[Tutorialsplanet.NET] Udemy - Practical Machine Learning by Example in Python

Creation Time	March 27, 2024, 9:08 p.m.
Last Access Time	Nov. 8, 2024, 3:14 p.m.
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Keywords	Tutorialsplanet Machine Python - Udemy Practical Learning in NET by Example
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4. Foundations NumPy/6. Linear Regression Example.mp4 64.6 MB
2. Python Quick Start/12. Classes.mp4 62.5 MB
7. Foundations Pandas/2. Loading and inspecting data example.mp4 59.0 MB
3. Example Logistic Regression/3. Data analysis.mp4 59.0 MB
9. Example Sentiment Analysis/11. Transfer Learning Example.mp4 57.8 MB
6. Example Image recognition/14. Hyperparameter tuning example.mp4 52.5 MB
2. Python Quick Start/3. String formatting.mp4 52.4 MB
4. Foundations NumPy/5. Introduction to Linear Regression.mp4 52.3 MB
6. Example Image recognition/8. Model training.mp4 50.2 MB
10. Example Fraud detection/9. Making predictions.mp4 49.4 MB
3. Example Logistic Regression/8. Gradient descent.mp4 46.3 MB
9. Example Sentiment Analysis/5. Data Preparation.mp4 44.4 MB
8. Example Recommendations/8. Model definition.mp4 42.2 MB
10. Example Fraud detection/2. Data analysis.mp4 41.8 MB
3. Example Logistic Regression/12. Making predictions.mp4 40.3 MB
7. Foundations Pandas/5. Sorting and transforming data example.mp4 39.8 MB
9. Example Sentiment Analysis/12. Fine Tuning and Prediction.mp4 39.5 MB
6. Example Image recognition/2. Data analysis.mp4 38.3 MB
3. Example Logistic Regression/6. The forward function.mp4 37.8 MB
8. Example Recommendations/5. Data preparation.mp4 36.2 MB
1. Course Structure and Development Environment/8. Sharing Colab Notebooks.mp4 35.7 MB
7. Foundations Pandas/7. Visualizing data.mp4 35.3 MB
2. Python Quick Start/2. Basic Syntax.mp4 35.2 MB
7. Foundations Pandas/3. Indexing and selecting data example.mp4 35.0 MB
9. Example Sentiment Analysis/8. Model Training.mp4 34.8 MB
2. Python Quick Start/11. Defining functions.mp4 34.7 MB
7. Foundations Pandas/6. Aggregations example.mp4 33.9 MB
2. Python Quick Start/13. File IO and Modules.mp4 33.7 MB
4. Foundations NumPy/10. Visualizing data.mp4 33.7 MB
1. Course Structure and Development Environment/2. Course Quick Tips.mp4 32.1 MB
2. Python Quick Start/10. Dictionaries.mp4 32.1 MB
4. Foundations NumPy/11. Images.mp4 31.8 MB
6. Example Image recognition/7. Model definition.mp4 31.7 MB
1. Course Structure and Development Environment/1. Course Structure and Development Environment.mp4 31.4 MB
9. Example Sentiment Analysis/2. Data Analysis.mp4 30.5 MB
3. Example Logistic Regression/11. Model training.mp4 29.9 MB
8. Example Recommendations/12. Making predictions.mp4 29.5 MB
8. Example Recommendations/9. Model training.mp4 29.3 MB
8. Example Recommendations/2. Data analysis.mp4 29.1 MB
9. Example Sentiment Analysis/7. Model Definition.mp4 29.0 MB
5. Foundations Tensorflow/2. Model example.mp4 28.9 MB
10. Example Fraud detection/4. Unsupervised learning.mp4 28.8 MB
10. Example Fraud detection/11. Common questions.mp4 28.8 MB
3. Example Logistic Regression/1. The problem.mp4 28.0 MB
6. Example Image recognition/16. Common questions.mp4 27.8 MB
9. Example Sentiment Analysis/4. Supervised Learning.mp4 27.6 MB
3. Example Logistic Regression/17. Improving the model.mp4 27.1 MB
1. Course Structure and Development Environment/4. Jupyter notebook Text Cells.mp4 27.1 MB
6. Example Image recognition/5. Data preparation.mp4 27.0 MB
