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[UdemyCourseDownloader] Complete Data Science & Machine Learning Bootcamp – Python 3
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04. Introduction to Optimisation and the Gradient Descent Algorithm (Size: 14.16 GB) (Files: 377)
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
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
Udemy Course downloader.txt
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
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