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

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  • 文件大小:17GB
  • 下载次数:18
  • 最近下载:2021-01-20 22:27:02
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文件列表

  1. 4. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 291MB
  2. 4. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4 287MB
  3. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 252MB
  4. 5. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 244MB
  5. 4. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 237MB
  6. 12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.mp4 235MB
  7. 3. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 232MB
  8. 12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.mp4 223MB
  9. 4. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 219MB
  10. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4 218MB
  11. 5. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 214MB
  12. 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 214MB
  13. 8. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4 205MB
  14. 8. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 195MB
  15. 4. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 193MB
  16. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 192MB
  17. 12. Serving a Tensorflow Model through a Website/7. Loading a Tensorflow.js Model and Starting your own Server.mp4 188MB
  18. 12. Serving a Tensorflow Model through a Website/9. Styling an HTML Canvas.mp4 187MB
  19. 12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.mp4 173MB
  20. 12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.mp4 172MB
  21. 3. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 171MB
  22. 3. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4 170MB
  23. 5. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 169MB
  24. 12. Serving a Tensorflow Model through a Website/13. Resizing and Addign Padding to Images.mp4 158MB
  25. 3. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 157MB
  26. 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 155MB
  27. 3. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4 153MB
  28. 5. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 153MB
  29. 5. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 153MB
  30. 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4 151MB
  31. 12. Serving a Tensorflow Model through a Website/6. HTML and CSS Styling.mp4 150MB
  32. 5. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4 150MB
  33. 2. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4 148MB
  34. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4 147MB
  35. 5. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 144MB
  36. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4 142MB
  37. 4. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 141MB
  38. 5. Predict House Prices with Multivariable Linear Regression/7. Working with Index Data, Pandas Series, and Dummy Variables.mp4 141MB
  39. 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.mp4 137MB
  40. 5. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 135MB
  41. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4 135MB
  42. 5. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 134MB
  43. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/7. Interacting with the Operating System and the Python Try-Catch Block.mp4 133MB
  44. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 133MB
  45. 4. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 133MB
  46. 12. Serving a Tensorflow Model through a Website/4. Converting a Model to Tensorflow.js.mp4 132MB
  47. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4 132MB
  48. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.mp4 131MB
  49. 5. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 131MB
  50. 4. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 131MB
  51. 5. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 129MB
  52. 11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.mp4 128MB
  53. 3. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 128MB
  54. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4 127MB
  55. 5. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 127MB
  56. 4. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 125MB
  57. 5. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 124MB
  58. 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.mp4 122MB
  59. 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.mp4 118MB
  60. 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 116MB
  61. 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.mp4 112MB
  62. 5. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 111MB
  63. 4. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.mp4 111MB
  64. 11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4 111MB
  65. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.mp4 110MB
  66. 12. Serving a Tensorflow Model through a Website/2. Saving Tensorflow Models.mp4 110MB
  67. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.mp4 107MB
  68. 2. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.mp4 105MB
  69. 12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4 104MB
  70. 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.mp4 104MB
  71. 12. Serving a Tensorflow Model through a Website/3. Loading a SavedModel.mp4 104MB
  72. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 104MB
  73. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.mp4 104MB
  74. 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.mp4 100MB
  75. 11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.mp4 100MB
  76. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp4 98MB
  77. 2. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.mp4 97MB
  78. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp4 96MB
  79. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.mp4 96MB
  80. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp4 96MB
  81. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.mp4 94MB
  82. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 93MB
  83. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.mp4 92MB
  84. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp4 91MB
  85. 4. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.mp4 91MB
  86. 4. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.mp4 90MB
  87. 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.mp4 88MB
  88. 5. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 87MB
  89. 4. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp4 87MB
  90. 4. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp4 87MB
  91. 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.mp4 86MB
