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[UdemyCourseDownloader] Complete Data Science & Machine Learning Bootcamp – Python 3
magnet:?xt=urn:btih:d432c60e9d1dc749517171c7d3d3392d0e7e754f&dn=[UdemyCourseDownloader] Complete Data Science & Machine Learning Bootcamp – Python 3
磁力链接详情
Hash值:
d432c60e9d1dc749517171c7d3d3392d0e7e754f
点击数:
158
文件大小:
14.07 GB
文件数量:
152
创建日期:
2019-8-3 04:05
最后访问:
2024-7-12 22:10
访问标签:
UdemyCourseDownloader
Complete
Data
Science
&
Machine
Learning
Bootcamp
–
Python
3
文件列表详情
04. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 291.34 MB
01. Introduction to the Course/1. What is Machine Learning.mp4 45.29 MB
01. Introduction to the Course/2. What is Data Science.mp4 42.86 MB
02. Predict Movie Box Office Revenue with Linear Regression/1. Introduction to Linear Regression & Specifying the Problem.mp4 30.33 MB
02. Predict Movie Box Office Revenue with Linear Regression/2. Gather & Clean the Data.mp4 97.02 MB
02. Predict Movie Box Office Revenue with Linear Regression/3. Explore & Visualise the Data with Python.mp4 148.16 MB
02. Predict Movie Box Office Revenue with Linear Regression/4. The Intuition behind the Linear Regression Model.mp4 29.63 MB
02. Predict Movie Box Office Revenue with Linear Regression/5. Analyse and Evaluate the Results.mp4 105.17 MB
03. Python Programming for Data Science and Machine Learning/1. Windows Users - Install Anaconda.mp4 49.6 MB
03. Python Programming for Data Science and Machine Learning/2. Mac Users - Install Anaconda.mp4 52.41 MB
03. Python Programming for Data Science and Machine Learning/3. Does LSD Make You Better at Maths.mp4 42.26 MB
03. Python Programming for Data Science and Machine Learning/5. [Python] - Variables and Types.mp4 71.37 MB
03. Python Programming for Data Science and Machine Learning/7. [Python] - Lists and Arrays.mp4 53.47 MB
03. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4 153.21 MB
03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 232.08 MB
03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 41.61 MB
03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 128.2 MB
03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp4 82.64 MB
03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 156.77 MB
03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 171.46 MB
03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp4 81.54 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/1. What's Coming Up.mp4 20.93 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/2. How a Machine Learns.mp4 22.78 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/3. Introduction to Cost Functions.mp4 66.2 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/4. LaTeX Markdown and Generating Data with Numpy.mp4 90.52 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/5. Understanding the Power Rule & Creating Charts with Subplots.mp4 90.17 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4 287.45 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 219.02 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 236.6 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 193.48 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 132.82 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp4 86.83 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 131.08 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 140.82 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp4 71.33 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp4 64.57 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp4 86.91 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp4 81.12 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 73.16 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 124.88 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp4 74.82 MB
05. Predict House Prices with Multivariable Linear Regression/1. Defining the Problem.mp4 39.92 MB
05. Predict House Prices with Multivariable Linear Regression/3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 87.14 MB
05. Predict House Prices with Multivariable Linear Regression/4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 135.03 MB
05. Predict House Prices with Multivariable Linear Regression/5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 64.56 MB
05. Predict House Prices with Multivariable Linear Regression/6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 57.32 MB
05. Predict House Prices with Multivariable Linear Regression/8. Understanding Descriptive Statistics the Mean vs the Median.mp4 62.19 MB
05. Predict House Prices with Multivariable Linear Regression/9. Introduction to Correlation Understanding Strength & Direction.mp4 33.09 MB
05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 111.44 MB
05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 168.65 MB
05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 128.53 MB
05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 214.4 MB
05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp4 48.81 MB
05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp4 64.35 MB
05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp4 55.57 MB
05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp4 32.4 MB
05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp4 15.99 MB
05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 126.87 MB
05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 65.4 MB
05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 143.83 MB
05. Predict House Prices with Multivariable Linear Regression/23. Model Simiplication & Baysian Information Criterion.mp4 150.15 MB
05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp4 64.18 MB
05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 124.42 MB
05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 153.02 MB
05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 152.68 MB
05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 84.85 MB
05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 131.31 MB
05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 134.39 MB
