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| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 Course Resources.html |
122б |
| 1.1 SpamData.zip.zip |
22.32Мб |
| 1.2 Course Resources.html |
122б |
| 1.2 SpamData.zip.zip |
22.83Мб |
| 1. Defining the Problem.mp4 |
39.92Мб |
| 1. Defining the Problem.vtt |
5.45Кб |
| 1. How to Translate a Business Problem into a Machine Learning Problem.mp4 |
42.26Мб |
| 1. How to Translate a Business Problem into a Machine Learning Problem.vtt |
8.16Кб |
| 1. Introduction to Linear Regression & Specifying the Problem.mp4 |
30.33Мб |
| 1. Introduction to Linear Regression & Specifying the Problem.vtt |
7.28Кб |
| 1. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 |
72.50Мб |
| 1. Setting up the Notebook and Understanding Delimiters in a Dataset.vtt |
9.79Кб |
| 1. Set up the Testing Notebook.mp4 |
26.45Мб |
| 1. Set up the Testing Notebook.vtt |
3.32Кб |
| 1. Solving a Business Problem with Image Classification.mp4 |
30.52Мб |
| 1. Solving a Business Problem with Image Classification.vtt |
4.39Кб |
| 1. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 |
51.81Мб |
| 1. The Human Brain and the Inspiration for Artificial Neural Networks.vtt |
9.60Кб |
| 1. What's coming up.mp4 |
7.10Мб |
| 1. What's Coming Up.mp4 |
20.93Мб |
| 1. What's coming up.vtt |
2.21Кб |
| 1. What's Coming Up.vtt |
3.24Кб |
| 1. What is Machine Learning.mp4 |
45.29Мб |
| 1. What is Machine Learning.vtt |
5.79Кб |
| 1. Where next.html |
3.93Кб |
| 1. Windows Users - Install Anaconda.mp4 |
49.60Мб |
| 1. Windows Users - Install Anaconda.vtt |
7.46Кб |
| 10. [Python] - Module Imports.mp4 |
232.08Мб |
| 10. [Python] - Module Imports.vtt |
30.42Кб |
| 10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 |
111.44Мб |
| 10. Calculating Correlations and the Problem posed by Multicollinearity.vtt |
15.25Кб |
| 10. Extracting the Text in the Email Body.mp4 |
47.43Мб |
| 10. Extracting the Text in the Email Body.vtt |
5.14Кб |
| 10. The F-score or F1 Metric.mp4 |
24.72Мб |
| 10. The F-score or F1 Metric.vtt |
4.03Кб |
| 10. Understanding the Learning Rate.mp4 |
236.60Мб |
| 10. Understanding the Learning Rate.vtt |
31.31Кб |
| 10. Understanding the Tensorflow Graph Nodes and Edges.mp4 |
115.74Мб |
| 10. Understanding the Tensorflow Graph Nodes and Edges.vtt |
18.56Кб |
| 10. Use the Model to Make Predictions.mp4 |
218.26Мб |
| 10. Use the Model to Make Predictions.vtt |
28.87Кб |
| 11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 |
41.61Мб |
| 11. [Python] - Functions - Part 1 Defining and Calling Functions.vtt |
8.86Кб |
| 11. [Python] - Generator Functions & the yield Keyword.mp4 |
133.16Мб |
| 11. [Python] - Generator Functions & the yield Keyword.vtt |
19.35Кб |
| 11. A Naive Bayes Implementation using SciKit Learn.mp4 |
195.10Мб |
| 11. A Naive Bayes Implementation using SciKit Learn.vtt |
29.17Кб |
| 11. How to Create 3-Dimensional Charts.mp4 |
193.48Мб |
| 11. How to Create 3-Dimensional Charts.vtt |
22.83Кб |
| 11. Model Evaluation and the Confusion Matrix.mp4 |
62.76Мб |
| 11. Model Evaluation and the Confusion Matrix.vtt |
9.41Кб |
| 11. Name Scoping and Image Visualisation in Tensorboard.mp4 |
155.37Мб |
| 11. Name Scoping and Image Visualisation in Tensorboard.vtt |
22.98Кб |
| 11. Visualising Correlations with a Heatmap.mp4 |
168.65Мб |
| 11. Visualising Correlations with a Heatmap.vtt |
20.68Кб |
| 12.1 08 Naive Bayes with scikit-learn.ipynb.zip.zip |
