FreeCourseSite com Udemy Machine Learning Deep Learning in Python

FreeCourseSite com Udemy Machine Learning Deep Learning in Python

Name	[FreeCourseSite.com] Udemy - Machine Learning & Deep Learning in Python & R	DOWNLOAD Copy Link
Total Size	14.1 GB
Total Files	559
Hash	5A9B8524969D3F34B3FD8226EAA63A38D5B4E63C

/0. Websites you may like/
[CourseClub.ME].url	0.1 KB
[FCS Forum].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
/1. Introduction/
1. Introduction.mp4	30.8 MB
1. Introduction.srt	4.6 KB
2. Course Resources.html	0.4 KB
/10. Logistic Regression/
1. Logistic Regression.mp4	34.5 MB
1. Logistic Regression.srt	8.8 KB
10. Evaluating performance of model.mp4	36.9 MB
10. Evaluating performance of model.srt	9.6 KB
11. Evaluating model performance in Python.mp4	9.4 MB
11. Evaluating model performance in Python.srt	2.7 KB
12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4	58.4 MB
12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt	7.6 KB
2. Training a Simple Logistic Model in Python.mp4	50.2 MB
2. Training a Simple Logistic Model in Python.srt	10.9 KB
3. Training a Simple Logistic model in R.mp4	26.8 MB
3. Training a Simple Logistic model in R.srt	4.3 KB
4. Result of Simple Logistic Regression.mp4	28.2 MB
4. Result of Simple Logistic Regression.srt	6.0 KB
5. Logistic with multiple predictors.mp4	9.0 MB
5. Logistic with multiple predictors.srt	3.0 KB
6. Training multiple predictor Logistic model in Python.mp4	27.5 MB
6. Training multiple predictor Logistic model in Python.srt	6.2 KB
7. Training multiple predictor Logistic model in R.mp4	16.6 MB
7. Training multiple predictor Logistic model in R.srt	2.1 KB
8. Confusion Matrix.mp4	22.1 MB
8. Confusion Matrix.srt	5.0 KB
9. Creating Confusion Matrix in Python.mp4	53.7 MB
9. Creating Confusion Matrix in Python.srt	11.1 KB
/11. Linear Discriminant Analysis (LDA)/
1. Linear Discriminant Analysis.mp4	42.9 MB
1. Linear Discriminant Analysis.srt	12.2 KB
2. LDA in Python.mp4	12.0 MB
2. LDA in Python.srt	2.6 KB
3. Linear Discriminant Analysis in R.mp4	78.0 MB
3. Linear Discriminant Analysis in R.srt	10.5 KB
/12. K-Nearest Neighbors classifier/
1. Test-Train Split.mp4	41.2 MB
1. Test-Train Split.srt	10.8 KB
2. Test-Train Split in Python.mp4	34.7 MB
2. Test-Train Split in Python.srt	7.6 KB
3. Test-Train Split in R.mp4	77.8 MB
3. Test-Train Split in R.srt	10.0 KB
4. K-Nearest Neighbors classifier.mp4	79.1 MB
4. K-Nearest Neighbors classifier.srt	10.2 KB
5. K-Nearest Neighbors in Python Part 1.mp4	39.0 MB
5. K-Nearest Neighbors in Python Part 1.srt	6.0 KB
6. K-Nearest Neighbors in Python Part 2.mp4	44.4 MB
6. K-Nearest Neighbors in Python Part 2.srt	7.1 KB
7. K-Nearest Neighbors in R.mp4	68.0 MB
7. K-Nearest Neighbors in R.srt	9.2 KB
/13. Comparing results from 3 models/
1. Understanding the results of classification models.mp4	43.7 MB
1. Understanding the results of classification models.srt	7.7 KB
2. Summary of the three models.mp4	23.3 MB
2. Summary of the three models.srt	6.1 KB
/14. Simple Decision Trees/
1. Basics of Decision Trees.mp4	44.7 MB
1. Basics of Decision Trees.srt	11.5 KB
10. Test-Train split in Python.mp4	26.1 MB
10. Test-Train split in Python.srt	6.3 KB
11. Splitting Data into Test and Train Set in R.mp4	46.1 MB
11. Splitting Data into Test and Train Set in R.srt	6.0 KB
12. Creating Decision tree in Python.mp4	18.7 MB
12. Creating Decision tree in Python.srt	4.4 KB
13. Building a Regression Tree in R.mp4	108.4 MB
