FreeTutorials Us Udemy - The Data Science Course 2018 Complete Data Science Bootcamp

1. 10. Statistics - Descriptive Statistics/11. Cross Table and Scatter Plot.mp439.81MB
2. 10. Statistics - Descriptive Statistics/13. Mean, median and mode.mp437.07MB
3. 10. Statistics - Descriptive Statistics/15. Skewness.mp419.41MB
4. 10. Statistics - Descriptive Statistics/17. Variance.mp450.95MB
5. 10. Statistics - Descriptive Statistics/19. Standard Deviation and Coefficient of Variation.mp445.13MB
6. 10. Statistics - Descriptive Statistics/1. Types of Data.mp472.52MB
7. 10. Statistics - Descriptive Statistics/21. Covariance.mp427.48MB
8. 10. Statistics - Descriptive Statistics/23. Correlation Coefficient.mp429.57MB
9. 10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp454.39MB
10. 10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp438.46MB
11. 10. Statistics - Descriptive Statistics/7. Numerical Variables - Frequency Distribution Table.mp425.98MB
12. 10. Statistics - Descriptive Statistics/9. The Histogram.mp413.78MB
13. 11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4159.46MB
14. 12. Statistics - Inferential Statistics Fundamentals/10. Standard error.mp422.77MB
15. 12. Statistics - Inferential Statistics Fundamentals/11. Estimators and Estimates.mp447.83MB
16. 12. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp415.51MB
17. 12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp461.59MB
18. 12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp449.85MB
19. 12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp422.51MB
20. 12. Statistics - Inferential Statistics Fundamentals/8. Central Limit Theorem.mp462.88MB
21. 13. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Dependent samples.mp470.47MB
22. 13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent samples (Part 1).mp428.75MB
23. 13. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 2).mp426.82MB
24. 13. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 3).mp419.93MB
25. 13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp449.98MB
26. 13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp478.2MB
27. 13. Statistics - Inferential Statistics Confidence Intervals/5. Student's T Distribution.mp435.43MB
28. 13. Statistics - Inferential Statistics Confidence Intervals/7. Confidence Intervals; Population Variance Unknown; t-score.mp432.21MB
29. 13. Statistics - Inferential Statistics Confidence Intervals/9. Margin of Error.mp459.09MB
30. 14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4102.67MB
31. 15. Statistics - Hypothesis Testing/10. p-value.mp455.87MB
32. 15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp440.21MB
33. 15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp450.39MB
34. 15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp429.96MB
35. 15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 2).mp436.37MB
36. 15. Statistics - Hypothesis Testing/1. The Null vs Alternative Hypothesis.mp492.12MB
37. 15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4113.16MB
38. 15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp443.93MB
39. 15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp454.22MB
40. 16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp469.48MB
41. 17. Part 3 Introduction to Python/1. Introduction to Programming.mp458.55MB
42. 17. Part 3 Introduction to Python/3. Why Python.mp475.08MB
43. 17. Part 3 Introduction to Python/5. Why Jupyter.mp444.31MB
44. 17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp454.41MB
45. 17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp413.79MB
46. 17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp430.58MB
47. 18. Python - Variables and Data Types/1. Variables.mp426.61MB
48. 18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp417.07MB
49. 18. Python - Variables and Data Types/5. Python Strings.mp430.76MB
50. 19. Python - Basic Python Syntax/10. Indexing Elements.mp45.94MB
51. 19. Python - Basic Python Syntax/12. Structuring with Indentation.mp46.81MB
52. 19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp418.92MB
53. 19. Python - Basic Python Syntax/3. The Double Equality Sign.mp45.99MB
54. 19. Python - Basic Python Syntax/5. How to Reassign Values.mp44MB
55. 19. Python - Basic Python Syntax/7. Add Comments.mp45.01MB
56. 19. Python - Basic Python Syntax/9. Understanding Line Continuation.mp42.35MB
57. 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp449.03MB
58. 1. Part 1 Introduction/2. What Does the Course Cover.mp462.25MB
59. 20. Python - Other Python Operators/1. Comparison Operators.mp410.18MB
60. 20. Python - Other Python Operators/3. Logical and Identity Operators.mp430.05MB
61. 21. Python - Conditional Statements/1. The IF Statement.mp413.63MB
62. 21. Python - Conditional Statements/3. The ELSE Statement.mp413.58MB
63. 21. Python - Conditional Statements/4. The ELIF Statement.mp433.15MB
64. 21. Python - Conditional Statements/5. A Note on Boolean Values.mp411.25MB
65. 22. Python - Python Functions/1. Defining a Function in Python.mp47.74MB
66. 22. Python - Python Functions/2. How to Create a Function with a Parameter.mp423.87MB
67. 22. Python - Python Functions/3. Defining a Function in Python - Part II.mp414.78MB
68. 22. Python - Python Functions/4. How to Use a Function within a Function.mp48.13MB
69. 22. Python - Python Functions/5. Conditional Statements and Functions.mp415.69MB
70. 22. Python - Python Functions/6. Functions Containing a Few Arguments.mp47.58MB
71. 22. Python - Python Functions/7. Built-in Functions in Python.mp422.02MB
72. 23. Python - Sequences/1. Lists.mp422MB
73. 23. Python - Sequences/3. Using Methods.mp421.95MB
74. 23. Python - Sequences/5. List Slicing.mp430.77MB
75. 23. Python - Sequences/6. Tuples.mp416.67MB
76. 23. Python - Sequences/7. Dictionaries.mp425.04MB
77. 24. Python - Iterations/1. For Loops.mp411.79MB
78. 24. Python - Iterations/3. While Loops and Incrementing.mp415.44MB
79. 24. Python - Iterations/4. Lists with the range() Function.mp411.42MB
80. 24. Python - Iterations/6. Conditional Statements and Loops.mp416.09MB
81. 24. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp49.48MB
82. 24. Python - Iterations/8. How to Iterate over Dictionaries.mp416.98MB
83. 25. Python - Advanced Python Tools/1. Object Oriented Programming.mp433.59MB