05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 244.16 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 42.26 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 112.05 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 28.91 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 33.39 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/5. Basic Probability.mp4 28.56 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/6. Joint & Conditional Probability.mp4 141.82 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/7. Bayes Theorem.mp4 83.12 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 60.9 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 104.33 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 47.43 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 133.16 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 48.67 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 121.93 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 61.83 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 56.35 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 90.69 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 61.79 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 50.81 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 117.76 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp4 71.44 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp4 95.82 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 53.91 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 83.4 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 86.41 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 79.49 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp4 98.44 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 131.37 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp4 57.11 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 127.3 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 32.35 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 54.47 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 87.63 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 137.23 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 80.5 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 28.93 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp4 96.37 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 72.5 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/2. Create a Full Matrix.mp4 132.24 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 96.19 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 46.71 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 53.46 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/6. Coding Challenge Prepare the Test Data.mp4 35.6 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/1. Set up the Testing Notebook.mp4 26.45 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/2. Joint Conditional Probability (Part 1) Dot Product.mp4 66.41 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/3. Joint Conditional Probablity (Part 2) Priors.mp4 63.98 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/4. Making Predictions Comparing Joint Probabilities.mp4 52.34 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/5. The Accuracy Metric.mp4 40.54 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4 205.31 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/7. False Positive vs False Negatives.mp4 63.25 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/8. The Recall Metric.mp4 28.16 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/9. The Precision Metric.mp4 53.34 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp4 24.72 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 195.1 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 51.81 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/2. Layers, Feature Generation and Learning.mp4 146.7 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/3. Costs and Disadvantages of Neural Networks.mp4 91.99 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/4. Preprocessing Image Data and How RGB Works.mp4 93.61 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/5. Importing Keras Models and the Tensorflow Graph.mp4 65.47 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/6. Making Predictions using InceptionResNet.mp4 134.58 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/7. Coding Challenge Solution Using other Keras Models.mp4 103.54 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/1. Solving a Business Problem with Image Classification.mp4 30.52 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/2. Installing Tensorflow and Keras for Jupyter.mp4 42.1 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/3. Gathering the CIFAR 10 Dataset.mp4 31.36 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/4. Exploring the CIFAR Data.mp4 110.31 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/5. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 93.16 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 103.61 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 133.41 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 100.43 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/9. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 191.53 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp4 62.76 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 251.84 MB
11. Use Tensorflow to Classify Handwritten Digits/1. What's coming up.mp4 7.1 MB
11. Use Tensorflow to Classify Handwritten Digits/2. Getting the Data and Loading it into Numpy Arrays.mp4 52.82 MB
11. Use Tensorflow to Classify Handwritten Digits/3. Data Exploration and Understanding the Structure of the Input Data.mp4 32.41 MB
11. Use Tensorflow to Classify Handwritten Digits/5. What is a Tensor.mp4 45.39 MB
11. Use Tensorflow to Classify Handwritten Digits/6. Creating Tensors and Setting up the Neural Network Architecture.mp4 150.86 MB
11. Use Tensorflow to Classify Handwritten Digits/7. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 75.12 MB
11. Use Tensorflow to Classify Handwritten Digits/8. TensorFlow Sessions and Batching Data.mp4 100.33 MB
11. Use Tensorflow to Classify Handwritten Digits/9. Tensorboard Summaries and the Filewriter.mp4 128.29 MB
11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 115.74 MB
11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 155.37 MB
11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 213.68 MB
11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4 110.71 MB
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