13. Building a Regression Tree in R.srt	15.9 KB
14. Evaluating model performance in Python.mp4	17.2 MB
14. Evaluating model performance in Python.srt	4.8 KB
15. Plotting decision tree in Python.mp4	22.5 MB
15. Plotting decision tree in Python.srt	5.4 KB
16. Pruning a tree.mp4	19.4 MB
16. Pruning a tree.srt	4.6 KB
17. Pruning a tree in Python.mp4	77.1 MB
17. Pruning a tree in Python.srt	11.0 KB
18. Pruning a Tree in R.mp4	86.1 MB
18. Pruning a Tree in R.srt	9.9 KB
2. Understanding a Regression Tree.mp4	45.8 MB
2. Understanding a Regression Tree.srt	12.2 KB
3. The stopping criteria for controlling tree growth.mp4	14.6 MB
3. The stopping criteria for controlling tree growth.srt	3.6 KB
4. The Data set for this part.mp4	39.1 MB
4. The Data set for this part.srt	3.4 KB
5. Importing the Data set into Python.mp4	27.1 MB
5. Importing the Data set into Python.srt	6.0 KB
6. Importing the Data set into R.mp4	45.8 MB
6. Importing the Data set into R.srt	7.4 KB
7. Missing value treatment in Python.mp4	18.8 MB
7. Missing value treatment in Python.srt	3.8 KB
8. Dummy Variable creation in Python.mp4	26.2 MB
8. Dummy Variable creation in Python.srt	5.5 KB
9. Dependent- Independent Data split in Python.mp4	15.9 MB
9. Dependent- Independent Data split in Python.srt	4.3 KB
/15. Simple Classification Tree/
1. Classification tree.mp4	29.6 MB
1. Classification tree.srt	6.9 KB
2. The Data set for Classification problem.mp4	19.5 MB
2. The Data set for Classification problem.srt	2.0 KB
3. Classification tree in Python Preprocessing.mp4	47.6 MB
3. Classification tree in Python Preprocessing.srt	9.1 KB
4. Classification tree in Python Training.mp4	86.7 MB
4. Classification tree in Python Training.srt	14.9 KB
5. Building a classification Tree in R.mp4	89.2 MB
5. Building a classification Tree in R.srt	10.4 KB
6. Advantages and Disadvantages of Decision Trees.mp4	7.2 MB
6. Advantages and Disadvantages of Decision Trees.srt	1.7 KB
/16. Ensemble technique 1 - Bagging/
1. Ensemble technique 1 - Bagging.mp4	29.5 MB
1. Ensemble technique 1 - Bagging.srt	7.4 KB
2. Ensemble technique 1 - Bagging in Python.mp4	81.1 MB
2. Ensemble technique 1 - Bagging in Python.srt	12.6 KB
3. Bagging in R.mp4	61.8 MB
3. Bagging in R.srt	7.3 KB
/17. Ensemble technique 2 - Random Forests/
1. Ensemble technique 2 - Random Forests.mp4	19.1 MB
1. Ensemble technique 2 - Random Forests.srt	4.7 KB
2. Ensemble technique 2 - Random Forests in Python.mp4	49.0 MB
2. Ensemble technique 2 - Random Forests in Python.srt	6.9 KB
3. Using Grid Search in Python.mp4	84.6 MB
3. Using Grid Search in Python.srt	14.0 KB
4. Random Forest in R.mp4	32.2 MB
4. Random Forest in R.srt	4.9 KB
/18. Ensemble technique 3 - Boosting/
1. Boosting.mp4	32.1 MB
1. Boosting.srt	8.0 KB
2. Ensemble technique 3a - Boosting in Python.mp4	41.8 MB
2. Ensemble technique 3a - Boosting in Python.srt	5.6 KB
3. Gradient Boosting in R.mp4	72.5 MB
3. Gradient Boosting in R.srt	8.8 KB
4. Ensemble technique 3b - AdaBoost in Python.mp4	32.0 MB
4. Ensemble technique 3b - AdaBoost in Python.srt	4.5 KB
5. AdaBoosting in R.mp4	93.0 MB
5. AdaBoosting in R.srt	10.8 KB
6. Ensemble technique 3c - XGBoost in Python.mp4	78.6 MB
6. Ensemble technique 3c - XGBoost in Python.srt	11.7 KB
7. XGBoosting in R.mp4	169.1 MB
7. XGBoosting in R.srt	18.9 KB
/19. Maximum Margin Classifier/
1. Content flow.mp4	9.1 MB
1. Content flow.srt	1.8 KB
2. The Concept of a Hyperplane.mp4	30.8 MB
