[UdemyCourseDownloader] Complete Data Science & Machine Learning Bootcamp – Python 3

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[UdemyCourseDownloader] Complete Data Science & Machine Learning Bootcamp – Python 3

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收录时间：2021-04-07
最近下载：2025-08-04

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文件列表

  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 305.5 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4 301.4 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 264.1 MB
  • 05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 256.0 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 248.1 MB
  • 03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 243.4 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 229.7 MB
  • 05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 224.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 224.1 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4 215.3 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 204.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 202.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 200.8 MB
  • 03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 179.8 MB
  • 05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 176.8 MB
  • 03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 164.4 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 162.9 MB
  • 03. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4 160.7 MB
  • 05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 160.4 MB
  • 05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 160.1 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4 158.2 MB
  • 05. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4 157.4 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4 155.4 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4 153.8 MB
  • 05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 150.8 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4 148.7 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 147.7 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 143.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 141.6 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4 141.1 MB
  • 05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 140.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/7. Interacting with the Operating System and the Python Try-Catch Block.mp4 139.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 139.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 139.3 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4 138.7 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.mp4 137.8 MB
  • 05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 137.7 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 137.4 MB
  • 05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 134.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.mp4 134.5 MB
  • 03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 134.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4 133.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 133.0 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 130.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 130.5 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 127.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 123.5 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 121.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/2. Gathering Email Data and Working with Archives & Text Editors.mp4 117.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 116.9 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4 116.1 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.mp4 115.7 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.mp4 110.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/9. Reading Files (Part 2) Stream Objects and Email Structure.mp4 109.4 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 108.6 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.mp4 108.6 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 105.3 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.mp4 105.2 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp4 103.2 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.mp4 101.7 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp4 101.1 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.mp4 100.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp4 100.5 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.mp4 98.2 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 97.7 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.mp4 96.5 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp4 95.1 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.mp4 94.9 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.mp4 94.5 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 91.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 91.4 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp4 91.1 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp4 91.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/25. [Python] - Logical Operators to Create Subsets and Indices.mp4 90.6 MB
  • 05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 89.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.mp4 87.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.mp4 87.2 MB
  • 03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp4 86.7 MB
  • 03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp4 85.5 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp4 85.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 84.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/26. Word Clouds & How to install Additional Python Packages.mp4 83.4 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 78.8 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp4 78.4 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 76.7 MB
  • 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 76.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp4 74.9 MB
  • 03. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.mp4 74.8 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp4 74.8 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.mp4 69.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.mp4 69.4 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.mp4 68.6 MB
  • 05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 68.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp4 67.7 MB
  • 05. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 67.7 MB
  • 05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp4 67.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp4 67.3 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.mp4 67.1 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.mp4 66.3 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp4 65.8 MB
  • 05. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.mp4 65.2 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4 64.8 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp4 64.8 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/8. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 63.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 60.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp4 59.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp4 59.1 MB
  • 05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp4 58.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp4 57.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp4 56.5 MB
  • 03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp4 56.1 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/5. Calculate the Token Probabilities and Save the Trained Model.mp4 56.1 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.mp4 55.9 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.mp4 55.4 MB
  • 03. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.mp4 55.0 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.mp4 54.9 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/1. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 54.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp4 53.3 MB
  • 03. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.mp4 52.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp4 51.2 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.mp4 51.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.mp4 49.7 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/4. Sum the Tokens across the Spam and Ham Subsets.mp4 49.0 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.mp4 47.6 MB
  • 01. Introduction to the Course/1. What is Machine Learning.mp4 47.5 MB
  • 01. Introduction to the Course/2. What is Data Science.mp4 44.9 MB