  92. 5. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 85MB
  93. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.mp4 84MB
  94. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.mp4 83MB
  95. 3. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp4 83MB
  96. 3. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp4 82MB
  97. 4. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp4 81MB
  98. 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.mp4 80MB
  99. 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.mp4 79MB
  100. 12. Serving a Tensorflow Model through a Website/5. Introducing the Website Project and Tooling.mp4 78MB
  101. 11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 75MB
  102. 4. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp4 75MB
  103. 4. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 73MB
  104. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 72MB
  105. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp4 71MB
  106. 3. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.mp4 71MB
  107. 4. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp4 71MB
  108. 11. Use Tensorflow to Classify Handwritten Digits/4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 70MB
  109. 8. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.mp4 66MB
  110. 4. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.mp4 66MB
  111. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.mp4 65MB
  112. 5. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 65MB
  113. 4. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp4 65MB
  114. 5. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 65MB
  115. 5. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp4 64MB
  116. 5. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp4 64MB
  117. 8. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.mp4 64MB
  118. 8. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.mp4 63MB
  119. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp4 63MB
  120. 5. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.mp4 62MB
  121. 12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.mp4 62MB
  122. 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.mp4 62MB
  123. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp4 62MB
  124. 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.mp4 61MB
  125. 5. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 57MB
  126. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp4 57MB
  127. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp4 56MB
  128. 5. Predict House Prices with Multivariable Linear Regression/2. Gathering the Boston House Price Data.mp4 56MB
  129. 5. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp4 56MB
  130. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp4 54MB
  131. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp4 54MB
  132. 3. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp4 53MB
  133. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/5. Calculate the Token Probabilities and Save the Trained Model.mp4 53MB
  134. 8. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.mp4 53MB
  135. 11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.mp4 53MB
  136. 3. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.mp4 52MB
  137. 8. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.mp4 52MB
  138. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/1. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 52MB
  139. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp4 51MB
  140. 3. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.mp4 50MB
  141. 5. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp4 49MB
  142. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.mp4 49MB
  143. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.mp4 47MB
  144. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/4. Sum the Tokens across the Spam and Ham Subsets.mp4 47MB
  145. 11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.mp4 45MB
  146. 1. Introduction to the Course/1. What is Machine Learning.mp4 45MB
  147. 1. Introduction to the Course/2. What is Data Science.mp4 43MB
  148. 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.mp4 42MB
  149. 3. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.mp4 42MB
  150. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.mp4 42MB
  151. 3. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 42MB
  152. 12. Serving a Tensorflow Model through a Website/8. Adding a Favicon.mp4 42MB
  153. 8. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.mp4 41MB
  154. 5. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.mp4 40MB
  155. 12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.mp4 39MB
  156. 12. Serving a Tensorflow Model through a Website/1. What you'll make.mp4 38MB
  157. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.mp4 36MB
  158. 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.mp4 33MB
  159. 5. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.mp4 33MB
  160. 11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.mp4 32MB
  161. 5. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp4 32MB
  162. 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.mp4 32MB
  163. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.mp4 31MB
  164. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.mp4 31MB
  165. 2. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.mp4 30MB
  166. 2. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.mp4 30MB
  167. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.mp4 29MB
  168. 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.mp4 29MB
  169. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.mp4 29MB
  170. 8. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.mp4 28MB
  171. 8. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.mp4 26MB
  172. 8. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp4 25MB
  173. 8. Test and Evaluate a Naive Bayes Classifier Part 3/1.1 SpamData.zip.zip 23MB
  174. 4. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.mp4 23MB
  175. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1.2 SpamData.zip.zip 22MB
  176. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/2.1 SpamData.zip.zip 21MB
  177. 4. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.mp4 21MB
  178. 5. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp4 16MB
  179. 11. Use Tensorflow to Classify Handwritten Digits/2.1 MNIST.zip.zip 15MB
  180. 11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.mp4 7MB
  181. 12. Serving a Tensorflow Model through a Website/16.1 math_garden_stub complete.zip.zip 4MB
