Download Udemy - Complete 2020 Data Science & Machine Learning Bootcamp Torrent

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Udemy - Complete 2020 Data Science & Machine Learning Bootcamp

Course

1. Introduction to the Course

1. What is Machine Learning.mp4	MP4	45.29 MB
1. What is Machine Learning.srt	SRT	6.91 KB
2. What is Data Science.mp4	MP4	42.86 MB
2. What is Data Science.srt	SRT	5.72 KB
3. Download the Syllabus.html	HTML	1.03 KB
3.1 ML Data Science Syllabus.pdf	PDF	103.97 KB
4. Top Tips for Succeeding on this Course.html	HTML	2.09 KB
4.1 App Brewery Cornell Notes Template.html	HTML	141 B
5. Course Resources List.html	HTML	1.13 KB

10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow

1. Solving a Business Problem with Image Classification.mp4	MP4	30.53 MB
1. Solving a Business Problem with Image Classification.srt	SRT	4.97 KB
1.1 Course Resources.html	HTML	122 B
10. Use the Model to Make Predictions.mp4	MP4	218.25 MB
10. Use the Model to Make Predictions.srt	SRT	32.97 KB
11. Model Evaluation and the Confusion Matrix.mp4	MP4	62.76 MB
11. Model Evaluation and the Confusion Matrix.srt	SRT	10.8 KB
12. Model Evaluation and the Confusion Matrix.mp4	MP4	251.83 MB
12. Model Evaluation and the Confusion Matrix.srt	SRT	40.5 KB
13. Download the Complete Notebook Here.html	HTML	242 B
13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip	ZIP	120.11 KB
14. Any Feedback on this Section.html	HTML	521 B
2. Installing Tensorflow and Keras for Jupyter.mp4	MP4	42.1 MB
2. Installing Tensorflow and Keras for Jupyter.srt	SRT	6.42 KB
3. Gathering the CIFAR 10 Dataset.mp4	MP4	31.37 MB
3. Gathering the CIFAR 10 Dataset.srt	SRT	6.1 KB
4. Exploring the CIFAR Data.mp4	MP4	110.31 MB
4. Exploring the CIFAR Data.srt	SRT	18.23 KB
5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4	MP4	93.16 MB
5. Pre-processing Scaling Inputs and Creating a Validation Dataset.srt	SRT	19.92 KB
6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4	MP4	103.6 MB
6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.srt	SRT	18.63 KB
7. Interacting with the Operating System and the Python Try-Catch Block.mp4	MP4	133.41 MB
7. Interacting with the Operating System and the Python Try-Catch Block.srt	SRT	23.69 KB
8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4	MP4	100.42 MB
8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.srt	SRT	14.1 KB
9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4	MP4	191.54 MB
9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.srt	SRT	28.28 KB

11. Use Tensorflow to Classify Handwritten Digits

1. What's coming up.mp4	MP4	7.1 MB
1. What's coming up.srt	SRT	2.49 KB
1.1 Course Resources.html	HTML	122 B
10. Understanding the Tensorflow Graph Nodes and Edges.mp4	MP4	115.75 MB
10. Understanding the Tensorflow Graph Nodes and Edges.srt	SRT	21.25 KB
11. Name Scoping and Image Visualisation in Tensorboard.mp4	MP4	155.37 MB
11. Name Scoping and Image Visualisation in Tensorboard.srt	SRT	26.26 KB
12. Different Model Architectures Experimenting with Dropout.mp4	MP4	213.67 MB
12. Different Model Architectures Experimenting with Dropout.srt	SRT	30.11 KB
13. Prediction and Model Evaluation.mp4	MP4	110.72 MB
13. Prediction and Model Evaluation.srt	SRT	18.9 KB
14. Download the Complete Notebook Here.html	HTML	242 B
14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip	ZIP	6.6 KB
15. Any Feedback on this Section.html	HTML	499 B
2. Getting the Data and Loading it into Numpy Arrays.mp4	MP4	52.82 MB
2. Getting the Data and Loading it into Numpy Arrays.srt	SRT	9.01 KB
2.1 MNIST.zip	ZIP	14.77 MB
3. Data Exploration and Understanding the Structure of the Input Data.mp4	MP4	32.41 MB
3. Data Exploration and Understanding the Structure of the Input Data.srt	SRT	6.49 KB
4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4	MP4	70.19 MB
4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.srt	SRT	12.67 KB
5. What is a Tensor.mp4	MP4	45.39 MB
5. What is a Tensor.srt	SRT	8.99 KB
6. Creating Tensors and Setting up the Neural Network Architecture.mp4	MP4	150.86 MB
6. Creating Tensors and Setting up the Neural Network Architecture.srt	SRT	29.05 KB
7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4	MP4	75.11 MB
7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.srt	SRT	14.15 KB
8. TensorFlow Sessions and Batching Data.mp4	MP4	100.32 MB
8. TensorFlow Sessions and Batching Data.srt	SRT	20.5 KB
9. Tensorboard Summaries and the Filewriter.mp4	MP4	128.29 MB
9. Tensorboard Summaries and the Filewriter.srt	SRT	23.21 KB

