UdemyCourseDownloader Complete Data Science Machine Learning Bootcamp Python

/04. Introduction to Optimisation and the Gradient Descent Algorithm/

8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4

1. What's Coming Up.mp4

1. What's Coming Up.vtt

1.1 Course Resources.html

2. How a Machine Learns.mp4

2. How a Machine Learns.vtt

3. Introduction to Cost Functions.mp4

3. Introduction to Cost Functions.vtt

4. LaTeX Markdown and Generating Data with Numpy.mp4

4. LaTeX Markdown and Generating Data with Numpy.vtt

5. Understanding the Power Rule & Creating Charts with Subplots.mp4

5. Understanding the Power Rule & Creating Charts with Subplots.vtt

6. [Python] - Loops and the Gradient Descent Algorithm.mp4

6. [Python] - Loops and the Gradient Descent Algorithm.vtt

7. Python Loops Coding Exercise.html

8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).vtt

9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4

9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).vtt

10. Understanding the Learning Rate.mp4

10. Understanding the Learning Rate.vtt

11. How to Create 3-Dimensional Charts.mp4

11. How to Create 3-Dimensional Charts.vtt

12. Understanding Partial Derivatives and How to use SymPy.mp4

12. Understanding Partial Derivatives and How to use SymPy.vtt

13. Implementing Batch Gradient Descent with SymPy.mp4

13. Implementing Batch Gradient Descent with SymPy.vtt

14. [Python] - Loops and Performance Considerations.mp4

14. [Python] - Loops and Performance Considerations.vtt

15. Reshaping and Slicing N-Dimensional Arrays.mp4

15. Reshaping and Slicing N-Dimensional Arrays.vtt

16. Concatenating Numpy Arrays.mp4

16. Concatenating Numpy Arrays.vtt

17. Introduction to the Mean Squared Error (MSE).mp4

17. Introduction to the Mean Squared Error (MSE).vtt

18. Transposing and Reshaping Arrays.mp4

18. Transposing and Reshaping Arrays.vtt

19. Implementing a MSE Cost Function.mp4

19. Implementing a MSE Cost Function.vtt

20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4

20. Understanding Nested Loops and Plotting the MSE Function (Part 1).vtt

21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4

21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).vtt

22. Running Gradient Descent with a MSE Cost Function.vtt

23. Visualising the Optimisation on a 3D Surface.mp4

23. Visualising the Optimisation on a 3D Surface.vtt

24. Download the Complete Notebook Here.html

24.1 03 Gradient Descent.ipynb.zip.zip

/

udemycoursedownloader.com.url

Udemy Course downloader.txt

/01. Introduction to the Course/

1. What is Machine Learning.mp4

1. What is Machine Learning.vtt

2. What is Data Science.mp4

2. What is Data Science.vtt

3. Download the Syllabus.html

3.1 ML Data Science Syllabus.pdf.pdf

4. Top Tips for Succeeding on this Course.html

4.1 App Brewery Cornell Notes Template.html

5. Course Resources List.html

/02. Predict Movie Box Office Revenue with Linear Regression/

1. Introduction to Linear Regression & Specifying the Problem.mp4

1. Introduction to Linear Regression & Specifying the Problem.vtt

1.1 Course Resources.html

2. Gather & Clean the Data.mp4

2. Gather & Clean the Data.vtt

2.1 cost_revenue_dirty.csv.csv

2.2 The-Numbers Movie Budgets.html

3. Explore & Visualise the Data with Python.mp4

3. Explore & Visualise the Data with Python.vtt

3.1 cost_revenue_clean.csv.csv

3.2 Try Jupyter in your Browser.html

4. The Intuition behind the Linear Regression Model.mp4

4. The Intuition behind the Linear Regression Model.vtt

4.1 01 Linear Regression (checkpoint).ipynb.zip.zip

5. Analyse and Evaluate the Results.mp4

5. Analyse and Evaluate the Results.vtt

6. Download the Complete Notebook Here.html

6.1 01 Linear Regression (complete).ipynb.zip.zip

7. Join the Student Community.html

/03. Python Programming for Data Science and Machine Learning/

1. Windows Users - Install Anaconda.mp4

1. Windows Users - Install Anaconda.vtt

