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[FreeCourseSite.com] Udemy - Complete 2020 Data Science & Machine Learning Bootcamp

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FreeCourseSite com Udemy - Complete 2020 Data Science & Machine Learning Bootcamp

文件列表
  1. 1. Introduction to the Course/1. What is Machine Learning.mp445.3MB
  2. 1. Introduction to the Course/2. What is Data Science.mp442.86MB
  3. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.mp430.52MB
  4. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4218.25MB
  5. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp462.76MB
  6. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4251.83MB
  7. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.mp442.1MB
  8. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.mp431.37MB
  9. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.mp4110.31MB
  10. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp493.16MB
  11. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4103.6MB
  12. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/7. Interacting with the Operating System and the Python Try-Catch Block.mp4133.41MB
  13. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4100.42MB
  14. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4191.53MB
  15. 11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.mp47.11MB
  16. 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4115.74MB
  17. 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4155.37MB
  18. 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4213.67MB
  19. 11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4110.71MB
  20. 11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.mp452.81MB
  21. 11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.mp432.41MB
  22. 11. Use Tensorflow to Classify Handwritten Digits/4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp470.18MB
  23. 11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.mp445.39MB
  24. 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4150.85MB
  25. 11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp475.11MB
  26. 11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.mp4100.32MB
  27. 11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.mp4128.29MB
  28. 12. Serving a Tensorflow Model through a Website/1. What you'll make.mp438.44MB
  29. 12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.mp4171.97MB
  30. 12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.mp461.89MB
  31. 12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.mp4235.33MB
  32. 12. Serving a Tensorflow Model through a Website/13. Resizing and Addign Padding to Images.mp4157.5MB
  33. 12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.mp4223.26MB
  34. 12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4104.41MB
  35. 12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.mp4172.83MB
  36. 12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.mp438.74MB
  37. 12. Serving a Tensorflow Model through a Website/2. Saving Tensorflow Models.mp4109.98MB
  38. 12. Serving a Tensorflow Model through a Website/3. Loading a SavedModel.mp4103.93MB
  39. 12. Serving a Tensorflow Model through a Website/4. Converting a Model to Tensorflow.js.mp4132.49MB
  40. 12. Serving a Tensorflow Model through a Website/5. Introducing the Website Project and Tooling.mp478.03MB
  41. 12. Serving a Tensorflow Model through a Website/6. HTML and CSS Styling.mp4150.23MB
  42. 12. Serving a Tensorflow Model through a Website/7. Loading a Tensorflow.js Model and Starting your own Server.mp4188.04MB
  43. 12. Serving a Tensorflow Model through a Website/8. Adding a Favicon.mp441.51MB
  44. 12. Serving a Tensorflow Model through a Website/9. Styling an HTML Canvas.mp4187.36MB
  45. 2. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.mp430.32MB
  46. 2. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.mp497.02MB
  47. 2. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4148.15MB
  48. 2. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.mp429.63MB
  49. 2. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.mp4105.16MB
  50. 3. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.mp449.6MB
  51. 3. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4232.07MB
  52. 3. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp441.61MB
  53. 3. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4128.19MB
  54. 3. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp482.63MB
  55. 3. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4156.77MB
  56. 3. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4171.46MB
  57. 3. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4169.98MB
  58. 3. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.mp452.42MB
  59. 3. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp481.53MB
  60. 3. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.mp442.26MB
  61. 3. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.mp471.36MB
  62. 3. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp453.47MB
  63. 3. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4153.2MB
  64. 4. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.mp420.93MB
  65. 4. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4236.6MB
  66. 4. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4193.48MB
  67. 4. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4132.81MB
  68. 4. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp486.82MB
  69. 4. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4131.07MB
  70. 4. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4140.81MB
  71. 4. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp471.33MB
  72. 4. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp464.57MB
  73. 4. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp486.9MB
  74. 4. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp481.11MB
  75. 4. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.mp422.78MB
  76. 4. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp473.16MB
  77. 4. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4124.88MB
  78. 4. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.mp4111.21MB
  79. 4. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp474.81MB
  80. 4. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.mp466.2MB
  81. 4. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.mp490.52MB
  82. 4. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.mp490.17MB
  83. 4. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4287.45MB
  84. 4. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4291.33MB
  85. 4. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4219.01MB
  86. 5. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.mp439.91MB
  87. 5. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4111.43MB
  88. 5. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4168.65MB
  89. 5. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4128.53MB
  90. 5. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4214.4MB