12. Serving a Tensorflow Model through a Website

1. What you'll make.mp4	MP4	38.44 MB
1. What you'll make.srt	SRT	9.76 KB
10. Drawing on an HTML Canvas.mp4	MP4	171.97 MB
10. Drawing on an HTML Canvas.srt	SRT	37.83 KB
11. Data Pre-Processing for Tensorflow.js.mp4	MP4	61.89 MB
11. Data Pre-Processing for Tensorflow.js.srt	SRT	11.92 KB
12. Introduction to OpenCV.mp4	MP4	235.33 MB
12. Introduction to OpenCV.srt	SRT	38.37 KB
12.1 math_garden_stub 12.12 checkpoint.zip	ZIP	4.09 MB
13. Resizing and Adding Padding to Images.mp4	MP4	157.5 MB
13. Resizing and Adding Padding to Images.srt	SRT	26.86 KB
14. Calculating the Centre of Mass and Shifting the Image.mp4	MP4	223.26 MB
14. Calculating the Centre of Mass and Shifting the Image.srt	SRT	35.49 KB
15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4	MP4	104.41 MB
15. Making a Prediction from a Digit drawn on the HTML Canvas.srt	SRT	17.04 KB
16. Adding the Game Logic.mp4	MP4	172.83 MB
16. Adding the Game Logic.srt	SRT	38.09 KB
16.1 math_garden_stub complete.zip	ZIP	4.09 MB
17. Publish and Share your Website!.mp4	MP4	38.75 MB
17. Publish and Share your Website!.srt	SRT	9.51 KB
18. Any Feedback on this Section.html	HTML	500 B
2. Saving Tensorflow Models.mp4	MP4	109.98 MB
2. Saving Tensorflow Models.srt	SRT	21.26 KB
2.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip	ZIP	6.39 KB
3. Loading a SavedModel.mp4	MP4	103.93 MB
3. Loading a SavedModel.srt	SRT	26.16 KB
3.1 MNIST_Model_Load_Files.zip	ZIP	2.84 MB
3.2 12 TF SavedModel Export Completed.ipynb.zip	ZIP	6.13 KB
4. Converting a Model to Tensorflow.js.mp4	MP4	132.49 MB
4. Converting a Model to Tensorflow.js.srt	SRT	21.13 KB
4.1 TFJS.zip	ZIP	1.54 MB
5. Introducing the Website Project and Tooling.mp4	MP4	78.04 MB
5. Introducing the Website Project and Tooling.srt	SRT	17.19 KB
5.1 math_garden_stub.zip	ZIP	44.03 KB
6. HTML and CSS Styling.mp4	MP4	150.23 MB
6. HTML and CSS Styling.srt	SRT	37.89 KB
7. Loading a Tensorflow.js Model and Starting your own Server.mp4	MP4	188.04 MB
7. Loading a Tensorflow.js Model and Starting your own Server.srt	SRT	37.18 KB
7.1 x_test2_ylabel1.txt	TXT	4.59 KB
7.2 x_test0_ylabel7.txt	TXT	4.59 KB
7.3 x_test1_ylabel2.txt	TXT	4.59 KB
8. Adding a Favicon.mp4	MP4	41.51 MB
8. Adding a Favicon.srt	SRT	7.39 KB
9. Styling an HTML Canvas.mp4	MP4	187.37 MB
9. Styling an HTML Canvas.srt	SRT	39.42 KB