13.26Кб |
| 12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip.zip |
243.05Кб |
| 12. Create a Pandas DataFrame of Email Bodies.mp4 |
48.67Мб |
| 12. Create a Pandas DataFrame of Email Bodies.vtt |
6.24Кб |
| 12. Different Model Architectures Experimenting with Dropout.mp4 |
213.68Мб |
| 12. Different Model Architectures Experimenting with Dropout.vtt |
26.32Кб |
| 12. Download the Complete Notebook Here.html |
242б |
| 12. Model Evaluation and the Confusion Matrix.mp4 |
251.84Мб |
| 12. Model Evaluation and the Confusion Matrix.vtt |
35.15Кб |
| 12. Python Functions Coding Exercise - Part 1.html |
149б |
| 12. Techniques to Style Scatter Plots.mp4 |
128.53Мб |
| 12. Techniques to Style Scatter Plots.vtt |
17.68Кб |
| 12. Understanding Partial Derivatives and How to use SymPy.mp4 |
132.82Мб |
| 12. Understanding Partial Derivatives and How to use SymPy.vtt |
17.38Кб |
| 13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 |
128.20Мб |
| 13. [Python] - Functions - Part 2 Arguments & Parameters.vtt |
17.58Кб |
| 13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip.zip |
120.11Кб |
| 13. A Note for the Next Lesson.html |
476б |
| 13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 |
121.93Мб |
| 13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.vtt |
15.32Кб |
| 13. Download the Complete Notebook Here.html |
242б |
| 13. Implementing Batch Gradient Descent with SymPy.mp4 |
86.83Мб |
| 13. Implementing Batch Gradient Descent with SymPy.vtt |
11.23Кб |
| 13. Prediction and Model Evaluation.mp4 |
110.71Мб |
| 13. Prediction and Model Evaluation.vtt |
16.52Кб |
| 14. [Python] - Loops and Performance Considerations.mp4 |
131.08Мб |
| 14. [Python] - Loops and Performance Considerations.vtt |
15.52Кб |
| 14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip.zip |
6.60Кб |
| 14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4 |
61.83Мб |
| 14. Cleaning Data (Part 2) Working with a DataFrame Index.vtt |
8.12Кб |
| 14. Download the Complete Notebook Here.html |
242б |
| 14. Python Functions Coding Exercise - Part 2.html |
149б |
| 14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 |
214.40Мб |
| 14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.vtt |
24.44Кб |
| 15. [Python] - Functions - Part 3 Results & Return Values.mp4 |
82.64Мб |
| 15. [Python] - Functions - Part 3 Results & Return Values.vtt |
14.05Кб |
| 15. Reshaping and Slicing N-Dimensional Arrays.mp4 |
140.82Мб |
| 15. Reshaping and Slicing N-Dimensional Arrays.vtt |
19.39Кб |
| 15. Saving a JSON File with Pandas.mp4 |
56.35Мб |
| 15. Saving a JSON File with Pandas.vtt |
6.02Кб |
| 15. Understanding Multivariable Regression.mp4 |
48.81Мб |
| 15. Understanding Multivariable Regression.vtt |
6.38Кб |
| 16. Concatenating Numpy Arrays.mp4 |
71.33Мб |
| 16. Concatenating Numpy Arrays.vtt |
7.64Кб |
| 16. Data Visualisation (Part 1) Pie Charts.mp4 |
90.69Мб |
| 16. Data Visualisation (Part 1) Pie Charts.vtt |
13.90Кб |
| 16. How to Shuffle and Split Training & Testing Data.mp4 |
64.35Мб |
| 16. How to Shuffle and Split Training & Testing Data.vtt |
10.08Кб |
| 16. Python Functions Coding Exercise - Part 3.html |
149б |
| 17. [Python] - Objects - Understanding Attributes and Methods.mp4 |
156.77Мб |
| 17. [Python] - Objects - Understanding Attributes and Methods.vtt |
25.19Кб |
| 17. Data Visualisation (Part 2) Donut Charts.mp4 |
61.79Мб |
| 17. Data Visualisation (Part 2) Donut Charts.vtt |
8.11Кб |