8. Example Recommendations/4. Model selection.mp4 27.0 MB
1. Course Structure and Development Environment/9. Artificial Intelligence, Machine Learning, and Deep Learning.mp4 25.2 MB
10. Example Fraud detection/7. Model training.mp4 25.1 MB
4. Foundations NumPy/2. Creating data with NumPy.mp4 25.0 MB
3. Example Logistic Regression/10. Backpropagation.mp4 23.9 MB
9. Example Sentiment Analysis/10. Transfer Learning with BERT.mp4 23.7 MB
5. Foundations Tensorflow/5. Training example.mp4 23.5 MB
2. Python Quick Start/7. Flow control.mp4 23.5 MB
5. Foundations Tensorflow/12. The Three Body Problem.mp4 23.5 MB
1. Course Structure and Development Environment/6. Jupyter notebook Math Markup and Magic Commands.mp4 23.3 MB
6. Example Image recognition/6. CNN Model Layers.mp4 23.0 MB
6. Example Image recognition/4. Model selection.mp4 22.8 MB
6. Example Image recognition/13. Hyperparameter tuning.mp4 22.4 MB
2. Python Quick Start/8. Lists.mp4 22.4 MB
10. Example Fraud detection/1. The problem.mp4 22.3 MB
3. Example Logistic Regression/5. The model.mp4 22.2 MB
10. Example Fraud detection/6. Model definition.mp4 21.4 MB
3. Example Logistic Regression/7. Loss and cost functions.mp4 19.8 MB
2. Python Quick Start/6. Type conversion.mp4 19.6 MB
8. Example Recommendations/15. Common questions.mp4 19.6 MB
6. Example Image recognition/1. The problem.mp4 19.4 MB
5. Foundations Tensorflow/4. Activation functions.mp4 19.3 MB
5. Foundations Tensorflow/7. Loss functions.mp4 19.2 MB
3. Example Logistic Regression/15. Test vs. train accuracy.mp4 19.0 MB
8. Example Recommendations/13. Error analysis.mp4 18.6 MB
3. Example Logistic Regression/16. Speeding up training.mp4 18.1 MB
5. Foundations Tensorflow/1. About this section.mp4 18.1 MB
8. Example Recommendations/7. Embedding layers.mp4 17.2 MB
8. Example Recommendations/1. The problem.mp4 17.1 MB
4. Foundations NumPy/9. Statistics and linear algebra.mp4 17.0 MB
1. Course Structure and Development Environment/3. Introduction to Jupyter Notebook.mp4 16.9 MB
10. Example Fraud detection/5. Data preparation.mp4 16.9 MB
5. Foundations Tensorflow/8. Optimizers.mp4 16.4 MB
2. Python Quick Start/4. Literal string interpolation.mp4 15.8 MB
5. Foundations Tensorflow/11. Saving and restoring models.mp4 15.6 MB
6. Example Image recognition/11. Error analysis.mp4 15.4 MB
1. Course Structure and Development Environment/5. Jupyter notebook Code Cells.mp4 15.0 MB
4. Foundations NumPy/3. Basic operations.mp4 14.7 MB
3. Example Logistic Regression/2. Machine Learning Development Process.mp4 14.0 MB
4. Foundations NumPy/8. More Complex Models.mp4 13.5 MB
5. Foundations Tensorflow/3. Model layers.mp4 13.2 MB
9. Example Sentiment Analysis/1. The Problem.mp4 13.1 MB
6. Example Image recognition/10. Making predictions.mp4 12.3 MB
11. Next steps/1. Next steps.mp4 11.6 MB
4. Foundations NumPy/13. Reshaping data.mp4 11.1 MB
7. Foundations Pandas/1. What is Pandas and why is it useful.mp4 8.9 MB
4. Foundations NumPy/1. What is NumPy and why it is needed.mp4 8.1 MB
2. Python Quick Start/1. About this section.mp4 7.4 MB
2. Python Quick Start/15. Prompting for passwords.mp4 7.1 MB
5. Foundations Tensorflow/10. Prediction example.mp4 6.8 MB
8. Example Recommendations/11. Predictions.mp4 5.7 MB
11. Next steps/2. Thank you.mp4 3.1 MB
2. Python Quick Start/12. Classes.srt 16.4 KB
4. Foundations NumPy/6. Linear Regression Example.srt 15.9 KB
4. Foundations NumPy/5. Introduction to Linear Regression.srt 14.5 KB