  • 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 44.3 MB
  • 03. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.mp4 44.3 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.mp4 44.1 MB
  • 03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 43.6 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.mp4 42.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.mp4 41.9 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.mp4 37.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/4. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 35.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.mp4 34.7 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.mp4 34.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp4 34.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/32. Coding Challenge Check for Membership in a Collection.mp4 33.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.mp4 32.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.mp4 32.0 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.mp4 31.8 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.mp4 31.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.mp4 30.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/3. How to Add the Lesson Resources to the Project.mp4 30.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.mp4 29.9 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.mp4 29.5 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.mp4 27.7 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp4 25.9 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/1.2 SpamData.zip.zip 23.9 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.mp4 23.9 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/1.1 SpamData.zip.zip 23.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/2.1 SpamData.zip.zip 22.3 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.mp4 22.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp4 16.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/2.1 MNIST.zip.zip 15.5 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.mp4 7.4 MB
  • 05. Predict House Prices with Multivariable Linear Regression/33.1 04 Multivariable Regression.ipynb.zip.zip 3.7 MB
  • 03. Python Programming for Data Science and Machine Learning/4.1 12 Rules to Learn to Code.pdf.pdf 2.4 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/24.1 03 Gradient Descent.ipynb.zip.zip 1.2 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip.zip 1.0 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/8.1 09 Neural Nets Pretrained Image Classification.ipynb.zip.zip 585.6 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/4.1 TF_Keras_Classification_Images.zip.zip 513.1 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/2.1 cost_revenue_dirty.csv.csv 383.7 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip.zip 248.9 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip.zip 123.0 kB
  • 01. Introduction to the Course/3.1 ML Data Science Syllabus.pdf.pdf 106.5 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/3.1 cost_revenue_clean.csv.csv 93.0 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/6.1 01 Linear Regression (complete).ipynb.zip.zip 77.1 kB
  • 03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp4.jpg 60.4 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/4.1 01 Linear Regression (checkpoint).ipynb.zip.zip 38.5 kB
  • 03. Python Programming for Data Science and Machine Learning/21.1 02 Python Intro.ipynb.zip.zip 37.3 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).vtt 37.3 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.vtt 36.7 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.vtt 36.0 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.vtt 32.1 kB
  • 03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.vtt 31.1 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.vtt 29.9 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.vtt 29.9 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.vtt 29.6 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).vtt 29.2 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.vtt 27.0 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.vtt 26.9 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.vtt 26.0 kB
  • 03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.vtt 25.8 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.vtt 25.2 kB
  • 05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.vtt 25.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.vtt 25.0 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.vtt 24.8 kB
  • 03. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.vtt 24.6 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.vtt 23.5 kB
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  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.vtt 23.4 kB
  • 03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.vtt 23.0 kB
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  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/35. Sparse Matrix (Part 2) Data Munging with Nested Loops.vtt 20.3 kB
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  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.vtt 19.8 kB
  • 05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.vtt 19.5 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.vtt 19.3 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.vtt 19.3 kB
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  • 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.vtt 19.0 kB
  • 05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).vtt 18.9 kB
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  • 03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.vtt 18.0 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.vtt 17.8 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.vtt 17.8 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.vtt 17.2 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.vtt 17.2 kB
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  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/6. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.vtt 16.7 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/3. Count the Tokens to Train the Naive Bayes Model.vtt 16.4 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/19. Tokenizing, Removing Stop Words and the Python Set Data Structure.vtt 16.4 kB
  • 05. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.vtt 16.2 kB
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  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.vtt 15.7 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.vtt 15.7 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).vtt 15.7 kB
  • 05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.vtt 15.6 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.vtt 15.6 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.vtt 15.1 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.vtt 14.6 kB
  • 03. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.vtt 14.5 kB
  • 03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.vtt 14.5 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.vtt 14.5 kB
  • 03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.vtt 14.4 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.vtt 14.2 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/34. Sparse Matrix (Part 1) Split the Training and Testing Data.vtt 13.8 kB
  • 05. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.vtt 13.6 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/12.1 08 Naive Bayes with scikit-learn.ipynb.zip.zip 13.6 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.vtt 13.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.vtt 13.0 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.vtt 12.9 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.vtt 12.7 kB
  • 05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.vtt 12.7 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/9. Reading Files (Part 2) Stream Objects and Email Structure.vtt 12.7 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/8. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.vtt 12.7 kB
  • 05. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.vtt 12.3 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.vtt 12.3 kB
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  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.vtt 11.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.vtt 11.9 kB
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  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).vtt 11.1 kB
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  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.vtt 9.4 kB
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