1.1 Course Resources.html

2. Mac Users - Install Anaconda.mp4

2. Mac Users - Install Anaconda.vtt

2.1 Course Resources.html

3. Does LSD Make You Better at Maths.mp4

3. Does LSD Make You Better at Maths.vtt

4. Download the 12 Rules to Learn to Code.html

4.1 12 Rules to Learn to Code.pdf.pdf

5. [Python] - Variables and Types.mp4

5. [Python] - Variables and Types.vtt

6. Python Variable Coding Exercise.html

7. [Python] - Lists and Arrays.mp4

7. [Python] - Lists and Arrays.mp4.jpg

7. [Python] - Lists and Arrays.txt

7. [Python] - Lists and Arrays.vtt

8. Python Lists Coding Exercise.html

9. [Python & Pandas] - Dataframes and Series.mp4

9. [Python & Pandas] - Dataframes and Series.vtt

9.1 lsd_math_score_data.csv.csv

10. [Python] - Module Imports.mp4

10. [Python] - Module Imports.vtt

11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4

11. [Python] - Functions - Part 1 Defining and Calling Functions.vtt

12. Python Functions Coding Exercise - Part 1.html

13. [Python] - Functions - Part 2 Arguments & Parameters.mp4

13. [Python] - Functions - Part 2 Arguments & Parameters.vtt

14. Python Functions Coding Exercise - Part 2.html

15. [Python] - Functions - Part 3 Results & Return Values.mp4

15. [Python] - Functions - Part 3 Results & Return Values.vtt

16. Python Functions Coding Exercise - Part 3.html

17. [Python] - Objects - Understanding Attributes and Methods.mp4

17. [Python] - Objects - Understanding Attributes and Methods.vtt

18. How to Make Sense of Python Documentation for Data Visualisation.mp4

18. How to Make Sense of Python Documentation for Data Visualisation.vtt

19. Working with Python Objects to Analyse Data.vtt

20. [Python] - Tips, Code Style and Naming Conventions.mp4

20. [Python] - Tips, Code Style and Naming Conventions.vtt

21. Download the Complete Notebook Here.html

21.1 02 Python Intro.ipynb.zip.zip

/05. Predict House Prices with Multivariable Linear Regression/

1. Defining the Problem.mp4

1. Defining the Problem.vtt

1.1 Course Resources.html

2. Gathering the Boston House Price Data.vtt

3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4

3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.vtt

4. Clean and Explore the Data (Part 2) Find Missing Values.mp4

4. Clean and Explore the Data (Part 2) Find Missing Values.vtt

5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4

5. Visualising Data (Part 1) Historams, Distributions & Outliers.vtt

6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4

6. Visualising Data (Part 2) Seaborn and Probability Density Functions.vtt

7. Working with Index Data, Pandas Series, and Dummy Variables.vtt

8. Understanding Descriptive Statistics the Mean vs the Median.mp4

8. Understanding Descriptive Statistics the Mean vs the Median.vtt

9. Introduction to Correlation Understanding Strength & Direction.mp4

9. Introduction to Correlation Understanding Strength & Direction.vtt

10. Calculating Correlations and the Problem posed by Multicollinearity.mp4

10. Calculating Correlations and the Problem posed by Multicollinearity.vtt

11. Visualising Correlations with a Heatmap.mp4

11. Visualising Correlations with a Heatmap.vtt

12. Techniques to Style Scatter Plots.mp4

12. Techniques to Style Scatter Plots.vtt

13. A Note for the Next Lesson.html

14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4

14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.vtt

15. Understanding Multivariable Regression.mp4

15. Understanding Multivariable Regression.vtt

16. How to Shuffle and Split Training & Testing Data.mp4

16. How to Shuffle and Split Training & Testing Data.vtt

17. Running a Multivariable Regression.mp4

17. Running a Multivariable Regression.vtt

18. How to Calculate the Model Fit with R-Squared.mp4

18. How to Calculate the Model Fit with R-Squared.vtt

19. Introduction to Model Evaluation.mp4

19. Introduction to Model Evaluation.vtt