  182. 12. Serving a Tensorflow Model through a Website/12.1 math_garden_stub 12.12 checkpoint.zip.zip 4MB
  183. 5. Predict House Prices with Multivariable Linear Regression/33.3 04 Multivariable Regression.ipynb.zip.zip 4MB
  184. 12. Serving a Tensorflow Model through a Website/3.1 MNIST_Model_Load_Files.zip.zip 3MB
  185. 3. Python Programming for Data Science and Machine Learning/4.1 12 Rules to Learn to Code.pdf.pdf 2MB
  186. 12. Serving a Tensorflow Model through a Website/4.1 TFJS.zip.zip 2MB
  187. 4. Introduction to Optimisation and the Gradient Descent Algorithm/24.1 03 Gradient Descent.ipynb.zip.zip 1MB
  188. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip.zip 978KB
  189. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/8.1 09 Neural Nets Pretrained Image Classification.ipynb.zip.zip 572KB
  190. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/4.1 TF_Keras_Classification_Images.zip.zip 501KB
  191. 2. Predict Movie Box Office Revenue with Linear Regression/2.2 cost_revenue_dirty.csv.csv 375KB
  192. 8. Test and Evaluate a Naive Bayes Classifier Part 3/12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip.zip 243KB
  193. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip.zip 120KB
  194. 1. Introduction to the Course/3.1 ML Data Science Syllabus.pdf.pdf 104KB
  195. 2. Predict Movie Box Office Revenue with Linear Regression/3.2 cost_revenue_clean.csv.csv 91KB
  196. 2. Predict Movie Box Office Revenue with Linear Regression/6.1 01 Linear Regression (complete).ipynb.zip.zip 75KB
  197. 3. Python Programming for Data Science and Machine Learning/21.1 02 Python Intro.ipynb.zip.zip 46KB
  198. 12. Serving a Tensorflow Model through a Website/5.1 math_garden_stub.zip.zip 44KB
  199. 4. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.srt 42KB
  200. 4. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).srt 42KB
  201. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.srt 40KB
  202. 12. Serving a Tensorflow Model through a Website/9. Styling an HTML Canvas.srt 39KB
  203. 12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.srt 38KB
  204. 12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.srt 38KB
  205. 12. Serving a Tensorflow Model through a Website/6. HTML and CSS Styling.srt 38KB
  206. 12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.srt 38KB
  207. 2. Predict Movie Box Office Revenue with Linear Regression/4.1 01 Linear Regression (checkpoint).ipynb.zip.zip 38KB
  208. 12. Serving a Tensorflow Model through a Website/7. Loading a Tensorflow.js Model and Starting your own Server.srt 37KB
  209. 4. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.srt 36KB
  210. 12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.srt 35KB
  211. 3. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.srt 35KB
  212. 8. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.srt 34KB
  213. 8. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.srt 33KB
  214. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.srt 33KB
  215. 4. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).srt 33KB
  216. 2. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.srt 30KB
  217. 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.srt 30KB
  218. 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.srt 29KB
  219. 3. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.srt 29KB
  220. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.srt 28KB
  221. 5. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt 28KB
  222. 5. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt 28KB
  223. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.srt 28KB
  224. 3. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.srt 27KB
  225. 12. Serving a Tensorflow Model through a Website/13. Resizing and Addign Padding to Images.srt 27KB
  226. 3. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.srt 26KB
  227. 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.srt 26KB
  228. 12. Serving a Tensorflow Model through a Website/3. Loading a SavedModel.srt 26KB
  229. 4. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.srt 26KB
  230. 3. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.srt 26KB
  231. 5. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.srt 25KB
  232. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/7. Interacting with the Operating System and the Python Try-Catch Block.srt 24KB
  233. 5. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.srt 24KB
  234. 11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.srt 23KB
  235. 5. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.srt 23KB
  236. 5. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.srt 23KB
  237. 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.srt 23KB
  238. 4. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.srt 22KB
  239. 4. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.srt 22KB
  240. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.srt 22KB
  241. 5. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt 22KB
  242. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.srt 22KB
  243. 2. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.srt 22KB
  244. 5. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.srt 21KB
  245. 5. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt 21KB
  246. 12. Serving a Tensorflow Model through a Website/2. Saving Tensorflow Models.srt 21KB
  247. 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.srt 21KB
  248. 12. Serving a Tensorflow Model through a Website/4. Converting a Model to Tensorflow.js.srt 21KB
  249. 5. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.srt 21KB
  250. 11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.srt 20KB
  251. 5. Predict House Prices with Multivariable Linear Regression/7. Working with Index Data, Pandas Series, and Dummy Variables.srt 20KB
  252. 5. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.srt 20KB
  253. 3. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.srt 20KB
  254. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.srt 20KB
  255. 4. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.srt 20KB
  256. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.srt 19KB
  257. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.srt 19KB
  258. 11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.srt 19KB
  259. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.srt 19KB
  260. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.srt 19KB
  261. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.srt 18KB
  262. 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.srt 18KB
  263. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.srt 18KB
  264. 5. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.srt 18KB
