Torrents Search Engine
Search Any Type Of Torrents
Torrent Details
-
Complete 2020 Data Science and Machine Learning Bootcamp [UdemyLibrary.com]
-
Course
-
1. Introduction to the Course
-
1. What is Machine Learning.mp4 - 45.29 MB
-
2. What is Data Science.mp4 - 42.86 MB
-
3.1 ML Data Science Syllabus.pdf - 103.97 KB
-
1. What is Machine Learning.srt - 6.91 KB
-
2. What is Data Science.srt - 5.72 KB
-
4. Top Tips for Succeeding on this Course.html - 2.09 KB
-
5. Course Resources List.html - 1.13 KB
-
3. Download the Syllabus.html - 1.03 KB
-
4.1 App Brewery Cornell Notes Template.html - 141 B
-
-
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
-
12. Model Evaluation and the Confusion Matrix.mp4 - 251.83 MB
-
10. Use the Model to Make Predictions.mp4 - 218.25 MB
-
9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 - 191.54 MB
-
7. Interacting with the Operating System and the Python Try-Catch Block.mp4 - 133.41 MB
-
4. Exploring the CIFAR Data.mp4 - 110.31 MB
-
6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 - 103.6 MB
-
8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 - 100.42 MB
-
5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 - 93.16 MB
-
11. Model Evaluation and the Confusion Matrix.mp4 - 62.76 MB
-
2. Installing Tensorflow and Keras for Jupyter.mp4 - 42.1 MB
-
3. Gathering the CIFAR 10 Dataset.mp4 - 31.37 MB
-
1. Solving a Business Problem with Image Classification.mp4 - 30.53 MB
-
13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip - 120.11 KB
-
12. Model Evaluation and the Confusion Matrix.srt - 40.5 KB
-
10. Use the Model to Make Predictions.srt - 32.97 KB
-
9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.srt - 28.28 KB
-
7. Interacting with the Operating System and the Python Try-Catch Block.srt - 23.69 KB
-
5. Pre-processing Scaling Inputs and Creating a Validation Dataset.srt - 19.92 KB
-
6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.srt - 18.63 KB
-
4. Exploring the CIFAR Data.srt - 18.23 KB
-
8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.srt - 14.1 KB
-
11. Model Evaluation and the Confusion Matrix.srt - 10.8 KB
-
2. Installing Tensorflow and Keras for Jupyter.srt - 6.42 KB
-
3. Gathering the CIFAR 10 Dataset.srt - 6.1 KB
-
1. Solving a Business Problem with Image Classification.srt - 4.97 KB
-
14. Any Feedback on this Section.html - 521 B
-
13. Download the Complete Notebook Here.html - 242 B
-
1.1 Course Resources.html - 122 B
-
-
11. Use Tensorflow to Classify Handwritten Digits
-
12. Different Model Architectures Experimenting with Dropout.mp4 - 213.67 MB
-
11. Name Scoping and Image Visualisation in Tensorboard.mp4 - 155.37 MB
-
6. Creating Tensors and Setting up the Neural Network Architecture.mp4 - 150.86 MB
-
9. Tensorboard Summaries and the Filewriter.mp4 - 128.29 MB
-
10. Understanding the Tensorflow Graph Nodes and Edges.mp4 - 115.75 MB
-
13. Prediction and Model Evaluation.mp4 - 110.72 MB
-
8. TensorFlow Sessions and Batching Data.mp4 - 100.32 MB
-
7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 - 75.11 MB
-
4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 - 70.19 MB
-
2. Getting the Data and Loading it into Numpy Arrays.mp4 - 52.82 MB
-
5. What is a Tensor.mp4 - 45.39 MB
-
3. Data Exploration and Understanding the Structure of the Input Data.mp4 - 32.41 MB
-
2.1 MNIST.zip - 14.77 MB
-
1. What's coming up.mp4 - 7.1 MB
-
12. Different Model Architectures Experimenting with Dropout.srt - 30.11 KB
-
6. Creating Tensors and Setting up the Neural Network Architecture.srt - 29.05 KB