2. The Concept of a Hyperplane.srt	5.4 KB
3. Maximum Margin Classifier.mp4	23.6 MB
3. Maximum Margin Classifier.srt	3.5 KB
4. Limitations of Maximum Margin Classifier.mp4	11.1 MB
4. Limitations of Maximum Margin Classifier.srt	2.7 KB
/2. Setting up Python and Jupyter Notebook/
1. Installing Python and Anaconda.mp4	17.1 MB
1. Installing Python and Anaconda.srt	2.7 KB
2. Opening Jupyter Notebook.mp4	68.4 MB
2. Opening Jupyter Notebook.srt	10.1 KB
3. Introduction to Jupyter.mp4	42.9 MB
3. Introduction to Jupyter.srt	13.5 KB
4. Arithmetic operators in Python Python Basics.mp4	13.4 MB
4. Arithmetic operators in Python Python Basics.srt	4.5 KB
5. Strings in Python Python Basics.mp4	67.6 MB
5. Strings in Python Python Basics.srt	18.4 KB
6. Lists, Tuples and Directories Python Basics.mp4	63.3 MB
6. Lists, Tuples and Directories Python Basics.srt	20.6 KB
7. Working with Numpy Library of Python.mp4	46.0 MB
7. Working with Numpy Library of Python.srt	12.1 KB
8. Working with Pandas Library of Python.mp4	49.2 MB
8. Working with Pandas Library of Python.srt	10.4 KB
9. Working with Seaborn Library of Python.mp4	42.3 MB
9. Working with Seaborn Library of Python.srt	8.4 KB
/20. Support Vector Classifier/
1. Support Vector classifiers.mp4	58.9 MB
1. Support Vector classifiers.srt	11.1 KB
2. Limitations of Support Vector Classifiers.mp4	11.3 MB
2. Limitations of Support Vector Classifiers.srt	1.7 KB
/21. Support Vector Machines/
1. Kernel Based Support Vector Machines.mp4	42.1 MB
1. Kernel Based Support Vector Machines.srt	6.9 KB
/22. Creating Support Vector Machine Model in Python/
1. Regression and Classification Models.mp4	4.2 MB
1. Regression and Classification Models.srt	0.8 KB
10. Classification model - Standardizing the data.mp4	10.2 MB
10. Classification model - Standardizing the data.srt	1.9 KB
11. SVM Based classification model.mp4	67.2 MB
11. SVM Based classification model.srt	12.7 KB
12. Hyper Parameter Tuning.mp4	60.5 MB
12. Hyper Parameter Tuning.srt	11.0 KB
13. Polynomial Kernel with Hyperparameter Tuning.mp4	24.0 MB
13. Polynomial Kernel with Hyperparameter Tuning.srt	4.6 KB
14. Radial Kernel with Hyperparameter Tuning.mp4	39.0 MB
14. Radial Kernel with Hyperparameter Tuning.srt	7.4 KB
2. The Data set for the Regression problem.mp4	39.0 MB
2. The Data set for the Regression problem.srt	3.4 KB
3. Importing data for regression model.mp4	27.1 MB
3. Importing data for regression model.srt	6.0 KB
4. X-y Split.mp4	15.9 MB
4. X-y Split.srt	4.3 KB
5. Test-Train Split.mp4	26.1 MB
5. Test-Train Split.srt	6.3 KB
6. Standardizing the data.mp4	40.3 MB
6. Standardizing the data.srt	6.7 KB
7. SVM based Regression Model in Python.mp4	70.9 MB
7. SVM based Regression Model in Python.srt	10.7 KB
8. The Data set for the Classification problem.mp4	19.4 MB
8. The Data set for the Classification problem.srt	2.0 KB
9. Classification model - Preprocessing.mp4	47.6 MB
9. Classification model - Preprocessing.srt	9.1 KB
/23. Creating Support Vector Machine Model in R/
1. Importing Data into R.mp4	56.3 MB
1. Importing Data into R.srt	9.1 KB
2. Test-Train Split.mp4	52.9 MB
2. Test-Train Split.srt	6.2 KB
3. Classification SVM model using Linear Kernel.mp4	145.9 MB
3. Classification SVM model using Linear Kernel.srt	18.2 KB
4. Hyperparameter Tuning for Linear Kernel.mp4	63.4 MB
4. Hyperparameter Tuning for Linear Kernel.srt	7.1 KB
5. Polynomial Kernel with Hyperparameter Tuning.mp4	87.2 MB