20. Improving the Model by Transforming the Data.mp4

20. Improving the Model by Transforming the Data.vtt

21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4

21. How to Interpret Coefficients using p-Values and Statistical Significance.vtt

22. Understanding VIF & Testing for Multicollinearity.mp4

22. Understanding VIF & Testing for Multicollinearity.vtt

23. Model Simiplication & Baysian Information Criterion.mp4

23. Model Simiplication & Baysian Information Criterion.vtt

24. How to Analyse and Plot Regression Residuals.mp4

24. How to Analyse and Plot Regression Residuals.vtt

25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4

25. Residual Analysis (Part 1) Predicted vs Actual Values.vtt

26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4

26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.vtt

27. Making Predictions (Part 1) MSE & R-Squared.mp4

27. Making Predictions (Part 1) MSE & R-Squared.vtt

28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4

28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.vtt

29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4

29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.vtt

30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4

30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).vtt

31. Python Conditional Statement Coding Exercise.html

32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4

32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.vtt

33. Download the Complete Notebook Here.html

33.1 04 Multivariable Regression.ipynb.zip.zip

33.2 04 Valuation Tool.ipynb.zip.zip

/06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/

1. How to Translate a Business Problem into a Machine Learning Problem.mp4

1. How to Translate a Business Problem into a Machine Learning Problem.vtt

1.1 Course Resources.html

2. Gathering Email Data and Working with Archives & Text Editors.mp4

2. Gathering Email Data and Working with Archives & Text Editors.vtt

2.1 SpamData.zip.zip

3. How to Add the Lesson Resources to the Project.mp4

3. How to Add the Lesson Resources to the Project.vtt

4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4

4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.vtt

5. Basic Probability.mp4

5. Basic Probability.vtt

6. Joint & Conditional Probability.mp4

6. Joint & Conditional Probability.vtt

7. Bayes Theorem.mp4

7. Bayes Theorem.vtt

8. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4

8. Reading Files (Part 1) Absolute Paths and Relative Paths.vtt

9. Reading Files (Part 2) Stream Objects and Email Structure.mp4

9. Reading Files (Part 2) Stream Objects and Email Structure.vtt

10. Extracting the Text in the Email Body.mp4

10. Extracting the Text in the Email Body.vtt

11. [Python] - Generator Functions & the yield Keyword.mp4

11. [Python] - Generator Functions & the yield Keyword.vtt

12. Create a Pandas DataFrame of Email Bodies.mp4

12. Create a Pandas DataFrame of Email Bodies.vtt

13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4

13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.vtt

14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4

14. Cleaning Data (Part 2) Working with a DataFrame Index.vtt

15. Saving a JSON File with Pandas.mp4

15. Saving a JSON File with Pandas.vtt

16. Data Visualisation (Part 1) Pie Charts.mp4

16. Data Visualisation (Part 1) Pie Charts.vtt

17. Data Visualisation (Part 2) Donut Charts.mp4

17. Data Visualisation (Part 2) Donut Charts.vtt

18. Introduction to Natural Language Processing (NLP).mp4

18. Introduction to Natural Language Processing (NLP).vtt

19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4

19. Tokenizing, Removing Stop Words and the Python Set Data Structure.vtt

20. Word Stemming & Removing Punctuation.mp4

20. Word Stemming & Removing Punctuation.vtt

21. Removing HTML tags with BeautifulSoup.mp4

21. Removing HTML tags with BeautifulSoup.vtt