-
11. Name Scoping and Image Visualisation in Tensorboard.srt - 26.26 KB
-
9. Tensorboard Summaries and the Filewriter.srt - 23.21 KB
-
10. Understanding the Tensorflow Graph Nodes and Edges.srt - 21.25 KB
-
8. TensorFlow Sessions and Batching Data.srt - 20.5 KB
-
13. Prediction and Model Evaluation.srt - 18.9 KB
-
7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.srt - 14.15 KB
-
4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.srt - 12.67 KB
-
2. Getting the Data and Loading it into Numpy Arrays.srt - 9.01 KB
-
5. What is a Tensor.srt - 8.99 KB
-
14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip - 6.6 KB
-
3. Data Exploration and Understanding the Structure of the Input Data.srt - 6.49 KB
-
1. What's coming up.srt - 2.49 KB
-
15. Any Feedback on this Section.html - 499 B
-
14. Download the Complete Notebook Here.html - 242 B
-
1.1 Course Resources.html - 122 B
-
-
12. Serving a Tensorflow Model through a Website
-
12. Introduction to OpenCV.mp4 - 235.33 MB
-
14. Calculating the Centre of Mass and Shifting the Image.mp4 - 223.26 MB
-
7. Loading a Tensorflow.js Model and Starting your own Server.mp4 - 188.04 MB
-
9. Styling an HTML Canvas.mp4 - 187.37 MB
-
16. Adding the Game Logic.mp4 - 172.83 MB
-
10. Drawing on an HTML Canvas.mp4 - 171.97 MB
-
13. Resizing and Adding Padding to Images.mp4 - 157.5 MB
-
6. HTML and CSS Styling.mp4 - 150.23 MB
-
4. Converting a Model to Tensorflow.js.mp4 - 132.49 MB
-
2. Saving Tensorflow Models.mp4 - 109.98 MB
-
15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4 - 104.41 MB
-
3. Loading a SavedModel.mp4 - 103.93 MB
-
5. Introducing the Website Project and Tooling.mp4 - 78.04 MB
-
11. Data Pre-Processing for Tensorflow.js.mp4 - 61.89 MB
-
8. Adding a Favicon.mp4 - 41.51 MB
-
17. Publish and Share your Website!.mp4 - 38.75 MB
-
1. What you'll make.mp4 - 38.44 MB
-
16.1 math_garden_stub complete.zip - 4.09 MB
-
12.1 math_garden_stub 12.12 checkpoint.zip - 4.09 MB
-
3.1 MNIST_Model_Load_Files.zip - 2.84 MB
-
4.1 TFJS.zip - 1.54 MB
-
5.1 math_garden_stub.zip - 44.03 KB
-
9. Styling an HTML Canvas.srt - 39.42 KB
-
12. Introduction to OpenCV.srt - 38.37 KB
-
16. Adding the Game Logic.srt - 38.09 KB
-
6. HTML and CSS Styling.srt - 37.89 KB
-
10. Drawing on an HTML Canvas.srt - 37.83 KB
-
7. Loading a Tensorflow.js Model and Starting your own Server.srt - 37.18 KB
-
14. Calculating the Centre of Mass and Shifting the Image.srt - 35.49 KB
-
13. Resizing and Adding Padding to Images.srt - 26.86 KB
-
3. Loading a SavedModel.srt - 26.16 KB
-
2. Saving Tensorflow Models.srt - 21.26 KB
-
4. Converting a Model to Tensorflow.js.srt - 21.13 KB
-
5. Introducing the Website Project and Tooling.srt - 17.19 KB
-
15. Making a Prediction from a Digit drawn on the HTML Canvas.srt - 17.04 KB
-
11. Data Pre-Processing for Tensorflow.js.srt - 11.92 KB
-
1. What you'll make.srt - 9.76 KB
-
17. Publish and Share your Website!.srt - 9.51 KB
-
8. Adding a Favicon.srt - 7.39 KB
-
2.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip - 6.39 KB
-
3.2 12 TF SavedModel Export Completed.ipynb.zip - 6.13 KB
-
7.3 x_test1_ylabel2.txt - 4.59 KB
-
7.2 x_test0_ylabel7.txt - 4.59 KB
-
7.1 x_test2_ylabel1.txt - 4.59 KB
-
18. Any Feedback on this Section.html - 500 B
-
-
13. Next Steps
-
1. Where next.html - 3.93 KB
-
3. Stay in Touch!.html - 1.05 KB
-
2. What Modules Do You Want to See.html - 431 B
-
-
2. Predict Movie Box Office Revenue with Linear Regression
-
3. Explore & Visualise the Data with Python.mp4 - 148.15 MB
-