13. Next Steps

1. Where next.html	HTML	3.93 KB
2. What Modules Do You Want to See.html	HTML	431 B
3. Stay in Touch!.html	HTML	1.05 KB

2. Predict Movie Box Office Revenue with Linear Regression

1. Introduction to Linear Regression & Specifying the Problem.mp4	MP4	30.32 MB
1. Introduction to Linear Regression & Specifying the Problem.srt	SRT	8.74 KB
1.1 Course Resources.html	HTML	122 B
2. Gather & Clean the Data.mp4	MP4	97.02 MB
2. Gather & Clean the Data.srt	SRT	13.93 KB
2.1 The-Numbers Movie Budgets.html	HTML	102 B
2.2 cost_revenue_dirty.csv	CSV	374.68 KB
3. Explore & Visualise the Data with Python.mp4	MP4	148.15 MB
3. Explore & Visualise the Data with Python.srt	SRT	31.02 KB
3.1 Try Jupyter in your Browser.html	HTML	85 B
3.2 cost_revenue_clean.csv	CSV	90.82 KB
4. The Intuition behind the Linear Regression Model.mp4	MP4	29.63 MB
4. The Intuition behind the Linear Regression Model.srt	SRT	10.84 KB
4.1 01 Linear Regression (checkpoint).ipynb.zip	ZIP	37.64 KB
5. Analyse and Evaluate the Results.mp4	MP4	105.16 MB
5. Analyse and Evaluate the Results.srt	SRT	22.41 KB
6. Download the Complete Notebook Here.html	HTML	242 B
6.1 01 Linear Regression (complete).ipynb.zip	ZIP	75.28 KB
7. Join the Student Community.html	HTML	730 B
8. Any Feedback on this Section.html	HTML	512 B

3. Python Programming for Data Science and Machine Learning

1. Windows Users - Install Anaconda.mp4	MP4	49.6 MB
1. Windows Users - Install Anaconda.srt	SRT	8.78 KB
1.1 Course Resources.html	HTML	122 B
10. [Python] - Module Imports.mp4	MP4	232.07 MB
10. [Python] - Module Imports.srt	SRT	36.12 KB
11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4	MP4	41.61 MB
11. [Python] - Functions - Part 1 Defining and Calling Functions.srt	SRT	10.49 KB
12. Python Functions Coding Exercise - Part 1.html	HTML	156 B
13. [Python] - Functions - Part 2 Arguments & Parameters.mp4	MP4	128.2 MB
13. [Python] - Functions - Part 2 Arguments & Parameters.srt	SRT	20.76 KB
14. Python Functions Coding Exercise - Part 2.html	HTML	156 B
15. [Python] - Functions - Part 3 Results & Return Values.mp4	MP4	82.63 MB
15. [Python] - Functions - Part 3 Results & Return Values.srt	SRT	16.55 KB
16. Python Functions Coding Exercise - Part 3.html	HTML	156 B
17. [Python] - Objects - Understanding Attributes and Methods.mp4	MP4	156.77 MB
17. [Python] - Objects - Understanding Attributes and Methods.srt	SRT	29.86 KB
18. How to Make Sense of Python Documentation for Data Visualisation.mp4	MP4	171.46 MB
18. How to Make Sense of Python Documentation for Data Visualisation.srt	SRT	26.51 KB
19. Working with Python Objects to Analyse Data.mp4	MP4	169.98 MB
19. Working with Python Objects to Analyse Data.srt	SRT	27.29 KB
2. Mac Users - Install Anaconda.mp4	MP4	52.41 MB
2. Mac Users - Install Anaconda.srt	SRT	8.05 KB
2.1 Course Resources.html	HTML	122 B
20. [Python] - Tips, Code Style and Naming Conventions.mp4	MP4	81.53 MB
20. [Python] - Tips, Code Style and Naming Conventions.srt	SRT	16.72 KB
21. Download the Complete Notebook Here.html	HTML	242 B
21.1 02 Python Intro.ipynb.zip	ZIP	36.44 KB
22. Any Feedback on this Section.html	HTML	513 B
3. Does LSD Make You Better at Maths.mp4	MP4	42.25 MB
3. Does LSD Make You Better at Maths.srt	SRT	7.35 KB
4. Download the 12 Rules to Learn to Code.html	HTML	1.13 KB
4.1 12 Rules to Learn to Code.pdf	PDF	2.25 MB
5. [Python] - Variables and Types.mp4	MP4	71.36 MB
5. [Python] - Variables and Types.srt	SRT	16.55 KB
6. Python Variable Coding Exercise.html	HTML	156 B
7. [Python] - Lists and Arrays.mp4	MP4	53.47 MB
7. [Python] - Lists and Arrays.srt	SRT	12.15 KB
8. Python Lists Coding Exercise.html	HTML	156 B
9. [Python & Pandas] - Dataframes and Series.mp4	MP4	153.2 MB
9. [Python & Pandas] - Dataframes and Series.srt	SRT	28.09 KB
9.1 lsd_math_score_data.csv	CSV	155 B