5. Polynomial Kernel with Hyperparameter Tuning.srt	11.8 KB
6. Radial Kernel with Hyperparameter Tuning.mp4	59.4 MB
6. Radial Kernel with Hyperparameter Tuning.srt	7.4 KB
7. SVM based Regression Model in R.mp4	111.3 MB
7. SVM based Regression Model in R.srt	12.3 KB
/24. Introduction - Deep Learning/
1. Introduction to Neural Networks and Course flow.mp4	30.5 MB
1. Introduction to Neural Networks and Course flow.srt	4.9 KB
2. Perceptron.mp4	46.9 MB
2. Perceptron.srt	10.5 KB
3. Activation Functions.mp4	36.3 MB
3. Activation Functions.srt	8.4 KB
4. Python - Creating Perceptron model.mp4	90.8 MB
4. Python - Creating Perceptron model.srt	16.1 KB
/25. Neural Networks - Stacking cells to create network/
1. Basic Terminologies.mp4	42.4 MB
1. Basic Terminologies.srt	11.1 KB
2. Gradient Descent.mp4	63.3 MB
2. Gradient Descent.srt	13.0 KB
3. Back Propagation.mp4	128.1 MB
3. Back Propagation.srt	25.4 KB
4. Some Important Concepts.mp4	65.2 MB
4. Some Important Concepts.srt	14.0 KB
5. Hyperparameter.mp4	47.6 MB
5. Hyperparameter.srt	9.5 KB
/26. ANN in Python/
1. Keras and Tensorflow.mp4	15.6 MB
1. Keras and Tensorflow.srt	3.9 KB
10. Using Functional API for complex architectures.mp4	96.6 MB
10. Using Functional API for complex architectures.srt	13.3 KB
11. Saving - Restoring Models and Using Callbacks.mp4	158.9 MB
11. Saving - Restoring Models and Using Callbacks.srt	21.3 KB
12. Hyperparameter Tuning.mp4	63.6 MB
12. Hyperparameter Tuning.srt	10.0 KB
2. Installing Tensorflow and Keras.mp4	21.0 MB
2. Installing Tensorflow and Keras.srt	4.2 KB
3. Dataset for classification.mp4	58.9 MB
3. Dataset for classification.srt	8.1 KB
4. Normalization and Test-Train split.mp4	46.3 MB
4. Normalization and Test-Train split.srt	6.3 KB
5. Different ways to create ANN using Keras.mp4	11.3 MB
5. Different ways to create ANN using Keras.srt	2.0 KB
6. Building the Neural Network using Keras.mp4	83.0 MB
6. Building the Neural Network using Keras.srt	13.2 KB
7. Compiling and Training the Neural Network model.mp4	85.6 MB
7. Compiling and Training the Neural Network model.srt	10.3 KB
8. Evaluating performance and Predicting using Keras.mp4	73.3 MB
8. Evaluating performance and Predicting using Keras.srt	10.0 KB
9. Building Neural Network for Regression Problem.mp4	163.5 MB
9. Building Neural Network for Regression Problem.srt	24.3 KB
/27. ANN in R/
1. Installing Keras and Tensorflow.mp4	23.9 MB
1. Installing Keras and Tensorflow.srt	3.1 KB
2. Data Normalization and Test-Train Split.mp4	117.2 MB
2. Data Normalization and Test-Train Split.srt	13.2 KB
3. Building,Compiling and Training.mp4	137.1 MB
3. Building,Compiling and Training.srt	16.7 KB
4. Evaluating and Predicting.mp4	104.1 MB
4. Evaluating and Predicting.srt	10.4 KB
5. ANN with NeuralNets Package.mp4	88.5 MB
5. ANN with NeuralNets Package.srt	8.6 KB
6. Building Regression Model with Functional API.mp4	137.5 MB
6. Building Regression Model with Functional API.srt	13.9 KB
7. Complex Architectures using Functional API.mp4	83.4 MB
7. Complex Architectures using Functional API.srt	9.1 KB
8. Saving - Restoring Models and Using Callbacks.mp4	226.5 MB
8. Saving - Restoring Models and Using Callbacks.srt	21.9 KB
/28. CNN - Basics/
1. CNN Introduction.mp4	53.6 MB
1. CNN Introduction.srt	8.3 KB
2. Stride.mp4	17.4 MB
2. Stride.srt	3.1 KB
3. Padding.mp4	33.2 MB
3. Padding.srt	5.1 KB
4. Filters and Feature maps.mp4	55.3 MB
4. Filters and Feature maps.srt	7.8 KB
