Udemy - Machine Learning & Deep Learning in Python & R (11.2021)

Creation Time	May 21, 2025, 2:55 a.m.
Last Access Time	May 21, 2025, 2:55 a.m.
File Size	12.5 GB
Keywords	11 Python - Deep Udemy Machine R 2021 Learning in
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Total Files	562
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27 - ANN in R/008 Saving - Restoring Models and Using Callbacks.mp4 216.0 MB
37 - Time Series - Preprocessing in Python/003 Time Series - Visualization in Python.mp4 165.2 MB
18 - Ensemble technique 3 - Boosting/007 XGBoosting in R.mp4 161.3 MB
26 - ANN in Python/009 Building Neural Network for Regression Problem.mp4 155.9 MB
26 - ANN in Python/011 Saving - Restoring Models and Using Callbacks.mp4 151.6 MB
23 - Creating Support Vector Machine Model in R/004 Classification SVM model using Linear Kernel.mp4 139.2 MB
27 - ANN in R/006 Building Regression Model with Functional API.mp4 131.1 MB
27 - ANN in R/003 Building,Compiling and Training.mp4 130.7 MB
34 - Transfer Learning _ Basics/006 Project - Transfer Learning - VGG16.mp4 129.1 MB
07 - Linear Regression/020 Ridge regression and Lasso in Python.mp4 128.8 MB
25 - Neural Networks - Stacking cells to create network/003 Back Propagation.mp4 122.2 MB
38 - Time Series - Important Concepts/005 Differencing in Python.mp4 113.0 MB
37 - Time Series - Preprocessing in Python/005 Time Series - Feature Engineering in Python.mp4 112.7 MB
27 - ANN in R/002 Data Normalization and Test-Train Split.mp4 111.8 MB
05 - Introduction to Machine Learning/001 Introduction to Machine Learning.mp4 109.2 MB
37 - Time Series - Preprocessing in Python/001 Data Loading in Python.mp4 108.9 MB
23 - Creating Support Vector Machine Model in R/008 SVM based Regression Model in R.mp4 106.1 MB
07 - Linear Regression/021 Ridge regression and Lasso in R.mp4 103.4 MB
14 - Simple Decision Trees/013 Building a Regression Tree in R.mp4 103.3 MB
35 - Transfer Learning in R/001 Project - Transfer Learning - VGG16 (Implementation).mp4 101.6 MB
37 - Time Series - Preprocessing in Python/007 Time Series - Upsampling and Downsampling in Python.mp4 100.7 MB
06 - Data Preprocessing/016 Bi-variate analysis and Variable transformation.mp4 100.4 MB
27 - ANN in R/004 Evaluating and Predicting.mp4 99.3 MB
06 - Data Preprocessing/008 EDD in R.mp4 97.0 MB
03 - Setting up R Studio and R crash course/007 Creating Barplots in R.mp4 96.7 MB
07 - Linear Regression/003 Assessing accuracy of predicted coefficients.mp4 92.1 MB
26 - ANN in Python/010 Using Functional API for complex architectures.mp4 92.1 MB
18 - Ensemble technique 3 - Boosting/005 AdaBoosting in R.mp4 88.7 MB
32 - Project _ Creating CNN model from scratch/001 Project in R - Data Preprocessing.mp4 87.8 MB
24 - Introduction - Deep Learning/004 Python - Creating Perceptron model.mp4 86.5 MB
15 - Simple Classification Tree/005 Building a classification Tree in R.mp4 85.1 MB
27 - ANN in R/005 ANN with NeuralNets Package.mp4 84.4 MB
23 - Creating Support Vector Machine Model in R/006 Polynomial Kernel with Hyperparameter Tuning.mp4 83.1 MB
06 - Data Preprocessing/025 Correlation Matrix in R.mp4 83.1 MB
03 - Setting up R Studio and R crash course/003 Packages in R.mp4 82.9 MB
15 - Simple Classification Tree/004 Classification tree in Python _ Training.mp4 82.7 MB
14 - Simple Decision Trees/018 Pruning a Tree in R.mp4 82.1 MB
26 - ANN in Python/007 Compiling and Training the Neural Network model.mp4 81.6 MB
17 - Ensemble technique 2 - Random Forests/003 Using Grid Search in Python.mp4 80.7 MB
27 - ANN in R/007 Complex Architectures using Functional API.mp4 79.6 MB
26 - ANN in Python/006 Building the Neural Network using Keras.mp4 79.1 MB
07 - Linear Regression/017 Subset selection techniques.mp4 79.1 MB