3. Example Logistic Regression/3. Data analysis.srt 12.5 KB
10. Example Fraud detection/9. Making predictions.srt 12.0 KB
3. Example Logistic Regression/12. Making predictions.srt 11.7 KB
3. Example Logistic Regression/8. Gradient descent.srt 11.5 KB
8. Example Recommendations/8. Model definition.srt 11.5 KB
2. Python Quick Start/11. Defining functions.srt 11.2 KB
6. Example Image recognition/2. Data analysis.srt 10.6 KB
9. Example Sentiment Analysis/11. Transfer Learning Example.srt 10.2 KB
2. Python Quick Start/13. File IO and Modules.srt 10.1 KB
9. Example Sentiment Analysis/5. Data Preparation.srt 9.6 KB
2. Python Quick Start/3. String formatting.srt 9.4 KB
9. Example Sentiment Analysis/12. Fine Tuning and Prediction.srt 9.3 KB
9. Example Sentiment Analysis/8. Model Training.srt 9.2 KB
7. Foundations Pandas/2. Loading and inspecting data example.srt 9.1 KB
3. Example Logistic Regression/11. Model training.srt 8.9 KB
1. Course Structure and Development Environment/2. Course Quick Tips.srt 8.6 KB
2. Python Quick Start/2. Basic Syntax.srt 8.5 KB
8. Example Recommendations/5. Data preparation.srt 8.5 KB
6. Example Image recognition/14. Hyperparameter tuning example.srt 8.5 KB
1. Course Structure and Development Environment/8. Sharing Colab Notebooks.srt 8.3 KB
10. Example Fraud detection/2. Data analysis.srt 8.0 KB
4. Foundations NumPy/11. Images.srt 7.7 KB
3. Example Logistic Regression/6. The forward function.srt 7.4 KB
7. Foundations Pandas/5. Sorting and transforming data example.srt 7.3 KB
2. Python Quick Start/4. Literal string interpolation.srt 7.2 KB
2. Python Quick Start/7. Flow control.srt 7.0 KB
6. Example Image recognition/8. Model training.srt 7.0 KB
2. Python Quick Start/8. Lists.srt 7.0 KB
7. Foundations Pandas/7. Visualizing data.srt 7.0 KB
9. Example Sentiment Analysis/7. Model Definition.srt 7.0 KB
2. Python Quick Start/10. Dictionaries.srt 6.9 KB
8. Example Recommendations/12. Making predictions.srt 6.9 KB
3. Example Logistic Regression/17. Improving the model.srt 6.9 KB
7. Foundations Pandas/3. Indexing and selecting data example.srt 6.9 KB
5. Foundations Tensorflow/2. Model example.srt 6.8 KB
9. Example Sentiment Analysis/10. Transfer Learning with BERT.srt 6.6 KB
8. Example Recommendations/4. Model selection.srt 6.5 KB
3. Example Logistic Regression/10. Backpropagation.srt 6.5 KB
8. Example Recommendations/9. Model training.srt 6.4 KB
10. Example Fraud detection/7. Model training.srt 6.1 KB
10. Example Fraud detection/11. Common questions.srt 5.9 KB
3. Example Logistic Regression/15. Test vs. train accuracy.srt 5.9 KB
4. Foundations NumPy/10. Visualizing data.srt 5.8 KB
6. Example Image recognition/6. CNN Model Layers.srt 5.7 KB
4. Foundations NumPy/9. Statistics and linear algebra.srt 5.7 KB
10. Example Fraud detection/4. Unsupervised learning.srt 5.7 KB
6. Example Image recognition/5. Data preparation.srt 5.7 KB
6. Example Image recognition/11. Error analysis.srt 5.7 KB
4. Foundations NumPy/2. Creating data with NumPy.srt 5.6 KB
1. Course Structure and Development Environment/1. Course Structure and Development Environment.srt 5.6 KB
1. Course Structure and Development Environment/9. Artificial Intelligence, Machine Learning, and Deep Learning.srt 5.5 KB
3. Example Logistic Regression/5. The model.srt 5.5 KB
8. Example Recommendations/13. Error analysis.srt 5.5 KB
2. Python Quick Start/6. Type conversion.srt 5.4 KB
9. Example Sentiment Analysis/2. Data Analysis.srt 5.4 KB
10. Example Fraud detection/6. Model definition.srt 5.4 KB