5. Channels.mp4	71.1 MB
5. Channels.srt	6.4 KB
6. PoolingLayer.mp4	49.2 MB
6. PoolingLayer.srt	6.0 KB
/29. Creating CNN model in Python/
1. CNN model in Python - Preprocessing.mp4	42.6 MB
1. CNN model in Python - Preprocessing.srt	5.9 KB
2. CNN model in Python - structure and Compile.mp4	45.4 MB
2. CNN model in Python - structure and Compile.srt	7.4 KB
3. CNN model in Python - Training and results.mp4	57.8 MB
3. CNN model in Python - Training and results.srt	6.6 KB
4. Comparison - Pooling vs Without Pooling in Python.mp4	60.8 MB
4. Comparison - Pooling vs Without Pooling in Python.srt	5.7 KB
/3. Setting up R Studio and R crash course/
1. Installing R and R studio.mp4	37.5 MB
1. Installing R and R studio.srt	7.0 KB
2. Basics of R and R studio.mp4	40.7 MB
2. Basics of R and R studio.srt	12.3 KB
3. Packages in R.mp4	87.0 MB
3. Packages in R.srt	12.5 KB
4. Inputting data part 1 Inbuilt datasets of R.mp4	42.7 MB
4. Inputting data part 1 Inbuilt datasets of R.srt	4.8 KB
5. Inputting data part 2 Manual data entry.mp4	26.8 MB
5. Inputting data part 2 Manual data entry.srt	3.4 KB
6. Inputting data part 3 Importing from CSV or Text files.mp4	63.0 MB
6. Inputting data part 3 Importing from CSV or Text files.srt	7.2 KB
7. Creating Barplots in R.mp4	101.4 MB
7. Creating Barplots in R.srt	15.4 KB
8. Creating Histograms in R.mp4	44.1 MB
8. Creating Histograms in R.srt	6.3 KB
/30. Creating CNN model in R/
1. CNN on MNIST Fashion Dataset - Model Architecture.mp4	7.7 MB
1. CNN on MNIST Fashion Dataset - Model Architecture.srt	2.4 KB
2. Data Preprocessing.mp4	70.3 MB
2. Data Preprocessing.srt	7.6 KB
3. Creating Model Architecture.mp4	75.1 MB
3. Creating Model Architecture.srt	6.4 KB
4. Compiling and training.mp4	33.8 MB
4. Compiling and training.srt	3.2 KB
5. Model Performance.mp4	71.4 MB
5. Model Performance.srt	6.7 KB
6. Comparison - Pooling vs Without Pooling in R.mp4	46.8 MB
6. Comparison - Pooling vs Without Pooling in R.srt	4.3 KB
/31. Project Creating CNN model from scratch in Python/
1. Project - Introduction.mp4	51.8 MB
1. Project - Introduction.srt	7.7 KB
2. Data for the project.html	0.2 KB
3. Project - Data Preprocessing in Python.mp4	75.3 MB
3. Project - Data Preprocessing in Python.srt	9.4 KB
4. Project - Training CNN model in Python.mp4	69.2 MB
4. Project - Training CNN model in Python.srt	9.4 KB
5. Project in Python - model results.mp4	22.1 MB
5. Project in Python - model results.srt	3.0 KB
/32. Project Creating CNN model from scratch/
1. Project in R - Data Preprocessing.mp4	92.0 MB
1. Project in R - Data Preprocessing.srt	12.2 KB
2. CNN Project in R - Structure and Compile.mp4	48.4 MB
2. CNN Project in R - Structure and Compile.srt	5.7 KB
3. Project in R - Training.mp4	25.8 MB
3. Project in R - Training.srt	3.2 KB
4. Project in R - Model Performance.mp4	24.3 MB
4. Project in R - Model Performance.srt	2.6 KB
5. Project in R - Data Augmentation.mp4	59.1 MB
5. Project in R - Data Augmentation.srt	8.0 KB
6. Project in R - Validation Performance.mp4	24.8 MB
6. Project in R - Validation Performance.srt	2.6 KB
/33. Project Data Augmentation for avoiding overfitting/
1. Project - Data Augmentation Preprocessing.mp4	43.4 MB
1. Project - Data Augmentation Preprocessing.srt	7.4 KB
2. Project - Data Augmentation Training and Results.mp4	55.6 MB
2. Project - Data Augmentation Training and Results.srt	7.0 KB
/34. Transfer Learning Basics/
1. ILSVRC.mp4	21.9 MB