84. 25. Python - Advanced Python Tools/3. Modules and Packages.mp48.5MB
85. 25. Python - Advanced Python Tools/5. What is the Standard Library.mp418.04MB
86. 25. Python - Advanced Python Tools/7. Importing Modules in Python.mp419.93MB
87. 26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp417.32MB
88. 27. Advanced Statistical Methods - Linear regression/10. How to Interpret the Regression Table.mp444.64MB
89. 27. Advanced Statistical Methods - Linear regression/11. Decomposition of Variability.mp449.66MB
90. 27. Advanced Statistical Methods - Linear regression/13. What is the OLS.mp428.31MB
91. 27. Advanced Statistical Methods - Linear regression/14. R-Squared.mp441.03MB
92. 27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp457.37MB
93. 27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.mp414.73MB
94. 27. Advanced Statistical Methods - Linear regression/5. Geometrical Representation of the Linear Regression Model.mp45.12MB
95. 27. Advanced Statistical Methods - Linear regression/6. Python Packages Installation.mp440.59MB
96. 27. Advanced Statistical Methods - Linear regression/7. First Regression in Python.mp444.57MB
97. 27. Advanced Statistical Methods - Linear regression/9. Using Seaborn for Graphs.mp412.24MB
98. 28. Advanced Statistical Methods - Multiple Linear Regression/10. A2 No Endogeneity.mp435.67MB
99. 28. Advanced Statistical Methods - Multiple Linear Regression/12. A3 Normality and Homoscedasticity.mp442.7MB
100. 28. Advanced Statistical Methods - Multiple Linear Regression/13. A4 No Autocorrelation.mp431.52MB
101. 28. Advanced Statistical Methods - Multiple Linear Regression/15. A5 No Multicollinearity.mp428.71MB
102. 28. Advanced Statistical Methods - Multiple Linear Regression/17. Dealing with Categorical Data - Dummy Variables.mp455.66MB
103. 28. Advanced Statistical Methods - Multiple Linear Regression/19. Making Predictions with the Linear Regression.mp424.7MB
104. 28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.mp421.53MB
105. 28. Advanced Statistical Methods - Multiple Linear Regression/2. Adjusted R-Squared.mp454.83MB
106. 28. Advanced Statistical Methods - Multiple Linear Regression/5. Test for Significance of the Model (F-Test).mp416.42MB
107. 28. Advanced Statistical Methods - Multiple Linear Regression/6. OLS Assumptions.mp421.85MB
108. 28. Advanced Statistical Methods - Multiple Linear Regression/8. A1 Linearity.mp412.61MB
109. 29. Advanced Statistical Methods - Logistic Regression/10. Underfitting and Overfitting.mp422.29MB
110. 29. Advanced Statistical Methods - Logistic Regression/11. Testing the Model.mp432.27MB
111. 29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp427.07MB
112. 29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp434.7MB
113. 29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp486.49MB
114. 29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp417.11MB
115. 29. Advanced Statistical Methods - Logistic Regression/5. An Invaluable Coding Tip.mp423.05MB
116. 29. Advanced Statistical Methods - Logistic Regression/6. Understanding Logistic Regression Tables.mp430.55MB
117. 29. Advanced Statistical Methods - Logistic Regression/7. What do the Odds Actually Mean.mp432.29MB
118. 29. Advanced Statistical Methods - Logistic Regression/8. Binary Predictors in a Logistic Regression.mp438.43MB
119. 29. Advanced Statistical Methods - Logistic Regression/9. Calculating the Accuracy of the Model.mp432.85MB
120. 2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp481.41MB
121. 2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp453.56MB
122. 2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp464.51MB
123. 2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4108.98MB
124. 2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp467.74MB
125. 30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp453.42MB
126. 30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp471.54MB
127. 30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp436.16MB
128. 30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp414.55MB
129. 31. Advanced Statistical Methods - K-Means Clustering/10. How is Clustering Useful.mp474.45MB
130. 31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp427.28MB
131. 31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp451.82MB
132. 31. Advanced Statistical Methods - K-Means Clustering/3. Clustering Categorical Data.mp421.24MB
133. 31. Advanced Statistical Methods - K-Means Clustering/4. How to Choose the Number of Clusters.mp444.14MB
134. 31. Advanced Statistical Methods - K-Means Clustering/5. Pros and Cons of K-Means Clustering.mp437.71MB
135. 31. Advanced Statistical Methods - K-Means Clustering/6. To Standardize or to not Standardize.mp430.11MB
136. 31. Advanced Statistical Methods - K-Means Clustering/7. Relationship between Clustering and Regression.mp49.93MB
137. 31. Advanced Statistical Methods - K-Means Clustering/8. Market Segmentation with Cluster Analysis (Part 1).mp443.01MB
138. 31. Advanced Statistical Methods - K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).mp456.11MB
139. 32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp444.58MB
140. 32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp429.06MB
141. 32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp429.62MB
142. 33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.mp432.62MB
143. 33. Part 5 Mathematics/12. Errors when Adding Matrices.mp411.18MB
144. 33. Part 5 Mathematics/13. Transpose of a Matrix.mp438.07MB
145. 33. Part 5 Mathematics/14. Dot Product.mp424MB
146. 33. Part 5 Mathematics/15. Dot Product of Matrices.mp449.43MB
147. 33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4144.34MB
148. 33. Part 5 Mathematics/1. What is a matrix.mp433.59MB
149. 33. Part 5 Mathematics/3. Scalars and Vectors.mp433.85MB
150. 33. Part 5 Mathematics/5. Linear Algebra and Geometry.mp449.79MB
151. 33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp426.12MB
152. 33. Part 5 Mathematics/8. What is a Tensor.mp422.53MB
153. 34. Part 6 Deep Learning/1. What to Expect from this Part.mp431.1MB
154. 35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp438.31MB
155. 35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp422.64MB
156. 35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp417.91MB
