Udemy - Neural Networks in Python: Deep Learning for Beginners [FCS]

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1. Introduction
  • 1. Welcome to the course.mp4 (21.4 MB)
  • 1. Welcome to the course.srt (3.1 KB)
  • 2. Introduction to Neural Networks and Course flow.mp4 (29.1 MB)
  • 2. Introduction to Neural Networks and Course flow.srt (4.6 KB)
  • 3. Course Resources.html (0.3 KB)
  • 4. This is a milestone!.mp4 (20.7 MB)
  • 4. This is a milestone!.srt (3.8 KB)
10. Python - Building and training the Model
  • 1. Different ways to create ANN using Keras.mp4 (10.8 MB)
  • 1. Different ways to create ANN using Keras.srt (1.9 KB)
  • 2. Building the Neural Network using Keras.mp4 (79.1 MB)
  • 2. Building the Neural Network using Keras.srt (12.0 KB)
  • 3. Compiling and Training the Neural Network model.mp4 (81.7 MB)
  • 3. Compiling and Training the Neural Network model.srt (9.6 KB)
  • 4. Evaluating performance and Predicting using Keras.mp4 (69.9 MB)
  • 4. Evaluating performance and Predicting using Keras.srt (9.0 KB)
11. Python - Solving a Regression problem using ANN
  • 1. Building Neural Network for Regression Problem.mp4 (155.9 MB)
  • 1. Building Neural Network for Regression Problem.srt (21.7 KB)
12. Complex ANN Architectures using Functional API
  • 1. Using Functional API for complex architectures.mp4 (92.1 MB)
  • 1. Using Functional API for complex architectures.srt (11.5 KB)
13. Saving and Restoring Models
  • 1. Saving - Restoring Models and Using Callbacks.mp4 (151.6 MB)
  • 1. Saving - Restoring Models and Using Callbacks.srt (18.8 KB)
14. Hyperparameter Tuning
  • 1. Hyperparameter Tuning.mp4 (60.6 MB)
  • 1. Hyperparameter Tuning.srt (9.4 KB)
15. Add-on 1 Data Preprocessing
  • 1. Gathering Business Knowledge.mp4 (22.3 MB)
  • 1. Gathering Business Knowledge.srt (3.9 KB)
  • 10. Missing Value Imputation.mp4 (25.0 MB)
  • 10. Missing Value Imputation.srt (4.1 KB)
  • 11. Missing Value Imputation in Python.mp4 (23.4 MB)
  • 11. Missing Value Imputation in Python.srt (4.1 KB)
  • 12. Seasonality in Data.mp4 (17.0 MB)
  • 12. Seasonality in Data.srt (3.8 KB)
  • 13. Bi-variate analysis and Variable transformation.mp4 (100.4 MB)
  • 13. Bi-variate analysis and Variable transformation.srt (18.3 KB)
  • 14. Variable transformation and deletion in Python.mp4 (44.1 MB)
  • 14. Variable transformation and deletion in Python.srt (7.5 KB)
  • 15. Non-usable variables.mp4 (20.2 MB)
  • 15. Non-usable variables.srt (5.4 KB)
  • 16. Dummy variable creation Handling qualitative data.mp4 (36.8 MB)
  • 16. Dummy variable creation Handling qualitative data.srt (4.9 KB)
  • 17. Dummy variable creation in Python.mp4 (26.5 MB)
  • 17. Dummy variable creation in Python.srt (5.5 KB)
  • 18. Correlation Analysis.mp4 (71.6 MB)
  • 18. Correlation Analysis.srt (11.0 KB)
  • 19. Correlation Analysis in Python.mp4 (55.3 MB)
  • 19. Correlation Analysis in Python.srt (6.6 KB)
  • 2. Data Exploration.mp4 (20.5 MB)
  • 2. Data Exploration.srt (3.6 KB)
  • 3. The Dataset and the Data Dictionary.mp4 (69.4 MB)
  • 3. The Dataset and the Data Dictionary.srt (7.8 KB)
  • 4. Add-on Resources.html (0.1 KB)
  • 4.1 Files_linear_py.zip (8.9 MB)
  • 5. Importing Data in Python.mp4 (27.8 MB)
  • 5. Importing Data in Python.srt (5.6 KB)
  • 6. Univariate analysis and EDD.mp4 (24.2 MB)
  • 6. Univariate analysis and EDD.srt (3.4 KB)
  • 7. EDD in Python.mp4 (61.8 MB)
  • 7. EDD in Python.srt (10.4 KB)
  • 8. Outlier Treatment.mp4 (24.5 MB)
  • 8. Outlier Treatment.srt (4.5 KB)
  • 9. Outlier Treatment in Python.mp4 (70.2 MB)
  • 9. Outlier Treatment in Python.srt (13.0 KB)
16. Add-on 2 Classic ML models - Linear Regression
  • 1. The Problem Statement.mp4 (9.4 MB)
  • 1. The Problem Statement.srt (1.6 KB)
  • 10. Test-train split.mp4 (41.9 MB)
  • 10. Test-train split.srt (10.1 KB)
  • 11. Bias Variance trade-off.mp4 (25.1 MB)
  • 11. Bias Variance trade-off.srt (6.4 KB)
  • 12. Test train split in Python.mp4 (44.9 MB)
  • 12. Test train split in Python.srt (8.1 KB)
  • 2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 (43.4 MB)
  • 2. Basic Equations and Ordinary Least Squares (OLS) method.srt (9.9 KB)
  • 3. Assessing accuracy of predicted coefficients.mp4 (92.1 MB)
  • 3. Assessing accuracy of predicted coefficients.srt (15.9 KB)
  • 4. Assessing Model Accuracy RSE and R squared.mp4 (43.6 MB)
  • 4. Assessing Model Accuracy RSE and R squared.srt (8.0 KB)
  • 5. Simple Linear Regression in Python.mp4 (63.4 MB)
  • 5. Simple Linear Regression in Python.srt (11.4 KB)
  • 6. Multiple Linear Regression.mp4 (34.3 MB)
  • 6. Multiple Linear Regression.srt (5.7 KB)
  • 7. The F - statistic.mp4 (56.0 MB)
  • 7. The F - statistic.srt (9.0 KB)
  • 8. Interpreting results of Categorical variables.mp4 (22.5 MB)
  • 8. Interpreting results of Categorical variables.srt (5.3 KB)
  • 9. Multiple Linear Regression in Python.mp4 (69.7 MB)
  • 9. Multiple Linear Regression in Python.srt (12.3 KB)
17. Practice Assignment
  • 1. Neural Networks Classification Assignment.html (0.2 KB)
18. Bonus Section
  • 1. The final milestone!.mp4 (11.8 MB)
  • 1. The final milestone!.srt (1.7 KB)
  • 2. Congratulations & About your certificate.html (1.6 KB)
2. Setting up Python and Jupyter Notebook
  • 1. Installing Python and Anaconda.mp4 (16.3 MB)
  • 1. Installing Python and Anaconda.srt (2.6 KB)
  • 2. Opening Jupyter Notebook.mp4 (65.2 MB)
  • Description

    Udemy - Neural Networks in Python: Deep Learning for Beginners [FCS]

    Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python

    Created by Start-Tech Academy
    Last updated 11/2021
    English
    English [Auto]


    For more Udemy Courses: https://freecoursesite.com
    Our Forum for Discussion: https://forum.freecoursesite.com



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3 GB
seeders:8
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Udemy - Neural Networks in Python: Deep Learning for Beginners [FCS]


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