1. ILSVRC.srt	4.7 KB
2. LeNET.mp4	7.3 MB
2. LeNET.srt	1.9 KB
3. VGG16NET.mp4	10.9 MB
3. VGG16NET.srt	2.0 KB
4. GoogLeNet.mp4	22.4 MB
4. GoogLeNet.srt	3.3 KB
5. Transfer Learning.mp4	31.5 MB
5. Transfer Learning.srt	5.6 KB
6. Project - Transfer Learning - VGG16.mp4	135.4 MB
6. Project - Transfer Learning - VGG16.srt	20.9 KB
/35. Transfer Learning in R/
1. Project - Transfer Learning - VGG16 (Implementation).mp4	106.5 MB
1. Project - Transfer Learning - VGG16 (Implementation).srt	14.5 KB
2. Project - Transfer Learning - VGG16 (Performance).mp4	67.2 MB
2. Project - Transfer Learning - VGG16 (Performance).srt	9.0 KB
/36. Time Series Analysis and Forecasting/
1. Introduction.mp4	12.9 MB
1. Introduction.srt	2.2 KB
2. Time Series Forecasting - Use cases.mp4	27.2 MB
2. Time Series Forecasting - Use cases.srt	2.6 KB
3. Forecasting model creation - Steps.mp4	10.6 MB
3. Forecasting model creation - Steps.srt	3.0 KB
4. Forecasting model creation - Steps 1 (Goal).mp4	36.2 MB
4. Forecasting model creation - Steps 1 (Goal).srt	6.6 KB
5. Time Series - Basic Notations.mp4	65.5 MB
5. Time Series - Basic Notations.srt	9.9 KB
/37. Time Series - Preprocessing in Python/
1. Data Loading in Python.mp4	114.1 MB
1. Data Loading in Python.srt	18.1 KB
10. Exponential Smoothing.mp4	8.8 MB
10. Exponential Smoothing.srt	2.2 KB
2. Time Series - Visualization Basics.mp4	66.8 MB
2. Time Series - Visualization Basics.srt	10.5 KB
3. Time Series - Visualization in Python.mp4	173.2 MB
3. Time Series - Visualization in Python.srt	29.6 KB
4. Time Series - Feature Engineering Basics.mp4	62.4 MB
4. Time Series - Feature Engineering Basics.srt	12.0 KB
5. Time Series - Feature Engineering in Python.mp4	118.2 MB
5. Time Series - Feature Engineering in Python.srt	19.7 KB
6. Time Series - Upsampling and Downsampling.mp4	17.8 MB
6. Time Series - Upsampling and Downsampling.srt	4.4 KB
7. Time Series - Upsampling and Downsampling in Python.mp4	105.6 MB
7. Time Series - Upsampling and Downsampling in Python.srt	18.0 KB
8. Time Series - Power Transformation.mp4	15.6 MB
8. Time Series - Power Transformation.srt	2.7 KB
9. Moving Average.mp4	40.6 MB
9. Moving Average.srt	8.0 KB
/38. Time Series - Important Concepts/
1. White Noise.mp4	11.9 MB
1. White Noise.srt	2.6 KB
2. Random Walk.mp4	22.2 MB
2. Random Walk.srt	4.7 KB
3. Decomposing Time Series in Python.mp4	62.7 MB
3. Decomposing Time Series in Python.srt	10.7 KB
4. Differencing.mp4	33.9 MB
4. Differencing.srt	6.9 KB
5. Differencing in Python.mp4	118.5 MB
5. Differencing in Python.srt	16.1 KB
/39. Time Series - Implementation in Python/
1. Test Train Split in Python.mp4	60.2 MB
1. Test Train Split in Python.srt	12.3 KB
2. Naive (Persistence) model in Python.mp4	45.5 MB
2. Naive (Persistence) model in Python.srt	8.4 KB
3. Auto Regression Model - Basics.mp4	17.7 MB
3. Auto Regression Model - Basics.srt	3.7 KB
4. Auto Regression Model creation in Python.mp4	56.1 MB
4. Auto Regression Model creation in Python.srt	10.4 KB
5. Auto Regression with Walk Forward validation in Python.mp4	52.0 MB
5. Auto Regression with Walk Forward validation in Python.srt	9.0 KB
6. Moving Average model -Basics.mp4	25.3 MB
6. Moving Average model -Basics.srt	5.1 KB
7. Moving Average model in Python.mp4	59.4 MB
7. Moving Average model in Python.srt	9.8 KB
/4. Basics of Statistics/
1. Types of Data.mp4	23.8 MB
1. Types of Data.srt	5.4 KB
2. Types of Statistics.mp4	11.9 MB
2. Types of Statistics.srt	3.4 KB
