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[UdemyCourseDownloader] Complete Data Science & Machine Learning Bootcamp – Python 3

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UdemyCourseDownloader Complete Data Science & Machine Learning Bootcamp – Python 3

文件列表
  1. 04. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4291.34MB
  2. 01. Introduction to the Course/1. What is Machine Learning.mp445.29MB
  3. 01. Introduction to the Course/2. What is Data Science.mp442.86MB
  4. 02. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.mp430.33MB
  5. 02. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.mp497.02MB
  6. 02. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4148.16MB
  7. 02. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.mp429.63MB
  8. 02. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.mp4105.17MB
  9. 03. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.mp449.6MB
  10. 03. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.mp452.41MB
  11. 03. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.mp442.26MB
  12. 03. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.mp471.37MB
  13. 03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp453.47MB
  14. 03. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4153.21MB
  15. 03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4232.08MB
  16. 03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp441.61MB
  17. 03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4128.2MB
  18. 03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp482.64MB
  19. 03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4156.77MB
  20. 03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4171.46MB
  21. 03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp481.54MB
  22. 04. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.mp420.93MB
  23. 04. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.mp422.78MB
  24. 04. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.mp466.2MB
  25. 04. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.mp490.52MB
  26. 04. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.mp490.17MB
  27. 04. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4287.45MB
  28. 04. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4219.02MB
  29. 04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4236.6MB
  30. 04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4193.48MB
  31. 04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4132.82MB
  32. 04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp486.83MB
  33. 04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4131.08MB
  34. 04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4140.82MB
  35. 04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp471.33MB
  36. 04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp464.57MB
  37. 04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp486.91MB
  38. 04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp481.12MB
  39. 04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp473.16MB
  40. 04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4124.88MB
  41. 04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp474.82MB
  42. 05. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.mp439.92MB
  43. 05. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp487.14MB
  44. 05. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4135.03MB
  45. 05. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp464.56MB
  46. 05. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp457.32MB
  47. 05. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.mp462.19MB
  48. 05. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.mp433.09MB
  49. 05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4111.44MB
  50. 05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4168.65MB
  51. 05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4128.53MB
  52. 05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4214.4MB
  53. 05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp448.81MB
  54. 05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp464.35MB
  55. 05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp455.57MB
  56. 05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp432.4MB
  57. 05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp415.99MB
  58. 05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4126.87MB
  59. 05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp465.4MB
  60. 05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4143.83MB
  61. 05. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4150.15MB
  62. 05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp464.18MB
  63. 05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4124.42MB
  64. 05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4153.02MB
  65. 05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4152.68MB
  66. 05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp484.85MB
  67. 05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4131.31MB
  68. 05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4134.39MB
  69. 05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4244.16MB
  70. 06. 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
  71. 06. 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.mp4112.05MB
  72. 06. 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.mp428.91MB
  73. 06. 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.mp433.39MB
  74. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.mp428.56MB
  75. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4141.82MB
  76. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.mp483.12MB
  77. 06. 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
  78. 06. 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.mp4104.33MB
  79. 06. 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
  80. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4133.16MB
  81. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.mp448.67MB
  82. 06. 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
  83. 06. 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
  84. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp456.35MB
  85. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp490.69MB
  86. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp461.79MB
  87. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp450.81MB
  88. 06. 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.mp4117.76MB
  89. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp471.44MB
  90. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp495.82MB
  91. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp453.91MB
  92. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.mp483.4MB
  93. 06. 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
  94. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/26. Word Clouds & How to install Additional Python Packages.mp479.49MB
  95. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp498.44MB
  96. 06. 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
  97. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp457.11MB
  98. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4127.3MB
  99. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/32. Coding Challenge Check for Membership in a Collection.mp432.35MB
  100. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp454.47MB
  101. 06. 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.mp487.63MB
  102. 06. 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
  103. 06. 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.mp480.5MB
  104. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.mp428.93MB
  105. 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp496.37MB
  106. 07. 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
  107. 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4132.24MB
  108. 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.mp496.19MB
  109. 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/4. Sum the Tokens across the Spam and Ham Subsets.mp446.71MB
  110. 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/5. Calculate the Token Probabilities and Save the Trained Model.mp453.46MB
  111. 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.mp435.6MB
  112. 08. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.mp426.45MB
  113. 08. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.mp466.41MB
  114. 08. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.mp463.98MB
  115. 08. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.mp452.34MB
  116. 08. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.mp440.54MB
  117. 08. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4205.31MB
  118. 08. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.mp463.25MB
  119. 08. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.mp428.16MB
  120. 08. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.mp453.34MB
  121. 08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp424.72MB
  122. 08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4195.1MB
  123. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/1. The Human Brain and the Inspiration for Artificial Neural Networks.mp451.81MB
  124. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4146.7MB
  125. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.mp491.99MB
  126. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.mp493.61MB
  127. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.mp465.47MB
  128. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4134.58MB
  129. 09. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.mp4103.54MB
  130. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.mp430.52MB
  131. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.mp442.1MB
  132. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.mp431.36MB
  133. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.mp4110.31MB
  134. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp493.16MB
  135. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4103.61MB
  136. 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
  137. 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.mp4100.43MB
  138. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4191.53MB
  139. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp462.76MB
  140. 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4251.84MB
  141. 11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.mp47.1MB
  142. 11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.mp452.82MB
  143. 11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.mp432.41MB
  144. 11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.mp445.39MB
  145. 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4150.86MB
  146. 11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp475.12MB
  147. 11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.mp4100.33MB
  148. 11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.mp4128.29MB
  149. 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4115.74MB
  150. 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4155.37MB
  151. 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4213.68MB
  152. 11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4110.71MB
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