5. Analyse and Evaluate the Results.mp4 - 105.16 MB
-
2. Gather & Clean the Data.mp4 - 97.02 MB
-
1. Introduction to Linear Regression & Specifying the Problem.mp4 - 30.32 MB
-
4. The Intuition behind the Linear Regression Model.mp4 - 29.63 MB
-
2.2 cost_revenue_dirty.csv - 374.68 KB
-
3.2 cost_revenue_clean.csv - 90.82 KB
-
6.1 01 Linear Regression (complete).ipynb.zip - 75.28 KB
-
4.1 01 Linear Regression (checkpoint).ipynb.zip - 37.64 KB
-
3. Explore & Visualise the Data with Python.srt - 31.02 KB
-
5. Analyse and Evaluate the Results.srt - 22.41 KB
-
2. Gather & Clean the Data.srt - 13.93 KB
-
4. The Intuition behind the Linear Regression Model.srt - 10.84 KB
-
1. Introduction to Linear Regression & Specifying the Problem.srt - 8.74 KB
-
7. Join the Student Community.html - 730 B
-
8. Any Feedback on this Section.html - 512 B
-
6. Download the Complete Notebook Here.html - 242 B
-
1.1 Course Resources.html - 122 B
-
2.1 The-Numbers Movie Budgets.html - 102 B
-
3.1 Try Jupyter in your Browser.html - 85 B
-
-
3. Python Programming for Data Science and Machine Learning
-
10. [Python] - Module Imports.mp4 - 232.07 MB
-
18. How to Make Sense of Python Documentation for Data Visualisation.mp4 - 171.46 MB
-
19. Working with Python Objects to Analyse Data.mp4 - 169.98 MB
-
17. [Python] - Objects - Understanding Attributes and Methods.mp4 - 156.77 MB
-
9. [Python & Pandas] - Dataframes and Series.mp4 - 153.2 MB
-
13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 - 128.2 MB
-
15. [Python] - Functions - Part 3 Results & Return Values.mp4 - 82.63 MB
-
20. [Python] - Tips, Code Style and Naming Conventions.mp4 - 81.53 MB
-
5. [Python] - Variables and Types.mp4 - 71.36 MB
-
7. [Python] - Lists and Arrays.mp4 - 53.47 MB
-
2. Mac Users - Install Anaconda.mp4 - 52.41 MB
-
1. Windows Users - Install Anaconda.mp4 - 49.6 MB
-
3. Does LSD Make You Better at Maths.mp4 - 42.25 MB
-
11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 - 41.61 MB
-
4.1 12 Rules to Learn to Code.pdf - 2.25 MB
-
21.1 02 Python Intro.ipynb.zip - 36.44 KB
-
10. [Python] - Module Imports.srt - 36.12 KB
-
17. [Python] - Objects - Understanding Attributes and Methods.srt - 29.86 KB
-
9. [Python & Pandas] - Dataframes and Series.srt - 28.09 KB
-
19. Working with Python Objects to Analyse Data.srt - 27.29 KB
-
18. How to Make Sense of Python Documentation for Data Visualisation.srt - 26.51 KB
-
13. [Python] - Functions - Part 2 Arguments & Parameters.srt - 20.76 KB
-
20. [Python] - Tips, Code Style and Naming Conventions.srt - 16.72 KB
-
5. [Python] - Variables and Types.srt - 16.55 KB
-
15. [Python] - Functions - Part 3 Results & Return Values.srt - 16.55 KB
-
7. [Python] - Lists and Arrays.srt - 12.15 KB
-
11. [Python] - Functions - Part 1 Defining and Calling Functions.srt - 10.49 KB
-
1. Windows Users - Install Anaconda.srt - 8.78 KB
-
2. Mac Users - Install Anaconda.srt - 8.05 KB
-
3. Does LSD Make You Better at Maths.srt - 7.35 KB
-
4. Download the 12 Rules to Learn to Code.html - 1.13 KB
-
22. Any Feedback on this Section.html - 513 B
-
21. Download the Complete Notebook Here.html - 242 B
-
6. Python Variable Coding Exercise.html - 156 B
-
12. Python Functions Coding Exercise - Part 1.html - 156 B
-
14. Python Functions Coding Exercise - Part 2.html - 156 B
-
16. Python Functions Coding Exercise - Part 3.html - 156 B
-
8. Python Lists Coding Exercise.html - 156 B
-
9.1 lsd_math_score_data.csv - 155 B
-
2.1 Course Resources.html - 122 B
-
1.1 Course Resources.html - 122 B
-
-