22. Creating a Function for Text Processing.mp4

23. A Note for the Next Lesson.html

24. Advanced Subsetting on DataFrames the apply() Function.mp4

24. Advanced Subsetting on DataFrames the apply() Function.vtt

25. [Python] - Logical Operators to Create Subsets and Indices.mp4

26. Word Clouds & How to install Additional Python Packages.mp4

26. Word Clouds & How to install Additional Python Packages.vtt

27. Creating your First Word Cloud.mp4

27. Creating your First Word Cloud.vtt

28. Styling the Word Cloud with a Mask.mp4

28. Styling the Word Cloud with a Mask.vtt

29. Solving the Hamlet Challenge.mp4

29. Solving the Hamlet Challenge.vtt

30. Styling Word Clouds with Custom Fonts.mp4

30. Styling Word Clouds with Custom Fonts.vtt

31. Create the Vocabulary for the Spam Classifier.vtt

32. Coding Challenge Check for Membership in a Collection.mp4

32. Coding Challenge Check for Membership in a Collection.vtt

33. Coding Challenge Find the Longest Email.mp4

33. Coding Challenge Find the Longest Email.vtt

34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4

34. Sparse Matrix (Part 1) Split the Training and Testing Data.vtt

35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4

35. Sparse Matrix (Part 2) Data Munging with Nested Loops.vtt

36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4

36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.vtt

37. Coding Challenge Solution Preparing the Test Data.mp4

37. Coding Challenge Solution Preparing the Test Data.vtt

38. Checkpoint Understanding the Data.mp4

38. Checkpoint Understanding the Data.vtt

39. Download the Complete Notebook Here.html

39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip.zip

/07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/

1. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4

1. Setting up the Notebook and Understanding Delimiters in a Dataset.vtt

1.1 SpamData.zip.zip

1.2 Course Resources.html

2. Create a Full Matrix.mp4

2. Create a Full Matrix.vtt

3. Count the Tokens to Train the Naive Bayes Model.mp4

3. Count the Tokens to Train the Naive Bayes Model.vtt

4. Sum the Tokens across the Spam and Ham Subsets.mp4

4. Sum the Tokens across the Spam and Ham Subsets.vtt

5. Calculate the Token Probabilities and Save the Trained Model.mp4

5. Calculate the Token Probabilities and Save the Trained Model.vtt

6. Coding Challenge Prepare the Test Data.mp4

6. Coding Challenge Prepare the Test Data.vtt

7. Download the Complete Notebook Here.html

7.1 07 Bayes Classifier - Training.ipynb.zip.zip

/08. Test and Evaluate a Naive Bayes Classifier Part 3/

1. Set up the Testing Notebook.mp4

1. Set up the Testing Notebook.vtt

1.1 Course Resources.html

1.2 SpamData.zip.zip

2. Joint Conditional Probability (Part 1) Dot Product.mp4

2. Joint Conditional Probability (Part 1) Dot Product.vtt

3. Joint Conditional Probablity (Part 2) Priors.mp4

3. Joint Conditional Probablity (Part 2) Priors.vtt

4. Making Predictions Comparing Joint Probabilities.mp4

4. Making Predictions Comparing Joint Probabilities.vtt

5. The Accuracy Metric.mp4

5. The Accuracy Metric.vtt

6. Visualising the Decision Boundary.mp4

6. Visualising the Decision Boundary.vtt

7. False Positive vs False Negatives.mp4

7. False Positive vs False Negatives.vtt

8. The Recall Metric.mp4

8. The Recall Metric.vtt

9. The Precision Metric.mp4

9. The Precision Metric.vtt

10. The F-score or F1 Metric.mp4

10. The F-score or F1 Metric.vtt

11. A Naive Bayes Implementation using SciKit Learn.mp4

11. A Naive Bayes Implementation using SciKit Learn.vtt

12. Download the Complete Notebook Here.html

12.1 08 Naive Bayes with scikit-learn.ipynb.zip.zip

12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip.zip

/09. Introduction to Neural Networks and How to Use Pre-Trained Models/

1. The Human Brain and the Inspiration for Artificial Neural Networks.mp4

1. The Human Brain and the Inspiration for Artificial Neural Networks.vtt