08 - Classification Models_ Data Preparation/001 The Data and the Data Dictionary.mp4 79.0 MB
08 - Classification Models_ Data Preparation/004 EDD in Python.mp4 77.6 MB
16 - Ensemble technique 1 - Bagging/002 Ensemble technique 1 - Bagging in Python.mp4 77.3 MB
07 - Linear Regression/015 Test-Train Split in R.mp4 75.6 MB
12 - K-Nearest Neighbors classifier/004 K-Nearest Neighbors classifier.mp4 75.4 MB
18 - Ensemble technique 3 - Boosting/006 Ensemble technique 3c - XGBoost in Python.mp4 75.0 MB
40 - Time Series - ARIMA model/003 ARIMA model in Python.mp4 74.4 MB
11 - Linear Discriminant Analysis (LDA)/003 Linear Discriminant Analysis in R.mp4 74.3 MB
12 - K-Nearest Neighbors classifier/003 Test-Train Split in R.mp4 74.2 MB
14 - Simple Decision Trees/017 Pruning a tree in Python.mp4 73.5 MB
31 - Project _ Creating CNN model from scratch in Python/003 Project - Data Preprocessing in Python.mp4 71.8 MB
30 - Creating CNN model in R/003 Creating Model Architecture.mp4 71.6 MB
06 - Data Preprocessing/023 Correlation Analysis.mp4 71.6 MB
06 - Data Preprocessing/010 Outlier Treatment in Python.mp4 70.3 MB
26 - ANN in Python/008 Evaluating performance and Predicting using Keras.mp4 69.9 MB
07 - Linear Regression/010 Multiple Linear Regression in Python.mp4 69.7 MB
06 - Data Preprocessing/003 The Dataset and the Data Dictionary.mp4 69.3 MB
18 - Ensemble technique 3 - Boosting/003 Gradient Boosting in R.mp4 69.1 MB
30 - Creating CNN model in R/005 Model Performance.mp4 68.1 MB
28 - CNN - Basics/005 Channels.mp4 67.8 MB
22 - Creating Support Vector Machine Model in Python/007 SVM based Regression Model in Python.mp4 67.6 MB
30 - Creating CNN model in R/002 Data Preprocessing.mp4 67.0 MB
08 - Classification Models_ Data Preparation/005 EDD in R.mp4 66.5 MB
41 - Time Series - SARIMA model/002 SARIMA model in Python.mp4 66.2 MB
31 - Project _ Creating CNN model from scratch in Python/004 Project - Training CNN model in Python.mp4 66.0 MB
04 - Basics of Statistics/003 Describing data Graphically.mp4 65.4 MB
02 - Setting up Python and Jupyter Notebook/003 Opening Jupyter Notebook.mp4 65.2 MB
12 - K-Nearest Neighbors classifier/007 K-Nearest Neighbors in R.mp4 64.9 MB
02 - Setting up Python and Jupyter Notebook/006 Strings in Python_ Python Basics.mp4 64.4 MB
22 - Creating Support Vector Machine Model in Python/011 SVM Based classification model.mp4 64.1 MB
37 - Time Series - Preprocessing in Python/002 Time Series - Visualization Basics.mp4 63.7 MB
07 - Linear Regression/018 Subset selection in R.mp4 63.5 MB
07 - Linear Regression/005 Simple Linear Regression in Python.mp4 63.4 MB
36 - Time Series Analysis and Forecasting/005 Time Series - Basic Notations.mp4 62.5 MB
07 - Linear Regression/011 Multiple Linear Regression in R.mp4 62.4 MB
25 - Neural Networks - Stacking cells to create network/004 Some Important Concepts.mp4 62.2 MB
06 - Data Preprocessing/007 EDD in Python.mp4 61.8 MB
26 - ANN in Python/012 Hyperparameter Tuning.mp4 60.6 MB
23 - Creating Support Vector Machine Model in R/005 Hyperparameter Tuning for Linear Kernel.mp4 60.5 MB
25 - Neural Networks - Stacking cells to create network/002 Gradient Descent.mp4 60.3 MB
02 - Setting up Python and Jupyter Notebook/007 Lists, Tuples and Directories_ Python Basics.mp4 60.3 MB
03 - Setting up R Studio and R crash course/006 Inputting data part 3_ Importing from CSV or Text files.mp4 60.1 MB
38 - Time Series - Important Concepts/003 Decomposing Time Series in Python.mp4 59.8 MB
37 - Time Series - Preprocessing in Python/004 Time Series - Feature Engineering Basics.mp4 59.5 MB
16 - Ensemble technique 1 - Bagging/003 Bagging in R.mp4 59.0 MB
29 - Creating CNN model in Python/004 Comparison - Pooling vs Without Pooling in Python.mp4 58.0 MB