4. Introduction to Optimisation and the Gradient Descent Algorithm
-
8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 - 291.33 MB
-
6. [Python] - Loops and the Gradient Descent Algorithm.mp4 - 287.46 MB
-
10. Understanding the Learning Rate.mp4 - 236.6 MB
-
9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 - 219.01 MB
-
11. How to Create 3-Dimensional Charts.mp4 - 193.48 MB
-
15. Reshaping and Slicing N-Dimensional Arrays.mp4 - 140.81 MB
-
12. Understanding Partial Derivatives and How to use SymPy.mp4 - 132.81 MB
-
14. [Python] - Loops and Performance Considerations.mp4 - 131.07 MB
-
21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 - 124.88 MB
-
22. Running Gradient Descent with a MSE Cost Function.mp4 - 111.22 MB
-
4. LaTeX Markdown and Generating Data with Numpy.mp4 - 90.52 MB
-
5. Understanding the Power Rule & Creating Charts with Subplots.mp4 - 90.17 MB
-
18. Transposing and Reshaping Arrays.mp4 - 86.9 MB
-
13. Implementing Batch Gradient Descent with SymPy.mp4 - 86.82 MB
-
19. Implementing a MSE Cost Function.mp4 - 81.11 MB
-
23. Visualising the Optimisation on a 3D Surface.mp4 - 74.81 MB
-
20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 - 73.16 MB
-
16. Concatenating Numpy Arrays.mp4 - 71.33 MB
-
3. Introduction to Cost Functions.mp4 - 66.21 MB
-
17. Introduction to the Mean Squared Error (MSE).mp4 - 64.56 MB
-
2. How a Machine Learns.mp4 - 22.78 MB
-
1. What's Coming Up.mp4 - 20.92 MB
-
24.1 03 Gradient Descent.ipynb.zip - 1.14 MB
-
6. [Python] - Loops and the Gradient Descent Algorithm.srt - 44.03 KB
-
8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).srt - 42.99 KB
-
10. Understanding the Learning Rate.srt - 37.72 KB
-
9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).srt - 33.54 KB
-
11. How to Create 3-Dimensional Charts.srt - 26.1 KB
-
15. Reshaping and Slicing N-Dimensional Arrays.srt - 22.96 KB
-
22. Running Gradient Descent with a MSE Cost Function.srt - 22.32 KB
-
12. Understanding Partial Derivatives and How to use SymPy.srt - 20.23 KB
-
5. Understanding the Power Rule & Creating Charts with Subplots.srt - 18.1 KB
-
14. [Python] - Loops and Performance Considerations.srt - 18.07 KB
-
21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).srt - 17.45 KB
-
4. LaTeX Markdown and Generating Data with Numpy.srt - 17.28 KB
-
20. Understanding Nested Loops and Plotting the MSE Function (Part 1).srt - 13.94 KB
-
19. Implementing a MSE Cost Function.srt - 13.56 KB
-
18. Transposing and Reshaping Arrays.srt - 13.52 KB
-
13. Implementing Batch Gradient Descent with SymPy.srt - 12.93 KB
-
17. Introduction to the Mean Squared Error (MSE).srt - 12.61 KB
-
23. Visualising the Optimisation on a 3D Surface.srt - 10.73 KB
-
3. Introduction to Cost Functions.srt - 9.49 KB
-
16. Concatenating Numpy Arrays.srt - 8.91 KB
-
2. How a Machine Learns.srt - 7.22 KB
-
1. What's Coming Up.srt - 3.83 KB
-
25. Any Feedback on this Section.html - 520 B
-
24. Download the Complete Notebook Here.html - 242 B
-
7. Python Loops Coding Exercise.html - 156 B
-
1.1 Course Resources.html - 122 B
-
-
5. Predict House Prices with Multivariable Linear Regression
-
32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 - 244.17 MB
-
14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 - 214.4 MB
-
11. Visualising Correlations with a Heatmap.mp4 - 168.65 MB
-
26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 - 153.01 MB
-
27. Making Predictions (Part 1) MSE & R-Squared.mp4 - 152.68 MB
-
23. Model Simplification & Baysian Information Criterion.mp4 - 150.15 MB
-