| 17. Introduction to the Mean Squared Error (MSE).mp4 |
64.57Мб |
| 17. Introduction to the Mean Squared Error (MSE).vtt |
10.83Кб |
| 17. Running a Multivariable Regression.mp4 |
55.57Мб |
| 17. Running a Multivariable Regression.vtt |
8.44Кб |
| 18. How to Calculate the Model Fit with R-Squared.mp4 |
32.40Мб |
| 18. How to Calculate the Model Fit with R-Squared.vtt |
3.85Кб |
| 18. How to Make Sense of Python Documentation for Data Visualisation.mp4 |
171.46Мб |
| 18. How to Make Sense of Python Documentation for Data Visualisation.vtt |
22.49Кб |
| 18. Introduction to Natural Language Processing (NLP).mp4 |
50.81Мб |
| 18. Introduction to Natural Language Processing (NLP).vtt |
7.00Кб |
| 18. Transposing and Reshaping Arrays.mp4 |
86.91Мб |
| 18. Transposing and Reshaping Arrays.vtt |
11.81Кб |
| 19. Implementing a MSE Cost Function.mp4 |
81.12Мб |
| 19. Implementing a MSE Cost Function.vtt |
11.65Кб |
| 19. Introduction to Model Evaluation.mp4 |
15.99Мб |
| 19. Introduction to Model Evaluation.vtt |
3.20Кб |
| 19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 |
117.76Мб |
| 19. Tokenizing, Removing Stop Words and the Python Set Data Structure.vtt |
15.99Кб |
| 19. Working with Python Objects to Analyse Data.mp4 |
169.98Мб |
| 19. Working with Python Objects to Analyse Data.vtt |
22.97Кб |
| 2.1 cost_revenue_dirty.csv.csv |
374.68Кб |
| 2.1 Course Resources.html |
122б |
| 2.1 MNIST.zip.zip |
14.77Мб |
| 2.1 SpamData.zip.zip |
21.28Мб |
| 2.2 The-Numbers Movie Budgets.html |
102б |
| 2. Create a Full Matrix.mp4 |
132.24Мб |
| 2. Create a Full Matrix.vtt |
18.82Кб |
| 2. Gather & Clean the Data.mp4 |
97.02Мб |
| 2. Gather & Clean the Data.vtt |
11.74Кб |
| 2. Gathering Email Data and Working with Archives & Text Editors.mp4 |
112.05Мб |
| 2. Gathering Email Data and Working with Archives & Text Editors.vtt |
11.89Кб |
| 2. Gathering the Boston House Price Data.mp4 |
56.24Мб |
| 2. Gathering the Boston House Price Data.vtt |
7.35Кб |
| 2. Getting the Data and Loading it into Numpy Arrays.mp4 |
52.82Мб |
| 2. Getting the Data and Loading it into Numpy Arrays.vtt |
7.91Кб |
| 2. How a Machine Learns.mp4 |
22.78Мб |
| 2. How a Machine Learns.vtt |
6.08Кб |
| 2. Installing Tensorflow and Keras for Jupyter.mp4 |
42.10Мб |
| 2. Installing Tensorflow and Keras for Jupyter.vtt |
5.72Кб |
| 2. Joint Conditional Probability (Part 1) Dot Product.mp4 |
66.41Мб |
| 2. Joint Conditional Probability (Part 1) Dot Product.vtt |
11.15Кб |
| 2. Layers, Feature Generation and Learning.mp4 |
146.70Мб |
| 2. Layers, Feature Generation and Learning.vtt |
24.25Кб |
| 2. Mac Users - Install Anaconda.mp4 |
52.41Мб |
| 2. Mac Users - Install Anaconda.vtt |
6.83Кб |
| 2. What is Data Science.mp4 |
42.86Мб |
| 2. What is Data Science.vtt |
4.86Кб |
| 2. What Modules Do You Want to See.html |
431б |
| 20. [Python] - Tips, Code Style and Naming Conventions.mp4 |
81.54Мб |
| 20. [Python] - Tips, Code Style and Naming Conventions.vtt |
14.12Кб |
| 20. Improving the Model by Transforming the Data.mp4 |
126.87Мб |
| 20. Improving the Model by Transforming the Data.vtt |
18.69Кб |
| 20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 |
73.16Мб |
| 20. Understanding Nested Loops and Plotting the MSE Function (Part 1).vtt |
11.95Кб |
| 20. Word Stemming & Removing Punctuation.mp4 |
71.44Мб |
| 20. Word Stemming & Removing Punctuation.vtt |
8.98Кб |
| 21.1 02 Python Intro.ipynb.zip.zip |
36.44Кб |