4. Introduction to Optimisation and the Gradient Descent Algorithm

1. What's Coming Up.mp4	MP4	20.92 MB
1. What's Coming Up.srt	SRT	3.83 KB
1.1 Course Resources.html	HTML	122 B
10. Understanding the Learning Rate.mp4	MP4	236.6 MB
10. Understanding the Learning Rate.srt	SRT	37.72 KB
11. How to Create 3-Dimensional Charts.mp4	MP4	193.48 MB
11. How to Create 3-Dimensional Charts.srt	SRT	26.1 KB
12. Understanding Partial Derivatives and How to use SymPy.mp4	MP4	132.81 MB
12. Understanding Partial Derivatives and How to use SymPy.srt	SRT	20.23 KB
13. Implementing Batch Gradient Descent with SymPy.mp4	MP4	86.82 MB
13. Implementing Batch Gradient Descent with SymPy.srt	SRT	12.93 KB
14. [Python] - Loops and Performance Considerations.mp4	MP4	131.07 MB
14. [Python] - Loops and Performance Considerations.srt	SRT	18.07 KB
15. Reshaping and Slicing N-Dimensional Arrays.mp4	MP4	140.81 MB
15. Reshaping and Slicing N-Dimensional Arrays.srt	SRT	22.96 KB
16. Concatenating Numpy Arrays.mp4	MP4	71.33 MB
16. Concatenating Numpy Arrays.srt	SRT	8.91 KB
17. Introduction to the Mean Squared Error (MSE).mp4	MP4	64.56 MB
17. Introduction to the Mean Squared Error (MSE).srt	SRT	12.61 KB
18. Transposing and Reshaping Arrays.mp4	MP4	86.9 MB
18. Transposing and Reshaping Arrays.srt	SRT	13.52 KB
19. Implementing a MSE Cost Function.mp4	MP4	81.11 MB
19. Implementing a MSE Cost Function.srt	SRT	13.56 KB
2. How a Machine Learns.mp4	MP4	22.78 MB
2. How a Machine Learns.srt	SRT	7.22 KB
20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4	MP4	73.16 MB
20. Understanding Nested Loops and Plotting the MSE Function (Part 1).srt	SRT	13.94 KB
21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4	MP4	124.88 MB
21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).srt	SRT	17.45 KB
22. Running Gradient Descent with a MSE Cost Function.mp4	MP4	111.22 MB
22. Running Gradient Descent with a MSE Cost Function.srt	SRT	22.32 KB
23. Visualising the Optimisation on a 3D Surface.mp4	MP4	74.81 MB
23. Visualising the Optimisation on a 3D Surface.srt	SRT	10.73 KB
24. Download the Complete Notebook Here.html	HTML	242 B
24.1 03 Gradient Descent.ipynb.zip	ZIP	1.14 MB
25. Any Feedback on this Section.html	HTML	520 B
3. Introduction to Cost Functions.mp4	MP4	66.21 MB
3. Introduction to Cost Functions.srt	SRT	9.49 KB
4. LaTeX Markdown and Generating Data with Numpy.mp4	MP4	90.52 MB
4. LaTeX Markdown and Generating Data with Numpy.srt	SRT	17.28 KB
5. Understanding the Power Rule & Creating Charts with Subplots.mp4	MP4	90.17 MB
5. Understanding the Power Rule & Creating Charts with Subplots.srt	SRT	18.1 KB
6. [Python] - Loops and the Gradient Descent Algorithm.mp4	MP4	287.46 MB
6. [Python] - Loops and the Gradient Descent Algorithm.srt	SRT	44.03 KB
7. Python Loops Coding Exercise.html	HTML	156 B
8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4	MP4	291.33 MB
8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).srt	SRT	42.99 KB
9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4	MP4	219.01 MB
9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).srt	SRT	33.54 KB