22. Understanding VIF & Testing for Multicollinearity.mp4 - 143.82 MB
-
7. Working with Index Data, Pandas Series, and Dummy Variables.mp4 - 140.76 MB
-
4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 - 135.02 MB
-
30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 - 134.38 MB
-
29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 - 131.31 MB
-
12. Techniques to Style Scatter Plots.mp4 - 128.53 MB
-
20. Improving the Model by Transforming the Data.mp4 - 126.87 MB
-
25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 - 124.42 MB
-
10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 - 111.43 MB
-
3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 - 87.14 MB
-
28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 - 84.85 MB
-
21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 - 65.41 MB
-
5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 - 64.55 MB
-
16. How to Shuffle and Split Training & Testing Data.mp4 - 64.34 MB
-
24. How to Analyse and Plot Regression Residuals.mp4 - 64.18 MB
-
8. Understanding Descriptive Statistics the Mean vs the Median.mp4 - 62.18 MB
-
6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 - 57.32 MB
-
2. Gathering the Boston House Price Data.mp4 - 56.24 MB
-
17. Running a Multivariable Regression.mp4 - 55.56 MB
-
15. Understanding Multivariable Regression.mp4 - 48.8 MB
-
1. Defining the Problem.mp4 - 39.91 MB
-
9. Introduction to Correlation Understanding Strength & Direction.mp4 - 33.09 MB
-
18. How to Calculate the Model Fit with R-Squared.mp4 - 32.4 MB
-
19. Introduction to Model Evaluation.mp4 - 15.99 MB
-
33.1 04 Multivariable Regression.ipynb.zip - 3.54 MB
-
14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt - 28.7 KB
-
32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt - 28.42 KB
-
22. Understanding VIF & Testing for Multicollinearity.srt - 25.62 KB
-
11. Visualising Correlations with a Heatmap.srt - 24.37 KB
-
27. Making Predictions (Part 1) MSE & R-Squared.srt - 23.72 KB
-
23. Model Simplification & Baysian Information Criterion.srt - 23.14 KB
-
26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt - 22.76 KB
-
20. Improving the Model by Transforming the Data.srt - 21.61 KB
-
30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt - 21.4 KB
-
29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.srt - 20.82 KB
-
7. Working with Index Data, Pandas Series, and Dummy Variables.srt - 20.72 KB
-
12. Techniques to Style Scatter Plots.srt - 20.56 KB
-
4. Clean and Explore the Data (Part 2) Find Missing Values.srt - 18.59 KB
-
25. Residual Analysis (Part 1) Predicted vs Actual Values.srt - 18.24 KB
-
10. Calculating Correlations and the Problem posed by Multicollinearity.srt - 17.83 KB
-
3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt - 15.59 KB
-
24. How to Analyse and Plot Regression Residuals.srt - 14.76 KB
-
28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt - 14.76 KB
-
5. Visualising Data (Part 1) Historams, Distributions & Outliers.srt - 14.24 KB
-
8. Understanding Descriptive Statistics the Mean vs the Median.srt - 12.14 KB
-
16. How to Shuffle and Split Training & Testing Data.srt - 11.55 KB
-
21. How to Interpret Coefficients using p-Values and Statistical Significance.srt - 10.78 KB
-
17. Running a Multivariable Regression.srt - 9.77 KB
-
6. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt - 8.98 KB
-
2. Gathering the Boston House Price Data.srt - 8.66 KB
-
9. Introduction to Correlation Understanding Strength & Direction.srt - 8.4 KB
-
15. Understanding Multivariable Regression.srt - 7.52 KB