| 21. Download the Complete Notebook Here.html |
242б |
| 21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 |
65.40Мб |
| 21. How to Interpret Coefficients using p-Values and Statistical Significance.vtt |
9.46Кб |
| 21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 |
124.88Мб |
| 21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).vtt |
15.30Кб |
| 21. Removing HTML tags with BeautifulSoup.mp4 |
95.82Мб |
| 21. Removing HTML tags with BeautifulSoup.vtt |
9.51Кб |
| 22. Creating a Function for Text Processing.mp4 |
53.91Мб |
| 22. Running Gradient Descent with a MSE Cost Function.mp4 |
111.22Мб |
| 22. Running Gradient Descent with a MSE Cost Function.vtt |
19.61Кб |
| 22. Understanding VIF & Testing for Multicollinearity.mp4 |
143.83Мб |
| 22. Understanding VIF & Testing for Multicollinearity.vtt |
22.11Кб |
| 23. A Note for the Next Lesson.html |
476б |
| 23. Model Simiplication & Baysian Information Criterion.mp4 |
150.15Мб |
| 23. Model Simiplication & Baysian Information Criterion.vtt |
19.90Кб |
| 23. Visualising the Optimisation on a 3D Surface.mp4 |
74.82Мб |
| 23. Visualising the Optimisation on a 3D Surface.vtt |
9.18Кб |
| 24.1 03 Gradient Descent.ipynb.zip.zip |
1.14Мб |
| 24. Advanced Subsetting on DataFrames the apply() Function.mp4 |
83.40Мб |
| 24. Advanced Subsetting on DataFrames the apply() Function.vtt |
11.62Кб |
| 24. Download the Complete Notebook Here.html |
242б |
| 24. How to Analyse and Plot Regression Residuals.mp4 |
64.18Мб |
| 24. How to Analyse and Plot Regression Residuals.vtt |
12.41Кб |
| 25. [Python] - Logical Operators to Create Subsets and Indices.mp4 |
86.41Мб |
| 25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 |
124.42Мб |
| 25. Residual Analysis (Part 1) Predicted vs Actual Values.vtt |
15.41Кб |
| 26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 |
153.02Мб |
| 26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.vtt |
19.00Кб |
| 26. Word Clouds & How to install Additional Python Packages.mp4 |
79.49Мб |
| 26. Word Clouds & How to install Additional Python Packages.vtt |
10.14Кб |
| 27. Creating your First Word Cloud.mp4 |
98.44Мб |
| 27. Creating your First Word Cloud.vtt |
11.87Кб |
| 27. Making Predictions (Part 1) MSE & R-Squared.mp4 |
152.68Мб |
| 27. Making Predictions (Part 1) MSE & R-Squared.vtt |
20.07Кб |
| 28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 |
84.85Мб |
| 28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.vtt |
12.66Кб |
| 28. Styling the Word Cloud with a Mask.mp4 |
131.37Мб |
| 28. Styling the Word Cloud with a Mask.vtt |
14.23Кб |
| 29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 |
131.31Мб |
| 29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.vtt |
17.95Кб |
| 29. Solving the Hamlet Challenge.mp4 |
57.11Мб |
| 29. Solving the Hamlet Challenge.vtt |
5.26Кб |
| 3.1 cost_revenue_clean.csv.csv |
90.82Кб |
| 3.1 ML Data Science Syllabus.pdf.pdf |
103.97Кб |
| 3.2 Try Jupyter in your Browser.html |
85б |
| 3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 |
87.14Мб |
| 3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.vtt |
13.29Кб |
| 3. Costs and Disadvantages of Neural Networks.mp4 |
91.99Мб |
| 3. Costs and Disadvantages of Neural Networks.vtt |
16.81Кб |
| 3. Count the Tokens to Train the Naive Bayes Model.mp4 |
96.19Мб |
| 3. Count the Tokens to Train the Naive Bayes Model.vtt |
16.02Кб |
| 3. Data Exploration and Understanding the Structure of the Input Data.mp4 |
32.41Мб |