3. Describing data Graphically.mp4	72.6 MB
3. Describing data Graphically.srt	14.2 KB
4. Measures of Centers.mp4	42.8 MB
4. Measures of Centers.srt	8.4 KB
5. Measures of Dispersion.mp4	25.9 MB
5. Measures of Dispersion.srt	6.2 KB
/40. Time Series - ARIMA model/
1. ACF and PACF.mp4	43.2 MB
1. ACF and PACF.srt	8.9 KB
2. ARIMA model - Basics.mp4	22.4 MB
2. ARIMA model - Basics.srt	5.2 KB
3. ARIMA model in Python.mp4	78.1 MB
3. ARIMA model in Python.srt	14.6 KB
4. ARIMA model with Walk Forward Validation in Python.mp4	33.7 MB
4. ARIMA model with Walk Forward Validation in Python.srt	6.4 KB
/41. Time Series - SARIMA model/
1. SARIMA model.mp4	40.9 MB
1. SARIMA model.srt	8.1 KB
2. SARIMA model in Python.mp4	69.5 MB
2. SARIMA model in Python.srt	11.9 KB
3. Stationary time Series.mp4	5.9 MB
3. Stationary time Series.srt	1.7 KB
/42. Bonus Section/
1. Congratulations & About your certificate.html	1.6 KB
/5. Introduction to Machine Learning/
1. Introduction to Machine Learning.mp4	114.5 MB
1. Introduction to Machine Learning.srt	20.2 KB
2. Building a Machine Learning Model.mp4	41.4 MB
2. Building a Machine Learning Model.srt	10.5 KB
/6. Data Preprocessing/
1. Gathering Business Knowledge.mp4	23.4 MB
1. Gathering Business Knowledge.srt	4.2 KB
10. Outlier Treatment in Python.mp4	73.7 MB
10. Outlier Treatment in Python.srt	14.5 KB
11. Outlier Treatment in R.mp4	32.2 MB
11. Outlier Treatment in R.srt	5.0 KB
12. Missing Value Imputation.mp4	26.2 MB
12. Missing Value Imputation.srt	4.3 KB
13. Missing Value Imputation in Python.mp4	24.6 MB
13. Missing Value Imputation in Python.srt	4.9 KB
14. Missing Value imputation in R.mp4	27.3 MB
14. Missing Value imputation in R.srt	4.2 KB
15. Seasonality in Data.mp4	17.9 MB
15. Seasonality in Data.srt	4.1 KB
16. Bi-variate analysis and Variable transformation.mp4	105.3 MB
16. Bi-variate analysis and Variable transformation.srt	19.8 KB
17. Variable transformation and deletion in Python.mp4	46.3 MB
17. Variable transformation and deletion in Python.srt	9.2 KB
18. Variable transformation in R.mp4	58.1 MB
18. Variable transformation in R.srt	10.2 KB
19. Non-usable variables.mp4	21.2 MB
19. Non-usable variables.srt	6.2 KB
2. Data Exploration.mp4	21.5 MB
2. Data Exploration.srt	4.0 KB
20. Dummy variable creation Handling qualitative data.mp4	38.6 MB
20. Dummy variable creation Handling qualitative data.srt	5.9 KB
21. Dummy variable creation in Python.mp4	27.8 MB
21. Dummy variable creation in Python.srt	6.4 KB
22. Dummy variable creation in R.mp4	46.1 MB
22. Dummy variable creation in R.srt	6.2 KB
23. Correlation Analysis.mp4	75.1 MB
23. Correlation Analysis.srt	12.2 KB
24. Correlation Analysis in Python.mp4	58.0 MB
24. Correlation Analysis in Python.srt	7.1 KB
25. Correlation Matrix in R.mp4	87.2 MB
25. Correlation Matrix in R.srt	9.8 KB
26. Quiz.html	0.1 KB
3. The Dataset and the Data Dictionary.mp4	72.6 MB
3. The Dataset and the Data Dictionary.srt	9.0 KB
4. Importing Data in Python.mp4	29.2 MB
4. Importing Data in Python.srt	6.6 KB
5. Importing the dataset into R.mp4	13.8 MB
5. Importing the dataset into R.srt	2.9 KB
6. Univariate analysis and EDD.mp4	25.4 MB
6. Univariate analysis and EDD.srt	4.1 KB
7. EDD in Python.mp4	64.8 MB
7. EDD in Python.srt	11.9 KB
8. EDD in R.mp4	101.7 MB
8. EDD in R.srt	13.5 KB
9. Outlier Treatment.mp4	25.7 MB
9. Outlier Treatment.srt	5.2 KB
/7. Linear Regression/
1. The Problem Statement.mp4	9.8 MB