  91. 5. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp448.8MB
  92. 5. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp464.34MB
  93. 5. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp455.56MB
  94. 5. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp432.4MB
  95. 5. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp416MB
  96. 5. Predict House Prices with Multivariable Linear Regression/2. Gathering the Boston House Price Data.mp456.24MB
  97. 5. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4126.87MB
  98. 5. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp465.4MB
  99. 5. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4143.82MB
  100. 5. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4150.15MB
  101. 5. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp464.18MB
  102. 5. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4124.41MB
  103. 5. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4153.01MB
  104. 5. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4152.68MB
  105. 5. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp484.85MB
  106. 5. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4131.31MB
  107. 5. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp487.14MB
  108. 5. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4134.38MB
  109. 5. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4244.16MB
  110. 5. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4135.02MB
  111. 5. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp464.55MB
  112. 5. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp457.32MB
  113. 5. Predict House Prices with Multivariable Linear Regression/7. Working with Index Data, Pandas Series, and Dummy Variables.mp4140.76MB
  114. 5. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.mp462.18MB
  115. 5. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.mp433.09MB
  116. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/1. How to Translate a Business Problem into a Machine Learning Problem.mp442.26MB
  117. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.mp447.43MB
  118. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4133.16MB
  119. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.mp448.66MB
  120. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4121.93MB
  121. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/14. Cleaning Data (Part 2) Working with a DataFrame Index.mp461.83MB
  122. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp456.34MB
  123. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp490.68MB
  124. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp461.78MB
  125. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp450.81MB
  126. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4117.75MB
  127. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/2. Gathering Email Data and Working with Archives & Text Editors.mp4112.04MB
  128. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp471.44MB
  129. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp495.82MB
  130. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp453.91MB
  131. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.mp483.39MB
  132. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/25. [Python] - Logical Operators to Create Subsets and Indices.mp486.41MB
  133. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/26. Word Clouds & How to install Additional Python Packages.mp479.48MB
  134. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp498.44MB
  135. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.mp4131.37MB
  136. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp457.1MB
  137. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/3. How to Add the Lesson Resources to the Project.mp428.9MB
  138. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4127.29MB
  139. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.mp4106.96MB
  140. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/32. Coding Challenge Check for Membership in a Collection.mp432.34MB
  141. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp454.47MB
  142. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp487.62MB
  143. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4137.23MB
  144. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp480.49MB
  145. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.mp428.92MB
  146. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp496.37MB
  147. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp433.38MB
  148. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.mp428.55MB
  149. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4141.82MB
  150. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.mp483.6MB
  151. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/8. Reading Files (Part 1) Absolute Paths and Relative Paths.mp460.9MB
  152. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/9. Reading Files (Part 2) Stream Objects and Email Structure.mp4104.32MB
  153. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1. Setting up the Notebook and Understanding Delimiters in a Dataset.mp472.5MB
  154. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4132.24MB
  155. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.mp496.18MB
  156. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/4. Sum the Tokens across the Spam and Ham Subsets.mp446.7MB
  157. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/5. Calculate the Token Probabilities and Save the Trained Model.mp453.45MB
  158. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.mp435.6MB
  159. 8. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.mp426.46MB
  160. 8. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp424.71MB
  161. 8. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4195.09MB
  162. 8. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.mp466.4MB
  163. 8. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.mp463.98MB
  164. 8. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.mp452.34MB
  165. 8. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.mp440.54MB
  166. 8. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4205.31MB
  167. 8. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.mp463.25MB
  168. 8. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.mp428.15MB
  169. 8. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.mp453.33MB
  170. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/1. The Human Brain and the Inspiration for Artificial Neural Networks.mp451.8MB
  171. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4146.7MB
  172. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.mp491.98MB
  173. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.mp493.6MB
  174. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.mp465.47MB
  175. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4134.58MB
  176. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.mp4103.53MB
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