5. Predict House Prices with Multivariable Linear Regression

1. Defining the Problem.mp4	MP4	39.91 MB
1. Defining the Problem.srt	SRT	6.46 KB
1.1 Course Resources.html	HTML	122 B
10. Calculating Correlations and the Problem posed by Multicollinearity.mp4	MP4	111.43 MB
10. Calculating Correlations and the Problem posed by Multicollinearity.srt	SRT	17.83 KB
11. Visualising Correlations with a Heatmap.mp4	MP4	168.65 MB
11. Visualising Correlations with a Heatmap.srt	SRT	24.37 KB
12. Techniques to Style Scatter Plots.mp4	MP4	128.53 MB
12. Techniques to Style Scatter Plots.srt	SRT	20.56 KB
13. A Note for the Next Lesson.html	HTML	476 B
14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4	MP4	214.4 MB
14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt	SRT	28.7 KB
15. Understanding Multivariable Regression.mp4	MP4	48.8 MB
15. Understanding Multivariable Regression.srt	SRT	7.52 KB
16. How to Shuffle and Split Training & Testing Data.mp4	MP4	64.34 MB
16. How to Shuffle and Split Training & Testing Data.srt	SRT	11.55 KB
17. Running a Multivariable Regression.mp4	MP4	55.56 MB
17. Running a Multivariable Regression.srt	SRT	9.77 KB
18. How to Calculate the Model Fit with R-Squared.mp4	MP4	32.4 MB
18. How to Calculate the Model Fit with R-Squared.srt	SRT	4.42 KB
19. Introduction to Model Evaluation.mp4	MP4	15.99 MB
19. Introduction to Model Evaluation.srt	SRT	3.81 KB
2. Gathering the Boston House Price Data.mp4	MP4	56.24 MB
2. Gathering the Boston House Price Data.srt	SRT	8.66 KB
20. Improving the Model by Transforming the Data.mp4	MP4	126.87 MB
20. Improving the Model by Transforming the Data.srt	SRT	21.61 KB
21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4	MP4	65.41 MB
21. How to Interpret Coefficients using p-Values and Statistical Significance.srt	SRT	10.78 KB
22. Understanding VIF & Testing for Multicollinearity.mp4	MP4	143.82 MB
22. Understanding VIF & Testing for Multicollinearity.srt	SRT	25.62 KB
23. Model Simplification & Baysian Information Criterion.mp4	MP4	150.15 MB
23. Model Simplification & Baysian Information Criterion.srt	SRT	23.14 KB
24. How to Analyse and Plot Regression Residuals.mp4	MP4	64.18 MB
24. How to Analyse and Plot Regression Residuals.srt	SRT	14.76 KB
25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4	MP4	124.42 MB
25. Residual Analysis (Part 1) Predicted vs Actual Values.srt	SRT	18.24 KB
26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4	MP4	153.01 MB
26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt	SRT	22.76 KB
27. Making Predictions (Part 1) MSE & R-Squared.mp4	MP4	152.68 MB
27. Making Predictions (Part 1) MSE & R-Squared.srt	SRT	23.72 KB
28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4	MP4	84.85 MB
28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt	SRT	14.76 KB
29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4	MP4	131.31 MB
29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.srt	SRT	20.82 KB
3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4	MP4	87.14 MB
3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt	SRT	15.59 KB
30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4	MP4	134.38 MB
30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt	SRT	21.4 KB
31. Python Conditional Statement Coding Exercise.html	HTML	156 B
32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4	MP4	244.17 MB
32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt	SRT	28.42 KB
33. Download the Complete Notebook Here.html	HTML	242 B
33.1 04 Multivariable Regression.ipynb.zip	ZIP	3.54 MB
33.2 04 Valuation Tool.ipynb.zip	ZIP	2.93 KB
33.3 boston_valuation.py	PY	3.05 KB
34. Any Feedback on this Section.html	HTML	512 B
4. Clean and Explore the Data (Part 2) Find Missing Values.mp4	MP4	135.02 MB
4. Clean and Explore the Data (Part 2) Find Missing Values.srt	SRT	18.59 KB
5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4	MP4	64.55 MB
5. Visualising Data (Part 1) Historams, Distributions & Outliers.srt	SRT	14.24 KB
6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4	MP4	57.32 MB
6. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt	SRT	8.98 KB
7. Working with Index Data, Pandas Series, and Dummy Variables.mp4	MP4	140.76 MB
7. Working with Index Data, Pandas Series, and Dummy Variables.srt	SRT	20.72 KB
8. Understanding Descriptive Statistics the Mean vs the Median.mp4	MP4	62.18 MB
8. Understanding Descriptive Statistics the Mean vs the Median.srt	SRT	12.14 KB
9. Introduction to Correlation Understanding Strength & Direction.mp4	MP4	33.09 MB
9. Introduction to Correlation Understanding Strength & Direction.srt	SRT	8.4 KB