| 3. Data Exploration and Understanding the Structure of the Input Data.vtt |
5.74Кб |
| 3. Does LSD Make You Better at Maths.mp4 |
42.26Мб |
| 3. Does LSD Make You Better at Maths.vtt |
6.23Кб |
| 3. Download the Syllabus.html |
1.03Кб |
| 3. Explore & Visualise the Data with Python.mp4 |
148.16Мб |
| 3. Explore & Visualise the Data with Python.vtt |
26.38Кб |
| 3. Gathering the CIFAR 10 Dataset.mp4 |
31.36Мб |
| 3. Gathering the CIFAR 10 Dataset.vtt |
5.42Кб |
| 3. How to Add the Lesson Resources to the Project.mp4 |
28.91Мб |
| 3. How to Add the Lesson Resources to the Project.vtt |
4.08Кб |
| 3. Introduction to Cost Functions.mp4 |
66.20Мб |
| 3. Introduction to Cost Functions.vtt |
7.89Кб |
| 3. Joint Conditional Probablity (Part 2) Priors.mp4 |
63.98Мб |
| 3. Joint Conditional Probablity (Part 2) Priors.vtt |
9.34Кб |
| 3. Stay in Touch!.html |
1.05Кб |
| 30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 |
134.39Мб |
| 30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).vtt |
18.50Кб |
| 30. Styling Word Clouds with Custom Fonts.mp4 |
127.30Мб |
| 30. Styling Word Clouds with Custom Fonts.vtt |
12.55Кб |
| 31. Create the Vocabulary for the Spam Classifier.mp4 |
106.97Мб |
| 31. Create the Vocabulary for the Spam Classifier.vtt |
15.37Кб |
| 31. Python Conditional Statement Coding Exercise.html |
149б |
| 32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 |
244.16Мб |
| 32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.vtt |
24.49Кб |
| 32. Coding Challenge Check for Membership in a Collection.mp4 |
32.35Мб |
| 32. Coding Challenge Check for Membership in a Collection.vtt |
5.08Кб |
| 33.1 04 Multivariable Regression.ipynb.zip.zip |
3.55Мб |
| 33.2 04 Valuation Tool.ipynb.zip.zip |
2.93Кб |
| 33. Coding Challenge Find the Longest Email.mp4 |
54.47Мб |
| 33. Coding Challenge Find the Longest Email.vtt |
6.51Кб |
| 33. Download the Complete Notebook Here.html |
242б |
| 34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 |
87.63Мб |
| 34. Sparse Matrix (Part 1) Split the Training and Testing Data.vtt |
13.43Кб |
| 35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 |
137.23Мб |
| 35. Sparse Matrix (Part 2) Data Munging with Nested Loops.vtt |
19.80Кб |
| 36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 |
80.50Мб |
| 36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.vtt |
10.63Кб |
| 37. Coding Challenge Solution Preparing the Test Data.mp4 |
28.93Мб |
| 37. Coding Challenge Solution Preparing the Test Data.vtt |
4.25Кб |
| 38. Checkpoint Understanding the Data.mp4 |
96.37Мб |
| 38. Checkpoint Understanding the Data.vtt |
12.00Кб |
| 39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip.zip |
988.02Кб |
| 39. Download the Complete Notebook Here.html |
242б |
| 4.1 01 Linear Regression (checkpoint).ipynb.zip.zip |
37.64Кб |
| 4.1 12 Rules to Learn to Code.pdf.pdf |
2.25Мб |
| 4.1 App Brewery Cornell Notes Template.html |
141б |
| 4.1 TF_Keras_Classification_Images.zip.zip |
501.10Кб |
| 4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 |
135.03Мб |
| 4. Clean and Explore the Data (Part 2) Find Missing Values.vtt |
15.83Кб |
| 4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 |
70.18Мб |
| 4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.vtt |
11.12Кб |
| 4. Download the 12 Rules to Learn to Code.html |
1.13Кб |
| 4. Exploring the CIFAR Data.mp4 |
110.31Мб |
| 4. Exploring the CIFAR Data.vtt |
15.81Кб |
