python - Iterations/4. Lists with the range() Function.mp425.8MB
  149. 29. Python - Iterations/6. Conditional Statements and Loops.mp427.76MB
  150. 29. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp49.48MB
  151. 29. Python - Iterations/8. How to Iterate over Dictionaries.mp429.65MB
  152. 30. Python - Advanced Python Tools/1. Object Oriented Programming.mp433.59MB
  153. 30. Python - Advanced Python Tools/3. Modules and Packages.mp48.51MB
  154. 30. Python - Advanced Python Tools/5. What is the Standard Library.mp418.03MB
  155. 30. Python - Advanced Python Tools/7. Importing Modules in Python.mp419.94MB
  156. 31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp417.32MB
  157. 32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.mp457.37MB
  158. 32. Advanced Statistical Methods - Linear regression with StatsModels/3. Correlation vs Regression.mp414.74MB
  159. 32. Advanced Statistical Methods - Linear regression with StatsModels/5. Geometrical Representation of the Linear Regression Model.mp45.12MB
  160. 32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.mp440.58MB
  161. 32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.mp444.56MB
  162. 32. Advanced Statistical Methods - Linear regression with StatsModels/10. Using Seaborn for Graphs.mp412.24MB
  163. 32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.mp444.64MB
  164. 32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.mp449.66MB
  165. 32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.mp428.31MB
  166. 32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.mp441.03MB
  167. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.mp421.52MB
  168. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp454.83MB
  169. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/6. Test for Significance of the Model (F-Test).mp416.42MB
  170. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.mp421.85MB
  171. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/9. A1 Linearity.mp412.6MB
  172. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp435.67MB
  173. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp442.7MB
  174. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp431.52MB
  175. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp428.7MB
  176. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp455.66MB
  177. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp424.69MB
  178. 34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp427.25MB
  179. 34. Advanced Statistical Methods - Linear Regression with sklearn/2. How are Going to Approach this Section.mp419.41MB
  180. 34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp434.77MB
  181. 34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp432MB
  182. 34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.mp420.07MB
  183. 34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp430.89MB
  184. 34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp429.51MB
  185. 34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with p-values.mp412.31MB
  186. 34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp439.08MB
  187. 34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp434.9MB
  188. 34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp425.97MB
  189. 34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp416.95MB
  190. 34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp449.17MB
  191. 35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp497.09MB
  192. 35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp446MB
  193. 35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp423.7MB
  194. 35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp456.05MB
  195. 35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp457.88MB
  196. 36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp427.06MB
  197. 36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp434.7MB
  198. 36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp486.49MB
  199. 36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp417.1MB
  200. 36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp423.06MB
  201. 36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp430.55MB
  202. 36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp432.28MB
  203. 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp438.44MB
  204. 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp432.86MB
  205. 36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp422.29MB
  206. 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp432.27MB
  207. 37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp453.43MB
  208. 37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp471.54MB
  209. 37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp436.16MB
  210. 37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp414.56MB
  211. 38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp427.29MB
  212. 38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp451.83MB
  213. 38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp421.24MB
  214. 38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp444.14MB
  215. 38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp437.7MB
  216. 38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp430.1MB
  217. 38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp49.93MB
  218. 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp443.01MB
  219. 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp456.11MB
  220. 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp474.45MB
  221. 39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp444.57MB
  222. 39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp429.06MB
  223. 39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp429.63MB
  224. 40. Part 6 Mathematics/1. What is a matrix.mp433.59MB
  225. 40. Part 6 Mathematics/3. Scalars and Vectors.mp433.85MB
  226. 40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp449.79MB
  227. 40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp426.67MB
  228. 40. Part 6 Mathematics/8. What is a Tensor.mp422.53MB
  229. 40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp432.61MB
  230. 40. Part 6 Mathematics/12. Errors when Adding Matrices.mp411.17MB
  231. 40. Part 6 Mathematics/13. Transpose of a Matrix.mp438.07MB
  232. 40. Part 6 Mathematics/14. Dot Product.mp423.99MB
  233. 40. Part 6 Mathematics/15. Dot Product of Matrices.mp449.44MB
  234. 40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4144.33MB
  235. 41. Part 7 Deep Learning/1. What to Expect from this Part.mp431.1MB
  236. 42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp442.93MB
  237. 42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp428.72MB
  238. 42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp445.1MB
  239. 42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp428.44MB
  240. 42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp425.11MB
  241. 42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp438.31MB
  242. 42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp422.64MB
  243. 42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp417.92MB
  244. 42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp423.27MB
  245. 42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp437.25MB
  246. 42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp455.62MB
  247. 42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp439.42MB
  248. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp420.59MB
  249. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp434.94MB
  250. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp424.41MB
  251. 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp461.14MB
  252. 44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.mp438.76MB
  253. 44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.mp433.52MB
  254. 44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.mp421.99MB
  255. 44. Deep Learning - TensorFlow 2.0 Introduction/4. A Note on TensorFlow 2 Syntax.mp46.76MB
  256. 44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.mp416.4MB
  257. 44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.mp434.69MB
  258. 44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.mp430.28MB
  259. 44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.mp422.91MB
  260. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp412.5MB
  261. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp429.54MB
  262. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp459.37MB
  263. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp427.68MB