-
1. Defining the Problem.srt - 6.46 KB
-
18. How to Calculate the Model Fit with R-Squared.srt - 4.42 KB
-
19. Introduction to Model Evaluation.srt - 3.81 KB
-
33.3 boston_valuation.py - 3.05 KB
-
33.2 04 Valuation Tool.ipynb.zip - 2.93 KB
-
34. Any Feedback on this Section.html - 512 B
-
13. A Note for the Next Lesson.html - 476 B
-
33. Download the Complete Notebook Here.html - 242 B
-
31. Python Conditional Statement Coding Exercise.html - 156 B
-
1.1 Course Resources.html - 122 B
-
-
6. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1
-
6. Joint & Conditional Probability.mp4 - 141.82 MB
-
35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 - 137.23 MB
-
11. [Python] - Generator Functions & the yield Keyword.mp4 - 133.16 MB
-
28. Styling the Word Cloud with a Mask.mp4 - 131.37 MB
-
30. Styling Word Clouds with Custom Fonts.mp4 - 127.29 MB
-
13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 - 121.94 MB
-
19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 - 117.75 MB
-
2. Gathering Email Data and Working with Archives & Text Editors.mp4 - 112.05 MB
-
31. Create the Vocabulary for the Spam Classifier.mp4 - 106.96 MB
-
9. Reading Files (Part 2) Stream Objects and Email Structure.mp4 - 104.32 MB
-
27. Creating your First Word Cloud.mp4 - 98.44 MB
-
38. Checkpoint Understanding the Data.mp4 - 96.37 MB
-
21. Removing HTML tags with BeautifulSoup.mp4 - 95.82 MB
-
16. Data Visualisation (Part 1) Pie Charts.mp4 - 90.68 MB
-
34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 - 87.62 MB
-
25. [Python] - Logical Operators to Create Subsets and Indices.mp4 - 86.41 MB
-
7. Bayes Theorem.mp4 - 83.6 MB
-
24. Advanced Subsetting on DataFrames the apply() Function.mp4 - 83.39 MB
-
36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 - 80.5 MB
-
26. Word Clouds & How to install Additional Python Packages.mp4 - 79.48 MB
-
20. Word Stemming & Removing Punctuation.mp4 - 71.44 MB
-
14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4 - 61.83 MB
-
17. Data Visualisation (Part 2) Donut Charts.mp4 - 61.78 MB
-
8. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 - 60.9 MB
-
29. Solving the Hamlet Challenge.mp4 - 57.1 MB
-
15. Saving a JSON File with Pandas.mp4 - 56.35 MB
-
33. Coding Challenge Find the Longest Email.mp4 - 54.47 MB
-
22. Creating a Function for Text Processing.mp4 - 53.91 MB
-
18. Introduction to Natural Language Processing (NLP).mp4 - 50.81 MB
-
12. Create a Pandas DataFrame of Email Bodies.mp4 - 48.66 MB
-
10. Extracting the Text in the Email Body.mp4 - 47.43 MB
-
1. How to Translate a Business Problem into a Machine Learning Problem.mp4 - 42.26 MB
-
4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 - 33.39 MB
-
32. Coding Challenge Check for Membership in a Collection.mp4 - 32.34 MB
-
37. Coding Challenge Solution Preparing the Test Data.mp4 - 28.92 MB
-
3. How to Add the Lesson Resources to the Project.mp4 - 28.9 MB
-
5. Basic Probability.mp4 - 28.55 MB
-
2.1 SpamData.zip - 21.29 MB
-
39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip - 978.02 KB
-
35. Sparse Matrix (Part 2) Data Munging with Nested Loops.srt - 22.34 KB
-
11. [Python] - Generator Functions & the yield Keyword.srt - 22.32 KB
-
6. Joint & Conditional Probability.srt - 19.86 KB
-
19. Tokenizing, Removing Stop Words and the Python Set Data Structure.srt - 19.07 KB
-
13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.srt - 17.96 KB
-
31. Create the Vocabulary for the Spam Classifier.srt - 17.79 KB
-
28. Styling the Word Cloud with a Mask.srt - 16.72 KB
