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

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最近下载:2021-01-19 23:15:41
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04. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 291MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4 287MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 252MB
05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 244MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 237MB
03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 232MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 219MB
05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 214MB
11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 214MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4 205MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 195MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 193MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 192MB
03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 171MB
05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 169MB
03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 157MB
11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 155MB
03. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4 153MB
05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 153MB
05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 153MB
11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4 151MB
05. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4 150MB
02. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4 148MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4 147MB
05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 144MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4 142MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 141MB
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.mp4 137MB
05. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 135MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4 135MB
05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 134MB
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
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 133MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 133MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4 132MB
06. 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
05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 131MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 131MB
05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 129MB
11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.mp4 128MB
03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 128MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4 127MB
05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 127MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 125MB
05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 124MB
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.mp4 122MB
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.mp4 118MB
11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 116MB
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.mp4 112MB
05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 111MB
11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4 111MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.mp4 110MB
02. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.mp4 105MB
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.mp4 104MB
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
09. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.mp4 104MB
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
11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.mp4 100MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp4 98MB
02. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.mp4 97MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp4 96MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.mp4 96MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp4 96MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.mp4 94MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 93MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.mp4 92MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp4 91MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.mp4 91MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.mp4 90MB
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.mp4 88MB
05. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 87MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp4 87MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp4 87MB
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.mp4 86MB
05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 85MB
06. 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
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.mp4 83MB
03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp4 83MB
03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp4 82MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp4 81MB
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.mp4 80MB
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.mp4 79MB
11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 75MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp4 75MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 73MB
07. 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
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp4 71MB
03. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.mp4 71MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp4 71MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.mp4 66MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.mp4 66MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.mp4 65MB
05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 65MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp4 65MB
05. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 65MB
05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp4 64MB
05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp4 64MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.mp4 64MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.mp4 63MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp4 63MB
05. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.mp4 62MB
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.mp4 62MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp4 62MB
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.mp4 61MB
05. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 57MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp4 57MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp4 56MB
05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp4 56MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp4 54MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp4 54MB
03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp4 53MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/5. Calculate the Token Probabilities and Save the Trained Model.mp4 53MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.mp4 53MB
11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.mp4 53MB
03. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.mp4 52MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.mp4 52MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/1. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 52MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp4 51MB
03. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.mp4 50MB
05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp4 49MB
06. 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
06. 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
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/4. Sum the Tokens across the Spam and Ham Subsets.mp4 47MB
11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.mp4 45MB
01. Introduction to the Course/1. What is Machine Learning.mp4 45MB
01. Introduction to the Course/2. What is Data Science.mp4 43MB
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.mp4 42MB
03. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.mp4 42MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.mp4 42MB
03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 42MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.mp4 41MB
05. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.mp4 40MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.mp4 36MB
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.mp4 33MB
05. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.mp4 33MB
11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.mp4 32MB
05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp4 32MB
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.mp4 32MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.mp4 31MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.mp4 31MB
02. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.mp4 30MB
02. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.mp4 30MB
06. 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
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.mp4 29MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.mp4 29MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.mp4 28MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.mp4 26MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp4 25MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/1.2 SpamData.zip.zip 23MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.mp4 23MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1.1 SpamData.zip.zip 22MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/2.1 SpamData.zip.zip 21MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.mp4 21MB
05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp4 16MB
11. Use Tensorflow to Classify Handwritten Digits/2.1 MNIST.zip.zip 15MB
11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.mp4 7MB
05. Predict House Prices with Multivariable Linear Regression/33.1 04 Multivariable Regression.ipynb.zip.zip 4MB
03. Python Programming for Data Science and Machine Learning/4.1 12 Rules to Learn to Code.pdf.pdf 2MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/24.1 03 Gradient Descent.ipynb.zip.zip 1MB
06. 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 988KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/8.1 09 Neural Nets Pretrained Image Classification.ipynb.zip.zip 572KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/4.1 TF_Keras_Classification_Images.zip.zip 501KB
02. Predict Movie Box Office Revenue with Linear Regression/2.1 cost_revenue_dirty.csv.csv 375KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip.zip 243KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip.zip 120KB
01. Introduction to the Course/3.1 ML Data Science Syllabus.pdf.pdf 104KB
02. Predict Movie Box Office Revenue with Linear Regression/3.1 cost_revenue_clean.csv.csv 91KB
02. Predict Movie Box Office Revenue with Linear Regression/6.1 01 Linear Regression (complete).ipynb.zip.zip 75KB
03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp4.jpg 59KB
02. Predict Movie Box Office Revenue with Linear Regression/4.1 01 Linear Regression (checkpoint).ipynb.zip.zip 38KB
03. Python Programming for Data Science and Machine Learning/21.1 02 Python Intro.ipynb.zip.zip 36KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).vtt 36KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.vtt 36KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.vtt 35KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.vtt 31KB
03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.vtt 30KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.vtt 29KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.vtt 29KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.vtt 29KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).vtt 28KB
02. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.vtt 26KB
11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.vtt 26KB
11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.vtt 25KB
03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.vtt 25KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.vtt 25KB
05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.vtt 24KB
05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.vtt 24KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.vtt 24KB
03. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.vtt 24KB
11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.vtt 23KB
03. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.vtt 23KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.vtt 23KB
03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.vtt 22KB
05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.vtt 22KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/7. Interacting with the Operating System and the Python Try-Catch Block.vtt 21KB
05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.vtt 21KB
11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.vtt 20KB
05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.vtt 20KB
05. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.vtt 20KB
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.vtt 20KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.vtt 20KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.vtt 19KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.vtt 19KB
05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.vtt 19KB
02. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.vtt 19KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.vtt 19KB
05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.vtt 19KB
11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.vtt 19KB
05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).vtt 18KB
05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.vtt 18KB
11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.vtt 18KB
05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.vtt 18KB
05. Predict House Prices with Multivariable Linear Regression/7. Working with Index Data, Pandas Series, and Dummy Variables.vtt 18KB
03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.vtt 18KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.vtt 17KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.vtt 17KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.vtt 17KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.vtt 17KB
11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.vtt 17KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.vtt 16KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.vtt 16KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.vtt 16KB
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.vtt 16KB
05. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.vtt 16KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.vtt 16KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.vtt 16KB
05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.vtt 15KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.vtt 15KB
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.vtt 15KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).vtt 15KB
05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.vtt 15KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.vtt 15KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.vtt 15KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.vtt 14KB
03. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.vtt 14KB
03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.vtt 14KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.vtt 14KB
03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.vtt 14KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.vtt 14KB
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.vtt 13KB
05. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.vtt 13KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/12.1 08 Naive Bayes with scikit-learn.ipynb.zip.zip 13KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.vtt 13KB
05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.vtt 13KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.vtt 13KB
11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.vtt 12KB
05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.vtt 12KB
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.vtt 12KB
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.vtt 12KB
05. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.vtt 12KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.vtt 12KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).vtt 12KB
