[GigaCourse.Com] Udemy - Neural Networks in Python Deep Learning for Beginners

Creation Time	July 13, 2025, 4:05 a.m.
Last Access Time	Oct. 25, 2025, 2:03 p.m.
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Keywords	for Neural Python - Deep Udemy Beginners GigaCourse Learning in Com Networks
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11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.mp4 155.9 MB
13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 151.6 MB
4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 122.2 MB
15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.mp4 100.4 MB
16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 92.1 MB
12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.mp4 92.1 MB
3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.mp4 86.6 MB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.mp4 81.7 MB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.mp4 79.1 MB
15. Add-on 1 Data Preprocessing/18. Correlation Analysis.mp4 71.6 MB
15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.mp4 70.2 MB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.mp4 69.9 MB
16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.mp4 69.7 MB
15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 69.4 MB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 65.2 MB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 64.4 MB
16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.mp4 63.4 MB
5. Important concepts Common Interview questions/1. Some Important Concepts.mp4 62.2 MB
15. Add-on 1 Data Preprocessing/7. EDD in Python.mp4 61.8 MB
14. Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 60.6 MB
4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 60.3 MB
2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 60.3 MB
9. Python - Dataset for classification problem/1. Dataset for classification.mp4 56.1 MB
16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.mp4 56.0 MB
15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.mp4 55.3 MB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 46.9 MB
6. Standard Model Parameters/1. Hyperparameters.mp4 45.3 MB
16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.mp4 44.9 MB
3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4 44.8 MB
9. Python - Dataset for classification problem/2. Normalization and Test-Train split.mp4 44.2 MB
15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.mp4 44.1 MB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 43.9 MB
16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 43.6 MB
16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 43.4 MB
16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.mp4 41.9 MB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 40.9 MB
4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 40.4 MB
2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 40.4 MB
15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.mp4 36.8 MB
3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4 34.6 MB
16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.mp4 34.3 MB
1. Introduction/2. Introduction to Neural Networks and Course flow.mp4 29.1 MB
15. Add-on 1 Data Preprocessing/5. Importing Data in Python.mp4 27.8 MB
15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.mp4 26.5 MB
16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.mp4 25.1 MB
15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.mp4 25.0 MB
15. Add-on 1 Data Preprocessing/8. Outlier Treatment.mp4 24.5 MB
15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.mp4 24.2 MB
15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.mp4 23.4 MB
16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.mp4 22.5 MB
15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.mp4 22.3 MB
1. Introduction/1. Welcome to the course.mp4 21.4 MB
1. Introduction/4. This is a milestone!.mp4 20.7 MB
15. Add-on 1 Data Preprocessing/2. Data Exploration.mp4 20.5 MB
15. Add-on 1 Data Preprocessing/15. Non-usable variables.mp4 20.2 MB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras.mp4 20.1 MB
15. Add-on 1 Data Preprocessing/12. Seasonality in Data.mp4 17.0 MB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 16.3 MB
8. Tensorflow and Keras/1. Keras and Tensorflow.mp4 14.9 MB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 12.7 MB
18. Bonus Section/1. The final milestone!.mp4 11.8 MB
10. Python - Building and training the Model/1. Different ways to create ANN using Keras.mp4 10.8 MB
16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.mp4 9.4 MB
15. Add-on 1 Data Preprocessing/4.1 Files_linear_py.zip 8.9 MB
4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 22.8 KB
11. Python - Solving a Regression problem using ANN/1. Building Neural Network for Regression Problem.srt 21.7 KB
13. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 18.8 KB
15. Add-on 1 Data Preprocessing/13. Bi-variate analysis and Variable transformation.srt 18.3 KB
2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 17.0 KB
2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 16.4 KB
16. Add-on 2 Classic ML models - Linear Regression/3. Assessing accuracy of predicted coefficients.srt 15.9 KB