8. Example Recommendations/2. Data analysis.srt 5.4 KB
9. Example Sentiment Analysis/4. Supervised Learning.srt 5.3 KB
6. Example Image recognition/4. Model selection.srt 5.3 KB
1. Course Structure and Development Environment/6. Jupyter notebook Math Markup and Magic Commands.srt 5.3 KB
6. Example Image recognition/7. Model definition.srt 5.2 KB
5. Foundations Tensorflow/5. Training example.srt 5.2 KB
6. Example Image recognition/13. Hyperparameter tuning.srt 5.1 KB
4. Foundations NumPy/3. Basic operations.srt 5.0 KB
6. Example Image recognition/16. Common questions.srt 5.0 KB
3. Example Logistic Regression/1. The problem.srt 5.0 KB
5. Foundations Tensorflow/4. Activation functions.srt 4.9 KB
3. Example Logistic Regression/7. Loss and cost functions.srt 4.8 KB
5. Foundations Tensorflow/7. Loss functions.srt 4.4 KB
4. Foundations NumPy/8. More Complex Models.srt 4.3 KB
5. Foundations Tensorflow/11. Saving and restoring models.srt 4.2 KB
8. Example Recommendations/7. Embedding layers.srt 4.1 KB
7. Foundations Pandas/6. Aggregations example.srt 4.0 KB
3. Example Logistic Regression/16. Speeding up training.srt 3.9 KB
1. Course Structure and Development Environment/3. Introduction to Jupyter Notebook.srt 3.9 KB
1. Course Structure and Development Environment/5. Jupyter notebook Code Cells.srt 3.9 KB
10. Example Fraud detection/5. Data preparation.srt 3.8 KB
6. Example Image recognition/1. The problem.srt 3.8 KB
3. Example Logistic Regression/2. Machine Learning Development Process.srt 3.8 KB
10. Example Fraud detection/1. The problem.srt 3.8 KB
4. Foundations NumPy/13. Reshaping data.srt 3.7 KB
5. Foundations Tensorflow/12. The Three Body Problem.srt 3.6 KB
8. Example Recommendations/15. Common questions.srt 3.5 KB
8. Example Recommendations/1. The problem.srt 3.3 KB
1. Course Structure and Development Environment/4. Jupyter notebook Text Cells.srt 3.2 KB
6. Example Image recognition/10. Making predictions.srt 3.0 KB
5. Foundations Tensorflow/1. About this section.srt 3.0 KB
9. Example Sentiment Analysis/1. The Problem.srt 2.9 KB
5. Foundations Tensorflow/8. Optimizers.srt 2.8 KB
5. Foundations Tensorflow/3. Model layers.srt 2.8 KB
6. Example Image recognition/19. What you learned in this section.html 2.6 KB
11. Next steps/1. Next steps.srt 2.5 KB
5. Foundations Tensorflow/10. Prediction example.srt 2.4 KB
2. Python Quick Start/15. Prompting for passwords.srt 2.3 KB
7. Foundations Pandas/1. What is Pandas and why is it useful.srt 2.2 KB
5. Foundations Tensorflow/13. What you learned in this section.html 1.7 KB
3. Example Logistic Regression/18. What you learned in this section.html 1.5 KB
4. Foundations NumPy/1. What is NumPy and why it is needed.srt 1.5 KB
10. Example Fraud detection/13. What you learned in this section.html 1.4 KB
2. Python Quick Start/1. About this section.srt 1.3 KB
8. Example Recommendations/11. Predictions.srt 1.2 KB
8. Example Recommendations/16. What you learned in this section.html 1.1 KB
4. Foundations NumPy/14. What you learned in this section.html 823 bytes
1. Course Structure and Development Environment/10. What you learned in this section.html 674 bytes
2. Python Quick Start/16. What you learned in this section.html 584 bytes
7. Foundations Pandas/9. What you learned in this section.html 555 bytes
11. Next steps/2. Thank you.srt 538 bytes
9. Example Sentiment Analysis/14. What you learned in this section.html 425 bytes
