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[ FreeCourseWeb com ] Udemy - Neural Networks in Python - Deep Learning for Beginners
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1. Introduction
1. Welcome to the course.mp4
1. Welcome to the course.srt
2. Introduction to Neural Networks and Course flow.mp4
2. Introduction to Neural Networks and Course flow.srt
3. Course Resources.html
4. This is a milestone!.mp4
4. This is a milestone!.srt
10. Python - Building and training the Model
1. Different ways to create ANN using Keras.mp4
1. Different ways to create ANN using Keras.srt
2. Building the Neural Network using Keras.mp4
2. Building the Neural Network using Keras.srt
3. Compiling and Training the Neural Network model.mp4
3. Compiling and Training the Neural Network model.srt
4. Evaluating performance and Predicting using Keras.mp4
4. Evaluating performance and Predicting using Keras.srt
11. Python - Solving a Regression problem using ANN
1. Building Neural Network for Regression Problem.mp4
1. Building Neural Network for Regression Problem.srt
12. Complex ANN Architectures using Functional API
1. Using Functional API for complex architectures.mp4
1. Using Functional API for complex architectures.srt
13. Saving and Restoring Models
1. Saving - Restoring Models and Using Callbacks.mp4
1. Saving - Restoring Models and Using Callbacks.srt
14. Hyperparameter Tuning
1. Hyperparameter Tuning.mp4
1. Hyperparameter Tuning.srt
15. Add-on 1 Data Preprocessing
1. Gathering Business Knowledge.mp4
1. Gathering Business Knowledge.srt
10. Missing Value Imputation.mp4
10. Missing Value Imputation.srt
11. Missing Value Imputation in Python.mp4
11. Missing Value Imputation in Python.srt
12. Seasonality in Data.mp4
12. Seasonality in Data.srt
13. Bi-variate analysis and Variable transformation.mp4
13. Bi-variate analysis and Variable transformation.srt
14. Variable transformation and deletion in Python.mp4
14. Variable transformation and deletion in Python.srt
15. Non-usable variables.mp4
15. Non-usable variables.srt
16. Dummy variable creation Handling qualitative data.mp4
16. Dummy variable creation Handling qualitative data.srt
17. Dummy variable creation in Python.mp4
17. Dummy variable creation in Python.srt
18. Correlation Analysis.mp4
18. Correlation Analysis.srt
19. Correlation Analysis in Python.mp4
19. Correlation Analysis in Python.srt
2. Data Exploration.mp4
2. Data Exploration.srt
3. The Dataset and the Data Dictionary.mp4
3. The Dataset and the Data Dictionary.srt
4. Add-on Resources.html
5. Importing Data in Python.mp4
5. Importing Data in Python.srt
6. Univariate analysis and EDD.mp4
6. Univariate analysis and EDD.srt
7. EDD in Python.mp4
7. EDD in Python.srt
8. Outlier Treatment.mp4
8. Outlier Treatment.srt
9. Outlier Treatment in Python.mp4
9. Outlier Treatment in Python.srt
Files
00_Introduction_01.pdf
01_01_Lecture_TypesOfData.pdf
01_02_Lecture_TypesOfStatistics.pdf
01_03_Lecture_DataSummaryandGraph.pdf
01_04_Lecture_Centers.pdf
01_05_Lecture_Dispersion.pdf
03_01_PDE_Business_knowledge.pdf
03_02_PDE_Data_exploration.pdf
03_03_PDE_Raw_Data_Analysis_Uni.pdf
03_04_PDE_Univariate_Analysis_Uni.pdf
04_05_PDE_Missing_value.pdf
04_06_PDE_Outlier_Treatment.pdf
04_07_PDE_Seasonality.pdf
04_07_Variable_Transformation.pdf
04_08_PDE_Non_Usable_var.pdf
04_09_Variable Transformation.pdf
04_10_Correlation.pdf
04_11_Dummy_Var.pdf
05_01_Intro.pdf
05_02_Simple_lin_reg.pdf
05_03_Simple_lin_reg_Accuracy.pdf
05_04_F.pdf
05_04_Multiple_lin_reg.pdf
05_05_F_stat.pdf
05_06_Cat_var.pdf
05_07_Heteroscedasticity.pdf