22 - Creating Support Vector Machine Model in Python/012 Hyper Parameter Tuning.mp4 57.7 MB
39 - Time Series - Implementation in Python/001 Test Train Split in Python.mp4 57.4 MB
23 - Creating Support Vector Machine Model in R/007 Radial Kernel with Hyperparameter Tuning.mp4 56.7 MB
39 - Time Series - Implementation in Python/007 Moving Average model in Python.mp4 56.7 MB
32 - Project _ Creating CNN model from scratch/005 Project in R - Data Augmentation.mp4 56.4 MB
26 - ANN in Python/003 Dataset for classification.mp4 56.2 MB
20 - Support Vector Classifier/001 Support Vector classifiers.mp4 56.2 MB
07 - Linear Regression/008 The F - statistic.mp4 56.0 MB
10 - Logistic Regression/012 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 55.7 MB
06 - Data Preprocessing/018 Variable transformation in R.mp4 55.4 MB
06 - Data Preprocessing/024 Correlation Analysis in Python.mp4 55.3 MB
29 - Creating CNN model in Python/003 CNN model in Python - Training and results.mp4 55.2 MB
23 - Creating Support Vector Machine Model in R/001 Importing Data into R.mp4 53.7 MB
39 - Time Series - Implementation in Python/004 Auto Regression Model creation in Python.mp4 53.5 MB
33 - Project _ Data Augmentation for avoiding overfitting/002 Project - Data Augmentation Training and Results.mp4 53.0 MB
28 - CNN - Basics/004 Filters and Feature maps.mp4 52.7 MB
10 - Logistic Regression/009 Creating Confusion Matrix in Python.mp4 51.3 MB
28 - CNN - Basics/001 CNN Introduction.mp4 51.2 MB
39 - Time Series - Implementation in Python/005 Auto Regression with Walk Forward validation in Python.mp4 49.6 MB
31 - Project _ Creating CNN model from scratch in Python/001 Project - Introduction.mp4 49.4 MB
10 - Logistic Regression/002 Training a Simple Logistic Model in Python.mp4 47.9 MB
08 - Classification Models_ Data Preparation/006 Outlier treatment in Python.mp4 47.3 MB
02 - Setting up Python and Jupyter Notebook/009 Working with Pandas Library of Python.mp4 46.9 MB
28 - CNN - Basics/006 PoolingLayer.mp4 46.9 MB
17 - Ensemble technique 2 - Random Forests/002 Ensemble technique 2 - Random Forests in Python.mp4 46.7 MB
32 - Project _ Creating CNN model from scratch/002 CNN Project in R - Structure and Compile.mp4 46.1 MB
15 - Simple Classification Tree/003 Classification tree in Python _ Preprocessing.mp4 45.4 MB
22 - Creating Support Vector Machine Model in Python/009 Classification model - Preprocessing.mp4 45.4 MB
25 - Neural Networks - Stacking cells to create network/005 Hyperparameter.mp4 45.4 MB
07 - Linear Regression/014 Test train split in Python.mp4 44.9 MB
24 - Introduction - Deep Learning/002 Perceptron.mp4 44.7 MB
30 - Creating CNN model in R/006 Comparison - Pooling vs Without Pooling in R.mp4 44.6 MB
08 - Classification Models_ Data Preparation/013 Dummy variable creation in R.mp4 44.4 MB
26 - ANN in Python/004 Normalization and Test-Train split.mp4 44.2 MB
06 - Data Preprocessing/017 Variable transformation and deletion in Python.mp4 44.1 MB
06 - Data Preprocessing/022 Dummy variable creation in R.mp4 44.0 MB
14 - Simple Decision Trees/011 Splitting Data into Test and Train Set in R.mp4 44.0 MB
02 - Setting up Python and Jupyter Notebook/008 Working with Numpy Library of Python.mp4 43.9 MB
14 - Simple Decision Trees/002 Understanding a Regression Tree.mp4 43.7 MB
14 - Simple Decision Trees/006 Importing the Data set into R.mp4 43.7 MB
07 - Linear Regression/004 Assessing Model Accuracy_ RSE and R squared.mp4 43.6 MB
07 - Linear Regression/002 Basic Equations and Ordinary Least Squares (OLS) method.mp4 43.4 MB
39 - Time Series - Implementation in Python/002 Naive (Persistence) model in Python.mp4 43.4 MB
29 - Creating CNN model in Python/002 CNN model in Python - structure and Compile.mp4 43.2 MB