1. The Problem Statement.srt	1.7 KB
10. Multiple Linear Regression in Python.mp4	73.1 MB
10. Multiple Linear Regression in Python.srt	14.6 KB
11. Multiple Linear Regression in R.mp4	65.4 MB
11. Multiple Linear Regression in R.srt	9.4 KB
12. Test-train split.mp4	43.9 MB
12. Test-train split.srt	11.1 KB
13. Bias Variance trade-off.mp4	26.3 MB
13. Bias Variance trade-off.srt	7.1 KB
14. Test train split in Python.mp4	47.1 MB
14. Test train split in Python.srt	8.9 KB
15. Test-Train Split in R.mp4	79.3 MB
15. Test-Train Split in R.srt	9.6 KB
16. Regression models other than OLS.mp4	17.4 MB
16. Regression models other than OLS.srt	4.9 KB
17. Subset selection techniques.mp4	82.9 MB
17. Subset selection techniques.srt	14.0 KB
18. Subset selection in R.mp4	66.6 MB
18. Subset selection in R.srt	8.4 KB
19. Shrinkage methods Ridge and Lasso.mp4	35.0 MB
19. Shrinkage methods Ridge and Lasso.srt	9.2 KB
2. Basic Equations and Ordinary Least Squares (OLS) method.mp4	45.5 MB
2. Basic Equations and Ordinary Least Squares (OLS) method.srt	10.7 KB
20. Ridge regression and Lasso in Python.mp4	135.1 MB
20. Ridge regression and Lasso in Python.srt	21.4 KB
21. Ridge regression and Lasso in R.mp4	108.5 MB
21. Ridge regression and Lasso in R.srt	12.7 KB
22. Heteroscedasticity.mp4	15.2 MB
22. Heteroscedasticity.srt	2.9 KB
3. Assessing accuracy of predicted coefficients.mp4	96.6 MB
3. Assessing accuracy of predicted coefficients.srt	17.8 KB
4. Assessing Model Accuracy RSE and R squared.mp4	45.7 MB
4. Assessing Model Accuracy RSE and R squared.srt	8.6 KB
5. Simple Linear Regression in Python.mp4	66.5 MB
5. Simple Linear Regression in Python.srt	13.4 KB
6. Simple Linear Regression in R.mp4	42.8 MB
6. Simple Linear Regression in R.srt	9.5 KB
7. Multiple Linear Regression.mp4	36.0 MB
7. Multiple Linear Regression.srt	6.5 KB
8. The F - statistic.mp4	58.7 MB
8. The F - statistic.srt	9.9 KB
9. Interpreting results of Categorical variables.mp4	23.6 MB
9. Interpreting results of Categorical variables.srt	6.1 KB
/8. Classification Models Data Preparation/
1. The Data and the Data Dictionary.mp4	82.8 MB
1. The Data and the Data Dictionary.srt	9.5 KB
10. Variable transformation and Deletion in Python.mp4	30.7 MB
10. Variable transformation and Deletion in Python.srt	4.4 KB
11. Variable transformation in R.mp4	39.9 MB
11. Variable transformation in R.srt	6.9 KB
12. Dummy variable creation in Python.mp4	27.7 MB
12. Dummy variable creation in Python.srt	6.3 KB
13. Dummy variable creation in R.mp4	46.5 MB
13. Dummy variable creation in R.srt	6.6 KB
2. Data Import in Python.mp4	23.1 MB
2. Data Import in Python.srt	5.4 KB
3. Importing the dataset into R.mp4	14.1 MB
3. Importing the dataset into R.srt	2.9 KB
4. EDD in Python.mp4	81.4 MB
4. EDD in Python.srt	18.2 KB
5. EDD in R.mp4	69.7 MB
5. EDD in R.srt	11.6 KB
6. Outlier treatment in Python.mp4	49.6 MB
6. Outlier treatment in Python.srt	9.8 KB
7. Outlier Treatment in R.mp4	26.6 MB
7. Outlier Treatment in R.srt	4.9 KB
8. Missing Value Imputation in Python.mp4	23.7 MB
8. Missing Value Imputation in Python.srt	4.9 KB
9. Missing Value imputation in R.mp4	20.0 MB
9. Missing Value imputation in R.srt	4.2 KB
/9. The Three classification models/
1. Three Classifiers and the problem statement.mp4	21.3 MB
1. Three Classifiers and the problem statement.srt	4.0 KB
2. Why can't we use Linear Regression.mp4	17.8 MB
2. Why can't we use Linear Regression.srt	5.6 KB
Total files 559