-
16. Data Visualisation (Part 1) Pie Charts.srt - 16.19 KB
-
25. [Python] - Logical Operators to Create Subsets and Indices.srt - 15.5 KB
-
34. Sparse Matrix (Part 1) Split the Training and Testing Data.srt - 15.26 KB
-
7. Bayes Theorem.srt - 15.16 KB
-
30. Styling Word Clouds with Custom Fonts.srt - 14.79 KB
-
9. Reading Files (Part 2) Stream Objects and Email Structure.srt - 14.57 KB
-
2. Gathering Email Data and Working with Archives & Text Editors.srt - 14.13 KB
-
27. Creating your First Word Cloud.srt - 13.67 KB
-
38. Checkpoint Understanding the Data.srt - 13.65 KB
-
24. Advanced Subsetting on DataFrames the apply() Function.srt - 13.53 KB
-
36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.srt - 12.18 KB
-
26. Word Clouds & How to install Additional Python Packages.srt - 11.97 KB
-
8. Reading Files (Part 1) Absolute Paths and Relative Paths.srt - 11.71 KB
-
21. Removing HTML tags with BeautifulSoup.srt - 11.01 KB
-
20. Word Stemming & Removing Punctuation.srt - 10.56 KB
-
1. How to Translate a Business Problem into a Machine Learning Problem.srt - 9.69 KB
-
17. Data Visualisation (Part 2) Donut Charts.srt - 9.56 KB
-
14. Cleaning Data (Part 2) Working with a DataFrame Index.srt - 9.23 KB
-
22. Creating a Function for Text Processing.srt - 8.41 KB
-
18. Introduction to Natural Language Processing (NLP).srt - 8.19 KB
-
33. Coding Challenge Find the Longest Email.srt - 7.54 KB
-
12. Create a Pandas DataFrame of Email Bodies.srt - 7.23 KB
-
15. Saving a JSON File with Pandas.srt - 6.92 KB
-
32. Coding Challenge Check for Membership in a Collection.srt - 6.08 KB
-
4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.srt - 6.08 KB
-
10. Extracting the Text in the Email Body.srt - 6 KB
-
29. Solving the Hamlet Challenge.srt - 5.99 KB
-
5. Basic Probability.srt - 5.26 KB
-
3. How to Add the Lesson Resources to the Project.srt - 4.96 KB
-
37. Coding Challenge Solution Preparing the Test Data.srt - 4.5 KB
-
40. Any Feedback on this Section.html - 519 B
-
23. A Note for the Next Lesson.html - 476 B
-
39. Download the Complete Notebook Here.html - 242 B
-
1.1 Course Resources.html - 122 B
-
-
7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2
-
2. Create a Full Matrix.mp4 - 132.24 MB
-
3. Count the Tokens to Train the Naive Bayes Model.mp4 - 96.18 MB
-
1. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 - 72.5 MB
-
5. Calculate the Token Probabilities and Save the Trained Model.mp4 - 53.45 MB
-
4. Sum the Tokens across the Spam and Ham Subsets.mp4 - 46.71 MB
-
6. Coding Challenge Prepare the Test Data.mp4 - 35.6 MB
-
1.2 SpamData.zip - 22.32 MB
-
2. Create a Full Matrix.srt - 21.72 KB
-
3. Count the Tokens to Train the Naive Bayes Model.srt - 18.35 KB
-
1. Setting up the Notebook and Understanding Delimiters in a Dataset.srt - 11.17 KB
-
5. Calculate the Token Probabilities and Save the Trained Model.srt - 9.44 KB
-
4. Sum the Tokens across the Spam and Ham Subsets.srt - 7.76 KB
-
7.1 07 Bayes Classifier - Training.ipynb.zip - 5.82 KB
-
6. Coding Challenge Prepare the Test Data.srt - 5.14 KB
-
8. Any Feedback on this Section.html - 527 B
-
7. Download the Complete Notebook Here.html - 242 B
-
1.1 Course Resources.html - 122 B
-
-
8. Test and Evaluate a Naive Bayes Classifier Part 3
-
6. Visualising the Decision Boundary.mp4 - 205.31 MB
-
11. A Naive Bayes Implementation using SciKit Learn.mp4 - 195.1 MB
-
2. Joint Conditional Probability (Part 1) Dot Product.mp4 - 66.4 MB
-
3. Joint Conditional Probablity (Part 2) Priors.mp4 - 63.98 MB