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.vtt 12KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.vtt 12KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.vtt 12KB
02. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.vtt 12KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.vtt 12KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.vtt 12KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.vtt 11KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.vtt 11KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.vtt 11KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.vtt 11KB
11. Use Tensorflow to Classify Handwritten Digits/4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.vtt 11KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).vtt 11KB
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.vtt 11KB
03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.vtt 10KB
05. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.vtt 10KB
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.vtt 10KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.vtt 10KB
05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.vtt 10KB
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.vtt 10KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1. Setting up the Notebook and Understanding Delimiters in a Dataset.vtt 10KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/1. The Human Brain and the Inspiration for Artificial Neural Networks.vtt 10KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.vtt 10KB
05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.vtt 9KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.vtt 9KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.vtt 9KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.vtt 9KB
02. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.vtt 9KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.vtt 9KB
03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.vtt 9KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.vtt 9KB
05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.vtt 8KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.vtt 8KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/5. Calculate the Token Probabilities and Save the Trained Model.vtt 8KB
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.vtt 8KB
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.vtt 8KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.vtt 8KB
11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.vtt 8KB
11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.vtt 8KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.vtt 8KB
05. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.vtt 8KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.vtt 8KB
03. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.vtt 7KB
05. Predict House Prices with Multivariable Linear Regression/2. Gathering the Boston House Price Data.vtt 7KB
02. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.vtt 7KB
05. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.vtt 7KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).vtt 7KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/4. Sum the Tokens across the Spam and Ham Subsets.vtt 7KB
03. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.vtt 7KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.vtt 7KB
11. Use Tensorflow to Classify Handwritten Digits/14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip.zip 7KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.vtt 7KB
05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.vtt 6KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.vtt 6KB
03. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.vtt 6KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.vtt 6KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.vtt 6KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/7.1 07 Bayes Classifier - Training.ipynb.zip.zip 6KB
01. Introduction to the Course/1. What is Machine Learning.vtt 6KB
11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.vtt 6KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.vtt 6KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.vtt 6KB
05. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.vtt 5KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.vtt 5KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.vtt 5KB
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.vtt 5KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.vtt 5KB
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.vtt 5KB
01. Introduction to the Course/2. What is Data Science.vtt 5KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.vtt 5KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.vtt 5KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.vtt 4KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.vtt 4KB
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.vtt 4KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.vtt 4KB
12. Next Steps/1. Where next.html 4KB
05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.vtt 4KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.vtt 3KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.vtt 3KB
05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.vtt 3KB
05. Predict House Prices with Multivariable Linear Regression/33.2 04 Valuation Tool.ipynb.zip.zip 3KB
11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.vtt 2KB
01. Introduction to the Course/4. Top Tips for Succeeding on this Course.html 2KB
03. Python Programming for Data Science and Machine Learning/4. Download the 12 Rules to Learn to Code.html 1KB
01. Introduction to the Course/5. Course Resources List.html 1KB
12. Next Steps/3. Stay in Touch!.html 1KB
01. Introduction to the Course/3. Download the Syllabus.html 1KB
02. Predict Movie Box Office Revenue with Linear Regression/7. Join the Student Community.html 730B
05. Predict House Prices with Multivariable Linear Regression/13. A Note for the Next Lesson.html 476B
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/23. A Note for the Next Lesson.html 476B
12. Next Steps/2. What Modules Do You Want to See.html 431B
09. Introduction to Neural Networks and How to Use Pre-Trained Models/8. Download the Complete Notebook Here.html 264B
02. Predict Movie Box Office Revenue with Linear Regression/6. Download the Complete Notebook Here.html 242B
03. Python Programming for Data Science and Machine Learning/21. Download the Complete Notebook Here.html 242B
04. Introduction to Optimisation and the Gradient Descent Algorithm/24. Download the Complete Notebook Here.html 242B
05. Predict House Prices with Multivariable Linear Regression/33. Download the Complete Notebook Here.html 242B
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/39. Download the Complete Notebook Here.html 242B
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/7. Download the Complete Notebook Here.html 242B
08. Test and Evaluate a Naive Bayes Classifier Part 3/12. Download the Complete Notebook Here.html 242B
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13. Download the Complete Notebook Here.html 242B
11. Use Tensorflow to Classify Handwritten Digits/14. Download the Complete Notebook Here.html 242B
03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.txt 235B
03. Python Programming for Data Science and Machine Learning/9.1 lsd_math_score_data.csv.csv 155B
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03. Python Programming for Data Science and Machine Learning/16. Python Functions Coding Exercise - Part 3.html 149B
04. Introduction to Optimisation and the Gradient Descent Algorithm/7. Python Loops Coding Exercise.html 149B
05. Predict House Prices with Multivariable Linear Regression/31. Python Conditional Statement Coding Exercise.html 149B
01. Introduction to the Course/4.1 App Brewery Cornell Notes Template.html 141B
udemycoursedownloader.com.url 132B
02. Predict Movie Box Office Revenue with Linear Regression/1.1 Course Resources.html 122B
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02. Predict Movie Box Office Revenue with Linear Regression/2.2 The-Numbers Movie Budgets.html 102B
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02. Predict Movie Box Office Revenue with Linear Regression/3.2 Try Jupyter in your Browser.html 85B