6. 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	MP4	42.26 MB
1. How to Translate a Business Problem into a Machine Learning Problem.srt	SRT	9.69 KB
1.1 Course Resources.html	HTML	122 B
10. Extracting the Text in the Email Body.mp4	MP4	47.43 MB
10. Extracting the Text in the Email Body.srt	SRT	6 KB
11. [Python] - Generator Functions & the yield Keyword.mp4	MP4	133.16 MB
11. [Python] - Generator Functions & the yield Keyword.srt	SRT	22.32 KB
12. Create a Pandas DataFrame of Email Bodies.mp4	MP4	48.66 MB
12. Create a Pandas DataFrame of Email Bodies.srt	SRT	7.23 KB
13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4	MP4	121.94 MB
13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.srt	SRT	17.96 KB
14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4	MP4	61.83 MB
14. Cleaning Data (Part 2) Working with a DataFrame Index.srt	SRT	9.23 KB
15. Saving a JSON File with Pandas.mp4	MP4	56.35 MB
15. Saving a JSON File with Pandas.srt	SRT	6.92 KB
16. Data Visualisation (Part 1) Pie Charts.mp4	MP4	90.68 MB
16. Data Visualisation (Part 1) Pie Charts.srt	SRT	16.19 KB
17. Data Visualisation (Part 2) Donut Charts.mp4	MP4	61.78 MB
17. Data Visualisation (Part 2) Donut Charts.srt	SRT	9.56 KB
18. Introduction to Natural Language Processing (NLP).mp4	MP4	50.81 MB
18. Introduction to Natural Language Processing (NLP).srt	SRT	8.19 KB
19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4	MP4	117.75 MB
19. Tokenizing, Removing Stop Words and the Python Set Data Structure.srt	SRT	19.07 KB
2. Gathering Email Data and Working with Archives & Text Editors.mp4	MP4	112.05 MB
2. Gathering Email Data and Working with Archives & Text Editors.srt	SRT	14.13 KB
2.1 SpamData.zip	ZIP	21.29 MB
20. Word Stemming & Removing Punctuation.mp4	MP4	71.44 MB
20. Word Stemming & Removing Punctuation.srt	SRT	10.56 KB
21. Removing HTML tags with BeautifulSoup.mp4	MP4	95.82 MB
21. Removing HTML tags with BeautifulSoup.srt	SRT	11.01 KB
22. Creating a Function for Text Processing.mp4	MP4	53.91 MB
22. Creating a Function for Text Processing.srt	SRT	8.41 KB
23. A Note for the Next Lesson.html	HTML	476 B
24. Advanced Subsetting on DataFrames the apply() Function.mp4	MP4	83.39 MB
24. Advanced Subsetting on DataFrames the apply() Function.srt	SRT	13.53 KB
25. [Python] - Logical Operators to Create Subsets and Indices.mp4	MP4	86.41 MB
25. [Python] - Logical Operators to Create Subsets and Indices.srt	SRT	15.5 KB
26. Word Clouds & How to install Additional Python Packages.mp4	MP4	79.48 MB
26. Word Clouds & How to install Additional Python Packages.srt	SRT	11.97 KB
27. Creating your First Word Cloud.mp4	MP4	98.44 MB
27. Creating your First Word Cloud.srt	SRT	13.67 KB
28. Styling the Word Cloud with a Mask.mp4	MP4	131.37 MB
28. Styling the Word Cloud with a Mask.srt	SRT	16.72 KB
29. Solving the Hamlet Challenge.mp4	MP4	57.1 MB
29. Solving the Hamlet Challenge.srt	SRT	5.99 KB
3. How to Add the Lesson Resources to the Project.mp4	MP4	28.9 MB
3. How to Add the Lesson Resources to the Project.srt	SRT	4.96 KB
30. Styling Word Clouds with Custom Fonts.mp4	MP4	127.29 MB
30. Styling Word Clouds with Custom Fonts.srt	SRT	14.79 KB
31. Create the Vocabulary for the Spam Classifier.mp4	MP4	106.96 MB
31. Create the Vocabulary for the Spam Classifier.srt	SRT	17.79 KB
32. Coding Challenge Check for Membership in a Collection.mp4	MP4	32.34 MB
32. Coding Challenge Check for Membership in a Collection.srt	SRT	6.08 KB
33. Coding Challenge Find the Longest Email.mp4	MP4	54.47 MB
33. Coding Challenge Find the Longest Email.srt	SRT	7.54 KB
34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4	MP4	87.62 MB
34. Sparse Matrix (Part 1) Split the Training and Testing Data.srt	SRT	15.26 KB
35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4	MP4	137.23 MB
35. Sparse Matrix (Part 2) Data Munging with Nested Loops.srt	SRT	22.34 KB
36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4	MP4	80.5 MB
36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.srt	SRT	12.18 KB
37. Coding Challenge Solution Preparing the Test Data.mp4	MP4	28.92 MB
37. Coding Challenge Solution Preparing the Test Data.srt	SRT	4.5 KB
38. Checkpoint Understanding the Data.mp4	MP4	96.37 MB
38. Checkpoint Understanding the Data.srt	SRT	13.65 KB
39. Download the Complete Notebook Here.html	HTML	242 B
39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip	ZIP	978.02 KB
4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4	MP4	33.39 MB
4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.srt	SRT	6.08 KB
40. Any Feedback on this Section.html	HTML	519 B
5. Basic Probability.mp4	MP4	28.55 MB
5. Basic Probability.srt	SRT	5.26 KB
6. Joint & Conditional Probability.mp4	MP4	141.82 MB
6. Joint & Conditional Probability.srt	SRT	19.86 KB
7. Bayes Theorem.mp4	MP4	83.6 MB
7. Bayes Theorem.srt	SRT	15.16 KB
8. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4	MP4	60.9 MB
8. Reading Files (Part 1) Absolute Paths and Relative Paths.srt	SRT	11.71 KB
9. Reading Files (Part 2) Stream Objects and Email Structure.mp4	MP4	104.32 MB
9. Reading Files (Part 2) Stream Objects and Email Structure.srt	SRT	14.57 KB