-
7. False Positive vs False Negatives.mp4 - 63.25 MB
-
9. The Precision Metric.mp4 - 53.33 MB
-
4. Making Predictions Comparing Joint Probabilities.mp4 - 52.34 MB
-
5. The Accuracy Metric.mp4 - 40.54 MB
-
8. The Recall Metric.mp4 - 28.15 MB
-
1. Set up the Testing Notebook.mp4 - 26.45 MB
-
10. The F-score or F1 Metric.mp4 - 24.71 MB
-
1.1 SpamData.zip - 22.83 MB
-
12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip - 243.05 KB
-
11. A Naive Bayes Implementation using SciKit Learn.srt - 33.68 KB
-
6. Visualising the Decision Boundary.srt - 33.44 KB
-
12.1 08 Naive Bayes with scikit-learn.ipynb.zip - 13.26 KB
-
7. False Positive vs False Negatives.srt - 12.81 KB
-
2. Joint Conditional Probability (Part 1) Dot Product.srt - 12.72 KB
-
3. Joint Conditional Probablity (Part 2) Priors.srt - 10.54 KB
-
4. Making Predictions Comparing Joint Probabilities.srt - 9.67 KB
-
9. The Precision Metric.srt - 9.5 KB
-
5. The Accuracy Metric.srt - 7.65 KB
-
8. The Recall Metric.srt - 6.54 KB
-
10. The F-score or F1 Metric.srt - 4.48 KB
-
1. Set up the Testing Notebook.srt - 3.82 KB
-
13. Any Feedback on this Section.html - 509 B
-
12. Download the Complete Notebook Here.html - 242 B
-
1.2 Course Resources.html - 122 B
-
-
9. Introduction to Neural Networks and How to Use Pre-Trained Models
-
2. Layers, Feature Generation and Learning.mp4 - 146.7 MB
-
6. Making Predictions using InceptionResNet.mp4 - 134.58 MB
-
7. Coding Challenge Solution Using other Keras Models.mp4 - 103.53 MB
-
4. Preprocessing Image Data and How RGB Works.mp4 - 93.6 MB
-
3. Costs and Disadvantages of Neural Networks.mp4 - 91.98 MB
-
5. Importing Keras Models and the Tensorflow Graph.mp4 - 65.47 MB
-
1. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 - 51.81 MB
-
8.1 09 Neural Nets Pretrained Image Classification.ipynb.zip - 571.83 KB
-
4.1 TF_Keras_Classification_Images.zip - 501.1 KB
-
2. Layers, Feature Generation and Learning.srt - 27.79 KB
-
3. Costs and Disadvantages of Neural Networks.srt - 19.24 KB
-
6. Making Predictions using InceptionResNet.srt - 18.9 KB
-
4. Preprocessing Image Data and How RGB Works.srt - 16.15 KB
-
7. Coding Challenge Solution Using other Keras Models.srt - 12.94 KB
-
5. Importing Keras Models and the Tensorflow Graph.srt - 11.44 KB
-
1. The Human Brain and the Inspiration for Artificial Neural Networks.srt - 10.88 KB
-
9. Any Feedback on this Section.html - 526 B
-
8. Download the Complete Notebook Here.html - 264 B
-
1.1 Course Resources.html - 122 B
-
-
Read me first!.txt - 265 B
-
-
[TGx]Downloaded from torrentgalaxy.to .txt - 642 B
-
Read me first!.txt - 265 B
-
Torrent downloaded from Demonoid.is.txt - 112 B
-
Torrent downloaded from ettvcentral.com.txt - 110 B
-
Torrent downloaded from 1337x.to.txt - 100 B
-
| Trackers | Seeders | Leechers | Downloads |
|---|---|---|---|
| udp://tracker.opentrackr.org:1337/announce | 0 | 1 | 0 |
| udp://tracker.moeking.me:6969/announce | 0 | 0 | 0 |
| udp://tracker.uw0.xyz:6969/announce | 0 | 2 | 0 |
| udp://open.stealth.si:80/announce | 0 | 1 | 241 |
| udp://tracker.torrent.eu.org:451/announce | 0 | 1 | 240 |
| udp://p4p.arenabg.com:1337/announce | 0 | 1 | 0 |
| udp://tracker4.itzmx.com:2710/announce | 0 | 0 | 0 |
| udp://retracker.lanta-net.ru:2710/announce | 0 | 0 | 0 |
| udp://exodus.desync.com:6969/announce | 0 | 1 | 0 |
| udp://tracker.internetwarriors.net:1337/announce | 0 | 0 | 0 |
InfoHash:
89BE1977110C01098EDFE38866DDB0B45D59C1EB
Last updated:
Jan 25, 2022
File count:
444 B
File Category:
9
Tags:
Other, Tutorials