| 4. LaTeX Markdown and Generating Data with Numpy.mp4 |
90.52Мб |
| 4. LaTeX Markdown and Generating Data with Numpy.vtt |
14.71Кб |
| 4. Making Predictions Comparing Joint Probabilities.mp4 |
52.34Мб |
| 4. Making Predictions Comparing Joint Probabilities.vtt |
8.53Кб |
| 4. Preprocessing Image Data and How RGB Works.mp4 |
93.61Мб |
| 4. Preprocessing Image Data and How RGB Works.vtt |
14.12Кб |
| 4. Sum the Tokens across the Spam and Ham Subsets.mp4 |
46.71Мб |
| 4. Sum the Tokens across the Spam and Ham Subsets.vtt |
6.89Кб |
| 4. The Intuition behind the Linear Regression Model.mp4 |
29.63Мб |
| 4. The Intuition behind the Linear Regression Model.vtt |
9.14Кб |
| 4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 |
33.39Мб |
| 4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.vtt |
5.18Кб |
| 4. Top Tips for Succeeding on this Course.html |
2.09Кб |
| 5. [Python] - Variables and Types.mp4 |
71.37Мб |
| 5. [Python] - Variables and Types.vtt |
14.20Кб |
| 5. Analyse and Evaluate the Results.mp4 |
105.17Мб |
| 5. Analyse and Evaluate the Results.vtt |
18.88Кб |
| 5. Basic Probability.mp4 |
28.56Мб |
| 5. Basic Probability.vtt |
4.54Кб |
| 5. Calculate the Token Probabilities and Save the Trained Model.mp4 |
53.46Мб |
| 5. Calculate the Token Probabilities and Save the Trained Model.vtt |
8.24Кб |
| 5. Course Resources List.html |
1.13Кб |
| 5. Importing Keras Models and the Tensorflow Graph.mp4 |
65.47Мб |
| 5. Importing Keras Models and the Tensorflow Graph.vtt |
10.11Кб |
| 5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 |
93.16Мб |
| 5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4.jpg |
71.56Кб |
| 5. Pre-processing Scaling Inputs and Creating a Validation Dataset.txt |
271б |
| 5. Pre-processing Scaling Inputs and Creating a Validation Dataset.vtt |
17.40Кб |
| 5. The Accuracy Metric.mp4 |
40.54Мб |
| 5. The Accuracy Metric.vtt |
6.69Кб |
| 5. Understanding the Power Rule & Creating Charts with Subplots.mp4 |
90.17Мб |
| 5. Understanding the Power Rule & Creating Charts with Subplots.vtt |
15.24Кб |
| 5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 |
64.56Мб |
| 5. Visualising Data (Part 1) Historams, Distributions & Outliers.vtt |
12.06Кб |
| 5. What is a Tensor.mp4 |
45.39Мб |
| 5. What is a Tensor.vtt |
7.94Кб |
| 6. [Python] - Loops and the Gradient Descent Algorithm.mp4 |
287.45Мб |
| 6. [Python] - Loops and the Gradient Descent Algorithm.vtt |
35.86Кб |
| 6.1 01 Linear Regression (complete).ipynb.zip.zip |
75.28Кб |
| 6. Coding Challenge Prepare the Test Data.mp4 |
35.60Мб |
| 6. Coding Challenge Prepare the Test Data.vtt |
4.52Кб |
| 6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 |
103.61Мб |
| 6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.vtt |
16.31Кб |
| 6. Creating Tensors and Setting up the Neural Network Architecture.mp4 |
150.86Мб |
| 6. Creating Tensors and Setting up the Neural Network Architecture.vtt |
25.37Кб |
| 6. Download the Complete Notebook Here.html |
242б |
| 6. Joint & Conditional Probability.mp4 |
141.82Мб |
| 6. Joint & Conditional Probability.vtt |
16.75Кб |
| 6. Making Predictions using InceptionResNet.mp4 |
134.58Мб |
| 6. Making Predictions using InceptionResNet.vtt |
16.48Кб |
| 6. Python Variable Coding Exercise.html |
149б |
| 6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 |
57.32Мб |
| 6. Visualising Data (Part 2) Seaborn and Probability Density Functions.vtt |
7.67Кб |
| 6. Visualising the Decision Boundary.mp4 |