5. Foundations Tensorflow/12.2 New Neural Network Could Solve The Three-Body Problem 100 Million Times Faster.html 174 bytes
1. Course Structure and Development Environment/8.1 Saving notebooks to Github or Drive.html 170 bytes
3. Example Logistic Regression/3.1 Github repo.html 159 bytes
7. Foundations Pandas/5.1 Sorting data.html 153 bytes
9. Example Sentiment Analysis/2.2 Github repo.html 149 bytes
1. Course Structure and Development Environment/7. Introduction to Notebooks.html 148 bytes
10. Example Fraud detection/10. Prediction and error analysis.html 148 bytes
10. Example Fraud detection/12. Improving the model.html 148 bytes
10. Example Fraud detection/3. Analyze credit card data set.html 148 bytes
10. Example Fraud detection/8. Training the model.html 148 bytes
2. Python Quick Start/14. Plot several math functions.html 148 bytes
2. Python Quick Start/5. Experiment with string formatting.html 148 bytes
2. Python Quick Start/9. Dot product.html 148 bytes
3. Example Logistic Regression/13. Training a model.html 148 bytes
3. Example Logistic Regression/14. Optional Wine Classification.html 148 bytes
3. Example Logistic Regression/4. Analyze Iris flower data set.html 148 bytes
3. Example Logistic Regression/9. Experiment with gradient descent.html 148 bytes
4. Foundations NumPy/12. Visualizing data.html 148 bytes
4. Foundations NumPy/4. Experiment with NumPy.html 148 bytes
4. Foundations NumPy/7. Experiment with Linear Regression.html 148 bytes
5. Foundations Tensorflow/6. Train a basic model.html 148 bytes
5. Foundations Tensorflow/9. Experiment with optimizers.html 148 bytes
6. Example Image recognition/12. Prediction and error analysis.html 148 bytes
6. Example Image recognition/15. Model improvement.html 148 bytes
6. Example Image recognition/17. Optional Real images.html 148 bytes
6. Example Image recognition/18. Optional Other image types.html 148 bytes
6. Example Image recognition/3. Analyze MNIST data set.html 148 bytes
6. Example Image recognition/9. Training a model.html 148 bytes
7. Foundations Pandas/4. Experiment with Pandas.html 148 bytes
7. Foundations Pandas/8. Visualizing data with Pandas.html 148 bytes
8. Example Recommendations/10. Training the model.html 148 bytes
8. Example Recommendations/14. Making recommendations and error analysis.html 148 bytes
8. Example Recommendations/3. Analyze MovieLens data set.html 148 bytes
8. Example Recommendations/6. Prepare data.html 148 bytes
9. Example Sentiment Analysis/13. Transfer Learning with BERT.html 148 bytes
9. Example Sentiment Analysis/3. Analyze Sentiment Data Set.html 148 bytes
9. Example Sentiment Analysis/6. Prepare Data.html 148 bytes
9. Example Sentiment Analysis/9. Training the Model.html 148 bytes
1. Course Structure and Development Environment/3.2 IBM Watson Studio Notebooks.html 147 bytes
2. Python Quick Start/3.2 printf style formatting.html 139 bytes
5. Foundations Tensorflow/4.2 Tensorflow activations.html 138 bytes
5. Foundations Tensorflow/2.1 Sequential models.html 137 bytes
5. Foundations Tensorflow/8.2 Tensorflow optimizers.html 137 bytes
7. Foundations Pandas/7.1 Pandas visualization user guide.html 135 bytes
5. Foundations Tensorflow/7.1 Loss functions.html 133 bytes
5. Foundations Tensorflow/10.1 Model API.html 132 bytes
5. Foundations Tensorflow/11.1 Model API.html 132 bytes
7. Foundations Pandas/3.1 User Guide Indexing and Selecting Data.html 130 bytes
9. Example Sentiment Analysis/2.1 Data set.html 129 bytes
7. Foundations Pandas/6.1 Pandas group by API.html 128 bytes