157. 35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp423.28MB
158. 35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp437.24MB
159. 35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp442.92MB
160. 35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp455.62MB
161. 35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp439.42MB
162. 35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp428.71MB
163. 35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp445.11MB
164. 35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp428.44MB
165. 35. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp425.11MB
166. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp420.6MB
167. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp434.94MB
168. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp424.4MB
169. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp461.14MB
170. 37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp414.56MB
171. 37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp447.69MB
172. 37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp417.41MB
173. 37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp420.34MB
174. 37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp438.49MB
175. 37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp432.51MB
176. 37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp437.39MB
177. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp412.5MB
178. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp429.53MB
179. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp459.36MB
180. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp427.68MB
181. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp425.1MB
182. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp425.92MB
183. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp434.95MB
184. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp419.51MB
185. 39. Deep Learning - Overfitting/1. What is Overfitting.mp431.08MB
186. 39. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp425.07MB
187. 39. Deep Learning - Overfitting/3. What is Validation.mp432.71MB
188. 39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp425.2MB
189. 39. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp420.7MB
190. 39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp424.17MB
191. 3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4126.87MB
192. 40. Deep Learning - Initialization/1. What is Initialization.mp421.76MB
193. 40. Deep Learning - Initialization/2. Types of Simple Initializations.mp414.31MB
194. 40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp417.14MB
195. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp428.68MB
196. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp411.02MB
197. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp416.44MB
198. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp429.09MB
199. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp49.11MB
200. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).mp426.35MB
201. 41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp422.36MB
202. 42. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp427.78MB
203. 42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp411.84MB
204. 42. Deep Learning - Preprocessing/3. Standardization.mp450.98MB
205. 42. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp418.6MB
206. 42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp428.95MB
207. 43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp417.82MB
208. 43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp422.59MB
209. 43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp418.91MB
210. 43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp456.38MB
211. 43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp425.86MB
212. 43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp443.9MB
213. 43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp412.85MB
214. 43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp446.69MB
215. 43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp462.77MB
216. 44. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp411.2MB
217. 44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp436.38MB
218. 44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp487.66MB
219. 44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp412.22MB
220. 44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp439.41MB
221. 44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4103.41MB
222. 44. Deep Learning - Business Case Example/6. Creating a Data Provider.mp476.34MB
223. 44. Deep Learning - Business Case Example/7. Business Case Model Outline.mp453.13MB
224. 44. Deep Learning - Business Case Example/8. Business Case Optimization.mp441.52MB
225. 44. Deep Learning - Business Case Example/9. Business Case Interpretation.mp425.74MB
226. 45. Deep Learning - Conclusion/1. Summary of What You Learned.mp439.76MB
227. 45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp420.13MB
228. 45. Deep Learning - Conclusion/3. An overview of CNNs.mp458.79MB
229. 45. Deep Learning - Conclusion/5. An Overview of RNNs.mp425.27MB
230. 45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp444.77MB
231. 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp481.19MB
232. 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4123.51MB
233. 5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp442.78MB
234. 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp499.32MB
235. 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4125.15MB
236. 5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp436.81MB
237. 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4138.3MB
238. 5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp429.94MB
239. 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp475.51MB
240. 5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp422.03MB
241. 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp489.94MB
242. 5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp429.54MB
243. 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4103.52MB
244. 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp454.38MB
245. 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp472.85MB
246. 9. Part 2 Statistics/1. Population and Sample.mp458.11MB