  264. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp425.1MB
  265. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp425.92MB
  266. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp434.95MB
  267. 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp419.51MB
  268. 46. Deep Learning - Overfitting/1. What is Overfitting.mp431.08MB
  269. 46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp425.08MB
  270. 46. Deep Learning - Overfitting/3. What is Validation.mp432.71MB
  271. 46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp425.19MB
  272. 46. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp420.7MB
  273. 46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp424.17MB
  274. 47. Deep Learning - Initialization/1. What is Initialization.mp421.77MB
  275. 47. Deep Learning - Initialization/2. Types of Simple Initializations.mp414.31MB
  276. 47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp417.14MB
  277. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp428.68MB
  278. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp411.02MB
  279. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp416.44MB
  280. 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
  281. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp49.12MB
  282. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp426.36MB
  283. 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp422.36MB
  284. 49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp427.78MB
  285. 49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp411.84MB
  286. 49. Deep Learning - Preprocessing/3. Standardization.mp450.99MB
  287. 49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp418.61MB
  288. 49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp428.95MB
  289. 50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.mp413.39MB
  290. 50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp418.67MB
  291. 50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.mp416.32MB
  292. 50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp429.04MB
  293. 50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.mp441.53MB
  294. 50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.mp428.23MB
  295. 50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.mp413.9MB
  296. 50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp440.96MB
  297. 50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp429.52MB
  298. 51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.mp466.27MB
  299. 51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp47.3MB
  300. 51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.mp430.44MB
  301. 51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.mp484.33MB
  302. 51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.mp417.57MB
  303. 51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.mp431.18MB
  304. 51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.mp449.81MB
  305. 51. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp410.8MB
  306. 52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp439.75MB
  307. 52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp420.12MB
  308. 52. Deep Learning - Conclusion/4. An overview of CNNs.mp458.8MB
  309. 52. Deep Learning - Conclusion/5. An Overview of RNNs.mp425.26MB
  310. 52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp444.78MB
  311. 53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.mp411.36MB
  312. 53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.mp447.69MB
  313. 53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.mp417.41MB
  314. 53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.mp420.34MB
  315. 53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp438.49MB
  316. 53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.mp432.51MB
  317. 53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.mp437.39MB
  318. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp417.82MB
  319. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp422.59MB
  320. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp418.9MB
  321. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/4. MNIST Model Outline.mp456.39MB
  322. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp425.87MB
  323. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp443.9MB
  324. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp412.86MB
  325. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/8. MNIST Learning.mp446.68MB
  326. 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp462.77MB
  327. 55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting acquainted with the dataset.mp487.65MB
  328. 55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.mp412.21MB
  329. 55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.mp439.41MB
  330. 55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.mp4103.42MB
  331. 55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.mp476.34MB
  332. 55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.mp453.13MB
  333. 55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.mp441.52MB
  334. 55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.mp425.74MB
  335. 55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp411.21MB
  336. 55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp436.38MB
  337. 56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp469.03MB
  338. 56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4104.08MB
  339. 56. Software Integration/5. Taking a Closer Look at APIs.mp4115.6MB
  340. 56. Software Integration/7. Communication between Software Products through Text Files.mp460.35MB
  341. 56. Software Integration/9. Software Integration - Explained.mp463.7MB
  342. 57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp452.3MB
  343. 57. Case Study - What's Next in the Course/2. The Business Task.mp439.15MB
  344. 57. Case Study - What's Next in the Course/3. Introducing the Data Set.mp440.86MB
  345. 58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp423.15MB
  346. 58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp461.9MB
  347. 58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp427.86MB
  348. 58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp420.18MB
  349. 58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp461.76MB
  350. 58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp440.58MB
  351. 58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp481.11MB
  352. 58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp413.74MB
  353. 58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp474.61MB
  354. 58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp438.73MB
  355. 58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp414.01MB
  356. 58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp425.67MB
  357. 58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp457.28MB
  358. 58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp447.8MB
  359. 58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp427.96MB
  360. 58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp429.51MB
  361. 58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp439.59MB
  362. 58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp421.64MB
  363. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp427.54MB
  364. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp445.79MB
  365. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp416.76MB
  366. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp420.59MB
  367. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp452.77MB
  368. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp441.62MB
  369. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp438.87MB
  370. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp452.37MB
  371. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.2MB
  372. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp440.41MB
  373. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp439.57MB
  374. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp449.07MB
  375. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp437.46MB
  376. 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp444.49MB
  377. 60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp425.49MB
  378. 60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp454.25MB
  379. 61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp456.55MB
  380. 61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp459.33MB
  381. 61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp440.63MB
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