  265. 4. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.srt 18KB
  266. 5. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.srt 18KB
  267. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.srt 18KB
  268. 4. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).srt 18KB
  269. 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.srt 18KB
  270. 4. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.srt 18KB
  271. 5. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.srt 17KB
  272. 12. Serving a Tensorflow Model through a Website/5. Introducing the Website Project and Tooling.srt 17KB
  273. 12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.srt 17KB
  274. 4. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.srt 17KB
  275. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.srt 16KB
  276. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.srt 16KB
  277. 3. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.srt 16KB
  278. 3. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.srt 16KB
  279. 3. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.srt 16KB
  280. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.srt 16KB
  281. 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.srt 15KB
  282. 5. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt 15KB
  283. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.srt 15KB
  284. 5. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt 14KB
  285. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.srt 14KB
  286. 5. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.srt 14KB
  287. 11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.srt 14KB
  288. 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.srt 14KB
  289. 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.srt 14KB
  290. 5. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.srt 14KB
  291. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.srt 14KB
  292. 4. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).srt 14KB
  293. 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.srt 14KB
  294. 4. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.srt 14KB
  295. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.srt 13KB
  296. 2. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.srt 13KB
  297. 4. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.srt 13KB
  298. 8. Test and Evaluate a Naive Bayes Classifier Part 3/12.1 08 Naive Bayes with scikit-learn.ipynb.zip.zip 13KB
  299. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.srt 13KB
  300. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.srt 13KB
  301. 8. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.srt 13KB
  302. 4. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.srt 13KB
  303. 8. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.srt 13KB
  304. 11. Use Tensorflow to Classify Handwritten Digits/4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.srt 13KB
  305. 4. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).srt 12KB
  306. 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.srt 12KB
  307. 3. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.srt 12KB
  308. 12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.srt 12KB
  309. 5. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.srt 12KB
  310. 5. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.srt 12KB
  311. 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.srt 12KB
  312. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.srt 11KB
  313. 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.srt 11KB
  314. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1. Setting up the Notebook and Understanding Delimiters in a Dataset.srt 11KB
  315. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/1. The Human Brain and the Inspiration for Artificial Neural Networks.srt 11KB
  316. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.srt 11KB
  317. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.srt 11KB
  318. 5. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.srt 11KB
  319. 8. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.srt 11KB
  320. 4. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.srt 10KB
  321. 2. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.srt 10KB
  322. 3. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.srt 10KB
  323. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.srt 10KB
  324. 12. Serving a Tensorflow Model through a Website/1. What you'll make.srt 10KB
  325. 8. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.srt 10KB
  326. 5. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.srt 10KB
  327. 12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.srt 10KB
  328. 8. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.srt 10KB
  329. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/5. Calculate the Token Probabilities and Save the Trained Model.srt 9KB
  330. 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.srt 9KB
  331. 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.srt 9KB
  332. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.srt 9KB
  333. 4. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.srt 9KB
  334. 11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.srt 9KB
  335. 11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.srt 9KB
  336. 5. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt 9KB
  337. 4. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.srt 9KB
  338. 3. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.srt 8KB
  339. 5. Predict House Prices with Multivariable Linear Regression/2. Gathering the Boston House Price Data.srt 8KB
  340. 2. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.srt 8KB
  341. 5. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.srt 8KB
  342. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).srt 8KB
  343. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/4. Sum the Tokens across the Spam and Ham Subsets.srt 8KB
  344. 3. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.srt 8KB
  345. 8. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.srt 8KB
  346. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.srt 7KB
  347. 12. Serving a Tensorflow Model through a Website/8. Adding a Favicon.srt 7KB
  348. 5. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.srt 7KB
  349. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.srt 7KB
  350. 3. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.srt 7KB
  351. 4. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.srt 7KB
  352. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.srt 7KB
  353. 1. Introduction to the Course/1. What is Machine Learning.srt 7KB