3. Single Cells - Perceptron and Sigmoid Neuron/3. Python - Creating Perceptron model.srt 14.5 KB
5. Important concepts Common Interview questions/1. Some Important Concepts.srt 13.1 KB
15. Add-on 1 Data Preprocessing/9. Outlier Treatment in Python.srt 13.0 KB
16. Add-on 2 Classic ML models - Linear Regression/9. Multiple Linear Regression in Python.srt 12.3 KB
2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 12.3 KB
10. Python - Building and training the Model/2. Building the Neural Network using Keras.srt 12.0 KB
4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 11.9 KB
12. Complex ANN Architectures using Functional API/1. Using Functional API for complex architectures.srt 11.5 KB
16. Add-on 2 Classic ML models - Linear Regression/5. Simple Linear Regression in Python.srt 11.4 KB
15. Add-on 1 Data Preprocessing/18. Correlation Analysis.srt 11.0 KB
2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 10.5 KB
15. Add-on 1 Data Preprocessing/7. EDD in Python.srt 10.4 KB
16. Add-on 2 Classic ML models - Linear Regression/10. Test-train split.srt 10.1 KB
16. Add-on 2 Classic ML models - Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 9.9 KB
3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt 9.7 KB
10. Python - Building and training the Model/3. Compiling and Training the Neural Network model.srt 9.6 KB
4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 9.5 KB
14. Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9.4 KB
2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 9.1 KB
10. Python - Building and training the Model/4. Evaluating performance and Predicting using Keras.srt 9.0 KB
16. Add-on 2 Classic ML models - Linear Regression/7. The F - statistic.srt 9.0 KB
6. Standard Model Parameters/1. Hyperparameters.srt 8.9 KB
2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 8.2 KB
16. Add-on 2 Classic ML models - Linear Regression/12. Test train split in Python.srt 8.1 KB
16. Add-on 2 Classic ML models - Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 8.0 KB
3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 7.9 KB
15. Add-on 1 Data Preprocessing/3. The Dataset and the Data Dictionary.srt 7.8 KB
15. Add-on 1 Data Preprocessing/14. Variable transformation and deletion in Python.srt 7.5 KB
2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 7.5 KB
9. Python - Dataset for classification problem/1. Dataset for classification.srt 7.2 KB
15. Add-on 1 Data Preprocessing/19. Correlation Analysis in Python.srt 6.6 KB
16. Add-on 2 Classic ML models - Linear Regression/11. Bias Variance trade-off.srt 6.4 KB
9. Python - Dataset for classification problem/2. Normalization and Test-Train split.srt 5.7 KB
16. Add-on 2 Classic ML models - Linear Regression/6. Multiple Linear Regression.srt 5.7 KB
15. Add-on 1 Data Preprocessing/5. Importing Data in Python.srt 5.6 KB
15. Add-on 1 Data Preprocessing/17. Dummy variable creation in Python.srt 5.5 KB
15. Add-on 1 Data Preprocessing/15. Non-usable variables.srt 5.4 KB
16. Add-on 2 Classic ML models - Linear Regression/8. Interpreting results of Categorical variables.srt 5.3 KB
15. Add-on 1 Data Preprocessing/16. Dummy variable creation Handling qualitative data.srt 4.9 KB
1. Introduction/2. Introduction to Neural Networks and Course flow.srt 4.6 KB
15. Add-on 1 Data Preprocessing/8. Outlier Treatment.srt 4.5 KB
15. Add-on 1 Data Preprocessing/10. Missing Value Imputation.srt 4.1 KB
15. Add-on 1 Data Preprocessing/11. Missing Value Imputation in Python.srt 4.1 KB
2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4.0 KB
15. Add-on 1 Data Preprocessing/1. Gathering Business Knowledge.srt 3.9 KB
8. Tensorflow and Keras/2. Installing Tensorflow and Keras.srt 3.8 KB
1. Introduction/4. This is a milestone!.srt 3.8 KB
15. Add-on 1 Data Preprocessing/12. Seasonality in Data.srt 3.8 KB
15. Add-on 1 Data Preprocessing/2. Data Exploration.srt 3.6 KB
8. Tensorflow and Keras/1. Keras and Tensorflow.srt 3.6 KB
15. Add-on 1 Data Preprocessing/6. Univariate analysis and EDD.srt 3.4 KB
1. Introduction/1. Welcome to the course.srt 3.1 KB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 2.6 KB
10. Python - Building and training the Model/1. Different ways to create ANN using Keras.srt 1.9 KB
18. Bonus Section/1. The final milestone!.srt 1.7 KB
16. Add-on 2 Classic ML models - Linear Regression/1. The Problem Statement.srt 1.6 KB
18. Bonus Section/2. Congratulations & About your certificate.html 1.6 KB
9. Python - Dataset for classification problem/3. More about test-train split.html 559 bytes
1. Introduction/3. Course Resources.html 319 bytes
17. Practice Assignment/1. Neural Networks Classification Assignment.html 213 bytes
5. Important concepts Common Interview questions/2. Quiz.html 209 bytes
6. Standard Model Parameters/2. Quiz.html 209 bytes
7. Practice Test/1. Test your conceptual understanding.html 209 bytes
15. Add-on 1 Data Preprocessing/4. Add-on Resources.html 131 bytes
0. Websites you may like/[CourseClub.ME].url 122 bytes
[CourseClub.Me].url 122 bytes
0. Websites you may like/[GigaCourse.Com].url 49 bytes
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[GigaCourse.Com] Udemy - Neural Networks in Python Deep Learning for Beginners

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