1.1 Course Resources.html

2. Layers, Feature Generation and Learning.mp4

2. Layers, Feature Generation and Learning.vtt

3. Costs and Disadvantages of Neural Networks.mp4

3. Costs and Disadvantages of Neural Networks.vtt

4. Preprocessing Image Data and How RGB Works.mp4

4. Preprocessing Image Data and How RGB Works.vtt

4.1 TF_Keras_Classification_Images.zip.zip

5. Importing Keras Models and the Tensorflow Graph.mp4

5. Importing Keras Models and the Tensorflow Graph.vtt

6. Making Predictions using InceptionResNet.mp4

6. Making Predictions using InceptionResNet.vtt

7. Coding Challenge Solution Using other Keras Models.mp4

7. Coding Challenge Solution Using other Keras Models.vtt

8. Download the Complete Notebook Here.html

8.1 09 Neural Nets Pretrained Image Classification.ipynb.zip.zip

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

1. Solving a Business Problem with Image Classification.mp4

1. Solving a Business Problem with Image Classification.vtt

1.1 Course Resources.html

2. Installing Tensorflow and Keras for Jupyter.mp4

2. Installing Tensorflow and Keras for Jupyter.vtt

3. Gathering the CIFAR 10 Dataset.mp4

3. Gathering the CIFAR 10 Dataset.vtt

4. Exploring the CIFAR Data.mp4

4. Exploring the CIFAR Data.vtt

5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4

5. Pre-processing Scaling Inputs and Creating a Validation Dataset.vtt

6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4

6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.vtt

7. Interacting with the Operating System and the Python Try-Catch Block.mp4

7. Interacting with the Operating System and the Python Try-Catch Block.vtt

8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4

8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.vtt

9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4

9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.vtt

10. Use the Model to Make Predictions.vtt

11. Model Evaluation and the Confusion Matrix.mp4

11. Model Evaluation and the Confusion Matrix.vtt

12. Model Evaluation and the Confusion Matrix.mp4

12. Model Evaluation and the Confusion Matrix.vtt

13. Download the Complete Notebook Here.html

13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip.zip

/11. Use Tensorflow to Classify Handwritten Digits/

1. What's coming up.mp4

1. What's coming up.vtt

1.1 Course Resources.html

2. Getting the Data and Loading it into Numpy Arrays.mp4

2. Getting the Data and Loading it into Numpy Arrays.vtt

2.1 MNIST.zip.zip

3. Data Exploration and Understanding the Structure of the Input Data.mp4

3. Data Exploration and Understanding the Structure of the Input Data.vtt

4. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.vtt

5. What is a Tensor.mp4

5. What is a Tensor.vtt

6. Creating Tensors and Setting up the Neural Network Architecture.mp4

6. Creating Tensors and Setting up the Neural Network Architecture.vtt

7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4

7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.vtt

8. TensorFlow Sessions and Batching Data.mp4

8. TensorFlow Sessions and Batching Data.vtt

9. Tensorboard Summaries and the Filewriter.mp4

9. Tensorboard Summaries and the Filewriter.vtt

10. Understanding the Tensorflow Graph Nodes and Edges.mp4

10. Understanding the Tensorflow Graph Nodes and Edges.vtt

11. Name Scoping and Image Visualisation in Tensorboard.mp4

11. Name Scoping and Image Visualisation in Tensorboard.vtt

12. Different Model Architectures Experimenting with Dropout.mp4

12. Different Model Architectures Experimenting with Dropout.vtt

13. Prediction and Model Evaluation.mp4

13. Prediction and Model Evaluation.vtt

14. Download the Complete Notebook Here.html

14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip.zip

/12. Next Steps/

1. Where next.html

2. What Modules Do You Want to See.html

3. Stay in Touch!.html

Total files 377