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