205.31Мб |
| 6. Visualising the Decision Boundary.vtt |
29.22Кб |
| 7. [Python] - Lists and Arrays.mp4 |
53.47Мб |
| 7. [Python] - Lists and Arrays.mp4.jpg |
59.00Кб |
| 7. [Python] - Lists and Arrays.txt |
235б |
| 7. [Python] - Lists and Arrays.vtt |
10.49Кб |
| 7.1 07 Bayes Classifier - Training.ipynb.zip.zip |
5.82Кб |
| 7. Bayes Theorem.mp4 |
83.12Мб |
| 7. Bayes Theorem.vtt |
12.80Кб |
| 7. Coding Challenge Solution Using other Keras Models.mp4 |
103.54Мб |
| 7. Coding Challenge Solution Using other Keras Models.vtt |
11.41Кб |
| 7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 |
75.12Мб |
| 7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.vtt |
12.42Кб |
| 7. Download the Complete Notebook Here.html |
242б |
| 7. False Positive vs False Negatives.mp4 |
63.25Мб |
| 7. False Positive vs False Negatives.vtt |
11.22Кб |
| 7. Interacting with the Operating System and the Python Try-Catch Block.mp4 |
133.41Мб |
| 7. Interacting with the Operating System and the Python Try-Catch Block.vtt |
20.80Кб |
| 7. Join the Student Community.html |
730б |
| 7. Python Loops Coding Exercise.html |
149б |
| 7. Working with Index Data, Pandas Series, and Dummy Variables.mp4 |
140.77Мб |
| 7. Working with Index Data, Pandas Series, and Dummy Variables.vtt |
17.62Кб |
| 8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 |
291.34Мб |
| 8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).vtt |
36.40Кб |
| 8.1 09 Neural Nets Pretrained Image Classification.ipynb.zip.zip |
571.83Кб |
| 8. Download the Complete Notebook Here.html |
264б |
| 8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 |
100.43Мб |
| 8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.vtt |
12.36Кб |
| 8. Python Lists Coding Exercise.html |
149б |
| 8. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 |
60.90Мб |
| 8. Reading Files (Part 1) Absolute Paths and Relative Paths.vtt |
10.01Кб |
| 8. TensorFlow Sessions and Batching Data.mp4 |
100.33Мб |
| 8. TensorFlow Sessions and Batching Data.vtt |
17.85Кб |
| 8. The Recall Metric.mp4 |
28.16Мб |
| 8. The Recall Metric.vtt |
5.74Кб |
| 8. Understanding Descriptive Statistics the Mean vs the Median.mp4 |
62.19Мб |
| 8. Understanding Descriptive Statistics the Mean vs the Median.vtt |
10.45Кб |
| 9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 |
219.02Мб |
| 9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).vtt |
28.47Кб |
| 9. [Python & Pandas] - Dataframes and Series.mp4 |
153.21Мб |
| 9. [Python & Pandas] - Dataframes and Series.vtt |
24.01Кб |
| 9.1 lsd_math_score_data.csv.csv |
155б |
| 9. Introduction to Correlation Understanding Strength & Direction.mp4 |
33.09Мб |
| 9. Introduction to Correlation Understanding Strength & Direction.vtt |
7.13Кб |
| 9. Reading Files (Part 2) Stream Objects and Email Structure.mp4 |
104.33Мб |
| 9. Reading Files (Part 2) Stream Objects and Email Structure.vtt |
12.37Кб |
| 9. Tensorboard Summaries and the Filewriter.mp4 |
128.29Мб |
| 9. Tensorboard Summaries and the Filewriter.vtt |
20.33Кб |
| 9. The Precision Metric.mp4 |
53.34Мб |
| 9. The Precision Metric.vtt |
8.32Кб |
| 9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.vtt |
24.62Кб |
| Course Downloaded from coursedrive.org.txt |
538б |
| Must Read.txt |
540б |
| ReadMe.txt |
538б |
| ReadMe.txt |
538б |
| Visit Coursedrive.org.url |
124б |
| Visit Coursedrive.org.url |
124б |
| Visit Coursedrive.org.url |
124б |
| Visit Coursedrive.org.url |
124б |