7. Train a Naive Bayes Classifier to Create a Spam Filter Part 2

1. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4	MP4	72.5 MB
1. Setting up the Notebook and Understanding Delimiters in a Dataset.srt	SRT	11.17 KB
1.1 Course Resources.html	HTML	122 B
1.2 SpamData.zip	ZIP	22.32 MB
2. Create a Full Matrix.mp4	MP4	132.24 MB
2. Create a Full Matrix.srt	SRT	21.72 KB
3. Count the Tokens to Train the Naive Bayes Model.mp4	MP4	96.18 MB
3. Count the Tokens to Train the Naive Bayes Model.srt	SRT	18.35 KB
4. Sum the Tokens across the Spam and Ham Subsets.mp4	MP4	46.71 MB
4. Sum the Tokens across the Spam and Ham Subsets.srt	SRT	7.76 KB
5. Calculate the Token Probabilities and Save the Trained Model.mp4	MP4	53.45 MB
5. Calculate the Token Probabilities and Save the Trained Model.srt	SRT	9.44 KB
6. Coding Challenge Prepare the Test Data.mp4	MP4	35.6 MB
6. Coding Challenge Prepare the Test Data.srt	SRT	5.14 KB
7. Download the Complete Notebook Here.html	HTML	242 B
7.1 07 Bayes Classifier - Training.ipynb.zip	ZIP	5.82 KB
8. Any Feedback on this Section.html	HTML	527 B