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8. Example Recommendations/15.2 BellKor solution.html 126 bytes
1. Course Structure and Development Environment/4.1 Markdown cheat sheet.html 125 bytes
7. Foundations Pandas/2.2 Pandas IO.html 124 bytes
4. Foundations NumPy/9.2 Statistics functions.html 122 bytes
2. Python Quick Start/3.1 Format string syntax.html 120 bytes
8. Example Recommendations/15.1 Other solutions.html 120 bytes
10. Example Fraud detection/11.1 Building Autoencoders in Keras.html 118 bytes
4. Foundations NumPy/9.1 Linear algebra.html 118 bytes
5. Foundations Tensorflow/8.1 Stochastic gradient descent and related optimizers.html 118 bytes
9. Example Sentiment Analysis/1.2 Natural Language Processing (NLP).html 118 bytes
9. Example Sentiment Analysis/4.1 Natural Language Processing (NLP).html 118 bytes
1. Course Structure and Development Environment/3.6 AWS Sagemaker Notebook Instances.html 117 bytes
6. Example Image recognition/7.2 Sequential model guide.html 117 bytes
8. Example Recommendations/8.1 Keras functional API.html 115 bytes
8. Example Recommendations/2.1 Collaborative filtering article.html 114 bytes
4. Foundations NumPy/11.3 Image manipulation with NumPy.html 113 bytes
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1. Course Structure and Development Environment/1.1 Github repo.html 110 bytes
5. Foundations Tensorflow/2.2 Github repo.html 110 bytes
5. Foundations Tensorflow/4.1 Activation functions.html 110 bytes
5. Foundations Tensorflow/12.1 Three Body Problem.html 109 bytes
9. Example Sentiment Analysis/1.1 Sentiment Analysis.html 109 bytes
1. Course Structure and Development Environment/6.1 LaTeX syntax.html 108 bytes
4. Foundations NumPy/11.2 Aviation.html 104 bytes
8. Example Recommendations/15.3 Netflix prize.html 104 bytes
9. Example Sentiment Analysis/5.1 GloVe Vectors.html 101 bytes
6. Example Image recognition/7.1 Keras CNN layers.html 99 bytes
1. Course Structure and Development Environment/3.5 Kaggle Notebooks.html 96 bytes
8. Example Recommendations/7.1 Keras Embedding Layers documentation.html 96 bytes
1. Course Structure and Development Environment/3.1 Google Colaboratory.html 95 bytes
10. Example Fraud detection/2.1 Github repo.html 94 bytes
4. Foundations NumPy/10.1 Matplotlib home page.html 94 bytes
6. Example Image recognition/1.1 The MNIST database of handwritten digits.html 94 bytes
6. Example Image recognition/2.1 Example Github repository.html 94 bytes
6. Example Image recognition/4.1 MNIST models and their accuracy.html 94 bytes
7. Foundations Pandas/2.1 Github repo.html 94 bytes
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6. Example Image recognition/10.1 Keras Model API.html 91 bytes
1. Course Structure and Development Environment/3.4 Microsoft Azure Notebooks.html 89 bytes
4. Foundations NumPy/2.2 NumPy documentation.html 88 bytes
5. Foundations Tensorflow/1.1 Tensorflow home page.html 87 bytes
7. Foundations Pandas/1.1 Pandas Home Page.html 86 bytes
1. Course Structure and Development Environment/3.3 CoCalc.html 80 bytes
4. Foundations NumPy/2.1 NumPy home page.html 79 bytes
1. Course Structure and Development Environment/[DesireCourse.Net].url 51 bytes
6. Example Image recognition/[DesireCourse.Net].url 51 bytes
9. Example Sentiment Analysis/[DesireCourse.Net].url 51 bytes
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6. Example Image recognition/[CourseClub.Me].url 48 bytes
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