05_09_Other_lin_model.pdf
05_10_Shrinkage_methods.pdf
05_10_Subset_Selection.pdf
05_11_Shrinkage_methods.pdf
05_12_Test_Train.pdf
05_13_Bias_Var_tradeoff.pdf
Code
Multiple_linear.ipynb
Python_CrashC1.ipynb
Python_cc2.ipynb
Simple_linear.ipynb
Customer.csv
Data
House_Price.csv
Movie_collection_test.csv
Movie_collection_train.csv
Product.txt
Lecture_machineLearning.pdf
16. Add-on 2 Classic ML models - Linear Regression
1. The Problem Statement.mp4
1. The Problem Statement.srt
10. Test-train split.mp4
10. Test-train split.srt
11. Bias Variance trade-off.mp4
11. Bias Variance trade-off.srt
12. Test train split in Python.mp4
12. Test train split in Python.srt
2. Basic Equations and Ordinary Least Squares (OLS) method.mp4
2. Basic Equations and Ordinary Least Squares (OLS) method.srt
3. Assessing accuracy of predicted coefficients.mp4
3. Assessing accuracy of predicted coefficients.srt
4. Assessing Model Accuracy RSE and R squared.mp4
4. Assessing Model Accuracy RSE and R squared.srt
5. Simple Linear Regression in Python.mp4
5. Simple Linear Regression in Python.srt
6. Multiple Linear Regression.mp4
6. Multiple Linear Regression.srt
7. The F - statistic.mp4
7. The F - statistic.srt
8. Interpreting results of Categorical variables.mp4
8. Interpreting results of Categorical variables.srt
9. Multiple Linear Regression in Python.mp4
9. Multiple Linear Regression in Python.srt
17. Practice Assignment
1. Neural Networks Classification Assignment.html
18. Bonus Section
1. The final milestone!.mp4
1. The final milestone!.srt
2. Congratulations & About your certificate.html
2. Setting up Python and Jupyter Notebook
1. Installing Python and Anaconda.mp4
1. Installing Python and Anaconda.srt
2. Opening Jupyter Notebook.mp4
2. Opening Jupyter Notebook.srt
3. Introduction to Jupyter.mp4
3. Introduction to Jupyter.srt
4. Arithmetic operators in Python Python Basics.mp4
4. Arithmetic operators in Python Python Basics.srt
5. Strings in Python Python Basics.mp4
5. Strings in Python Python Basics.srt
6. Lists, Tuples and Directories Python Basics.mp4
6. Lists, Tuples and Directories Python Basics.srt
7. Working with Numpy Library of Python.mp4
7. Working with Numpy Library of Python.srt
8. Working with Pandas Library of Python.mp4
8. Working with Pandas Library of Python.srt
9. Working with Seaborn Library of Python.mp4
9. Working with Seaborn Library of Python.srt
3. Single Cells - Perceptron and Sigmoid Neuron
1. Perceptron.mp4
1. Perceptron.srt
2. Activation Functions.mp4
2. Activation Functions.srt
3. Python - Creating Perceptron model.mp4
3. Python - Creating Perceptron model.srt
4. Neural Networks - Stacking cells to create network
1. Basic Terminologies.mp4
1. Basic Terminologies.srt
2. Gradient Descent.mp4
2. Gradient Descent.srt
3. Back Propagation.mp4
3. Back Propagation.srt
5. Important concepts Common Interview questions
1. Some Important Concepts.mp4
1. Some Important Concepts.srt
2. Quiz.html
6. Standard Model Parameters
1. Hyperparameters.mp4
1. Hyperparameters.srt
2. Quiz.html
7. Practice Test
1. Test your conceptual understanding.html
8. Tensorflow and Keras
1. Keras and Tensorflow.mp4
1. Keras and Tensorflow.srt
2. Installing Tensorflow and Keras.mp4
2. Installing Tensorflow and Keras.srt
9. Python - Dataset for classification problem
1. Dataset for classification.mp4
1. Dataset for classification.srt
2. Normalization and Test-Train split.mp4
2. Normalization and Test-Train split.srt
3. More about test-train split.html
Bonus Resources.txt
MyBlogs.pdf
Torrent description
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