  354. 11. Use Tensorflow to Classify Handwritten Digits/14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip.zip 7KB
  355. 8. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.srt 7KB
  356. 11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.srt 6KB
  357. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.srt 6KB
  358. 12. Serving a Tensorflow Model through a Website/2.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip.zip 6KB
  359. 5. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.srt 6KB
  360. 12. Serving a Tensorflow Model through a Website/3.2 12 TF SavedModel Export Completed.ipynb.zip.zip 6KB
  361. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.srt 6KB
  362. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.srt 6KB
  363. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.srt 6KB
  364. 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.srt 6KB
  365. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/7.1 07 Bayes Classifier - Training.ipynb.zip.zip 6KB
  366. 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.srt 6KB
  367. 1. Introduction to the Course/2. What is Data Science.srt 5KB
  368. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.srt 5KB
  369. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.srt 5KB
  370. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.srt 5KB
  371. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.srt 5KB
  372. 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.srt 5KB
  373. 12. Serving a Tensorflow Model through a Website/7.1 x_test0_ylabel7.txt.txt 5KB
  374. 12. Serving a Tensorflow Model through a Website/7.2 x_test1_ylabel2.txt.txt 5KB
  375. 12. Serving a Tensorflow Model through a Website/7.3 x_test2_ylabel1.txt.txt 5KB
  376. 8. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.srt 4KB
  377. 5. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.srt 4KB
  378. 13. Next Steps/1. Where next.html 4KB
  379. 8. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.srt 4KB
  380. 4. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.srt 4KB
  381. 5. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.srt 4KB
  382. 5. Predict House Prices with Multivariable Linear Regression/33.1 boston_valuation.py.py 3KB
  383. 5. Predict House Prices with Multivariable Linear Regression/33.2 04 Valuation Tool.ipynb.zip.zip 3KB
  384. 11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.srt 2KB
  385. 1. Introduction to the Course/4. Top Tips for Succeeding on this Course.html 2KB
  386. 3. Python Programming for Data Science and Machine Learning/4. Download the 12 Rules to Learn to Code.html 1KB
  387. 1. Introduction to the Course/5. Course Resources List.html 1KB
  388. 13. Next Steps/3. Stay in Touch!.html 1KB
  389. 1. Introduction to the Course/3. Download the Syllabus.html 1KB
  390. 2. Predict Movie Box Office Revenue with Linear Regression/7. Join the Student Community.html 730B
  391. 5. Predict House Prices with Multivariable Linear Regression/13. A Note for the Next Lesson.html 476B
  392. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/23. A Note for the Next Lesson.html 476B
  393. 13. Next Steps/2. What Modules Do You Want to See.html 431B
  394. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/8. Download the Complete Notebook Here.html 264B
  395. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13. Download the Complete Notebook Here.html 242B
  396. 11. Use Tensorflow to Classify Handwritten Digits/14. Download the Complete Notebook Here.html 242B
  397. 2. Predict Movie Box Office Revenue with Linear Regression/6. Download the Complete Notebook Here.html 242B
  398. 3. Python Programming for Data Science and Machine Learning/21. Download the Complete Notebook Here.html 242B
  399. 4. Introduction to Optimisation and the Gradient Descent Algorithm/24. Download the Complete Notebook Here.html 242B
  400. 5. Predict House Prices with Multivariable Linear Regression/33. Download the Complete Notebook Here.html 242B
  401. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/39. Download the Complete Notebook Here.html 242B
  402. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/7. Download the Complete Notebook Here.html 242B
  403. 8. Test and Evaluate a Naive Bayes Classifier Part 3/12. Download the Complete Notebook Here.html 242B
  404. 3. Python Programming for Data Science and Machine Learning/12. Python Functions Coding Exercise - Part 1.html 156B
  405. 3. Python Programming for Data Science and Machine Learning/14. Python Functions Coding Exercise - Part 2.html 156B
  406. 3. Python Programming for Data Science and Machine Learning/16. Python Functions Coding Exercise - Part 3.html 156B
  407. 3. Python Programming for Data Science and Machine Learning/6. Python Variable Coding Exercise.html 156B
  408. 3. Python Programming for Data Science and Machine Learning/8. Python Lists Coding Exercise.html 156B
  409. 4. Introduction to Optimisation and the Gradient Descent Algorithm/7. Python Loops Coding Exercise.html 156B
  410. 5. Predict House Prices with Multivariable Linear Regression/31. Python Conditional Statement Coding Exercise.html 156B
  411. 3. Python Programming for Data Science and Machine Learning/9.1 lsd_math_score_data.csv.csv 155B
  412. 1. Introduction to the Course/4.1 App Brewery Cornell Notes Template.html 141B
  413. 0. Websites you may like/[FCS Forum].url 133B
  414. 0. Websites you may like/[FreeCourseSite.com].url 127B
  415. 0. Websites you may like/[CourseClub.ME].url 122B
  416. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1.1 Course Resources.html 122B
  417. 11. Use Tensorflow to Classify Handwritten Digits/1.1 Course Resources.html 122B
  418. 2. Predict Movie Box Office Revenue with Linear Regression/1.1 Course Resources.html 122B
  419. 3. Python Programming for Data Science and Machine Learning/1.1 Course Resources.html 122B
  420. 3. Python Programming for Data Science and Machine Learning/2.1 Course Resources.html 122B
  421. 4. Introduction to Optimisation and the Gradient Descent Algorithm/1.1 Course Resources.html 122B
  422. 5. Predict House Prices with Multivariable Linear Regression/1.1 Course Resources.html 122B
  423. 6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/1.1 Course Resources.html 122B
  424. 7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1.1 Course Resources.html 122B
  425. 8. Test and Evaluate a Naive Bayes Classifier Part 3/1.2 Course Resources.html 122B
  426. 9. Introduction to Neural Networks and How to Use Pre-Trained Models/1.1 Course Resources.html 122B
  427. 2. Predict Movie Box Office Revenue with Linear Regression/2.1 The-Numbers Movie Budgets.html 102B
  428. 2. Predict Movie Box Office Revenue with Linear Regression/3.1 Try Jupyter in your Browser.html 85B