8. Test and Evaluate a Naive Bayes Classifier Part 3

1. Set up the Testing Notebook.mp4	MP4	26.45 MB
1. Set up the Testing Notebook.srt	SRT	3.82 KB
1.1 SpamData.zip	ZIP	22.83 MB
1.2 Course Resources.html	HTML	122 B
10. The F-score or F1 Metric.mp4	MP4	24.71 MB
10. The F-score or F1 Metric.srt	SRT	4.48 KB
11. A Naive Bayes Implementation using SciKit Learn.mp4	MP4	195.1 MB
11. A Naive Bayes Implementation using SciKit Learn.srt	SRT	33.68 KB
12. Download the Complete Notebook Here.html	HTML	242 B
12.1 08 Naive Bayes with scikit-learn.ipynb.zip	ZIP	13.26 KB
12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip	ZIP	243.05 KB
13. Any Feedback on this Section.html	HTML	509 B
2. Joint Conditional Probability (Part 1) Dot Product.mp4	MP4	66.4 MB
2. Joint Conditional Probability (Part 1) Dot Product.srt	SRT	12.72 KB
3. Joint Conditional Probablity (Part 2) Priors.mp4	MP4	63.98 MB
3. Joint Conditional Probablity (Part 2) Priors.srt	SRT	10.54 KB
4. Making Predictions Comparing Joint Probabilities.mp4	MP4	52.34 MB
4. Making Predictions Comparing Joint Probabilities.srt	SRT	9.67 KB
5. The Accuracy Metric.mp4	MP4	40.54 MB
5. The Accuracy Metric.srt	SRT	7.65 KB
6. Visualising the Decision Boundary.mp4	MP4	205.31 MB
6. Visualising the Decision Boundary.srt	SRT	33.44 KB
7. False Positive vs False Negatives.mp4	MP4	63.25 MB
7. False Positive vs False Negatives.srt	SRT	12.81 KB
8. The Recall Metric.mp4	MP4	28.15 MB
8. The Recall Metric.srt	SRT	6.54 KB
9. The Precision Metric.mp4	MP4	53.33 MB
9. The Precision Metric.srt	SRT	9.5 KB

9. Introduction to Neural Networks and How to Use Pre-Trained Models

1. The Human Brain and the Inspiration for Artificial Neural Networks.mp4	MP4	51.81 MB
1. The Human Brain and the Inspiration for Artificial Neural Networks.srt	SRT	10.88 KB
1.1 Course Resources.html	HTML	122 B
2. Layers, Feature Generation and Learning.mp4	MP4	146.7 MB
2. Layers, Feature Generation and Learning.srt	SRT	27.79 KB
3. Costs and Disadvantages of Neural Networks.mp4	MP4	91.98 MB
3. Costs and Disadvantages of Neural Networks.srt	SRT	19.24 KB
4. Preprocessing Image Data and How RGB Works.mp4	MP4	93.6 MB
4. Preprocessing Image Data and How RGB Works.srt	SRT	16.15 KB
4.1 TF_Keras_Classification_Images.zip	ZIP	501.1 KB
5. Importing Keras Models and the Tensorflow Graph.mp4	MP4	65.47 MB
5. Importing Keras Models and the Tensorflow Graph.srt	SRT	11.44 KB
6. Making Predictions using InceptionResNet.mp4	MP4	134.58 MB
6. Making Predictions using InceptionResNet.srt	SRT	18.9 KB
7. Coding Challenge Solution Using other Keras Models.mp4	MP4	103.53 MB
7. Coding Challenge Solution Using other Keras Models.srt	SRT	12.94 KB
8. Download the Complete Notebook Here.html	HTML	264 B
8.1 09 Neural Nets Pretrained Image Classification.ipynb.zip	ZIP	571.83 KB
9. Any Feedback on this Section.html	HTML	526 B

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