14 - Simple Decision Trees/001 Basics of Decision Trees.mp4 42.6 MB
12 - K-Nearest Neighbors classifier/006 K-Nearest Neighbors in Python_ Part 2.mp4 42.4 MB
03 - Setting up R Studio and R crash course/008 Creating Histograms in R.mp4 42.0 MB
07 - Linear Regression/012 Test-train split.mp4 41.9 MB
13 - Comparing results from 3 models/001 Understanding the results of classification models.mp4 41.6 MB
40 - Time Series - ARIMA model/001 ACF and PACF.mp4 41.2 MB
11 - Linear Discriminant Analysis (LDA)/001 Linear Discriminant Analysis.mp4 40.9 MB
02 - Setting up Python and Jupyter Notebook/004 Introduction to Jupyter.mp4 40.9 MB
07 - Linear Regression/006 Simple Linear Regression in R.mp4 40.8 MB
03 - Setting up R Studio and R crash course/004 Inputting data part 1_ Inbuilt datasets of R.mp4 40.7 MB
02 - Setting up Python and Jupyter Notebook/010 Working with Seaborn Library of Python.mp4 40.4 MB
18 - Ensemble technique 3 - Boosting/002 Ensemble technique 3a - Boosting in Python.mp4 39.9 MB
12 - K-Nearest Neighbors classifier/001 Test-Train Split.mp4 39.3 MB
41 - Time Series - SARIMA model/001 SARIMA model.mp4 39.0 MB
03 - Setting up R Studio and R crash course/002 Basics of R and R studio.mp4 38.8 MB
37 - Time Series - Preprocessing in Python/009 Moving Average.mp4 38.7 MB
04 - Basics of Statistics/004 Measures of Centers.mp4 38.6 MB
22 - Creating Support Vector Machine Model in Python/006 Standardizing the data.mp4 38.4 MB
08 - Classification Models_ Data Preparation/011 Variable transformation in R.mp4 38.0 MB
14 - Simple Decision Trees/004 The Data set for this part.mp4 37.3 MB
12 - K-Nearest Neighbors classifier/005 K-Nearest Neighbors in Python_ Part 1.mp4 37.2 MB
22 - Creating Support Vector Machine Model in Python/014 Radial Kernel with Hyperparameter Tuning.mp4 37.2 MB
22 - Creating Support Vector Machine Model in Python/002 The Data set for the Regression problem.mp4 37.2 MB
06 - Data Preprocessing/020 Dummy variable creation_ Handling qualitative data.mp4 36.8 MB
03 - Setting up R Studio and R crash course/001 Installing R and R studio.mp4 35.7 MB
10 - Logistic Regression/010 Evaluating performance of model.mp4 35.2 MB
24 - Introduction - Deep Learning/003 Activation Functions.mp4 34.6 MB
36 - Time Series Analysis and Forecasting/004 Forecasting model creation - Steps 1 (Goal).mp4 34.5 MB
07 - Linear Regression/007 Multiple Linear Regression.mp4 34.3 MB
07 - Linear Regression/019 Shrinkage methods_ Ridge and Lasso.mp4 33.3 MB
10 - Logistic Regression/001 Logistic Regression.mp4 32.9 MB
38 - Time Series - Important Concepts/004 Differencing.mp4 32.4 MB
30 - Creating CNN model in R/004 Compiling and training.mp4 32.2 MB
40 - Time Series - ARIMA model/004 ARIMA model with Walk Forward Validation in Python.mp4 32.1 MB
28 - CNN - Basics/003 Padding.mp4 31.6 MB
06 - Data Preprocessing/011 Outlier Treatment in R.mp4 30.7 MB
17 - Ensemble technique 2 - Random Forests/004 Random Forest in R.mp4 30.7 MB
18 - Ensemble technique 3 - Boosting/001 Boosting.mp4 30.6 MB
34 - Transfer Learning _ Basics/005 Transfer Learning.mp4 30.0 MB
19 - Maximum Margin Classifier/002 The Concept of a Hyperplane.mp4 29.4 MB
08 - Classification Models_ Data Preparation/010 Variable transformation and Deletion in Python.mp4 29.3 MB
15 - Simple Classification Tree/001 Classification tree.mp4 28.2 MB
06 - Data Preprocessing/004 Importing Data in Python.mp4 27.8 MB
10 - Logistic Regression/004 Result of Simple Logistic Regression.mp4 26.9 MB
06 - Data Preprocessing/021 Dummy variable creation in Python.mp4 26.5 MB
08 - Classification Models_ Data Preparation/012 Dummy variable creation in Python.mp4 26.4 MB
10 - Logistic Regression/006 Training multiple predictor Logistic model in Python.mp4 26.3 MB
06 - Data Preprocessing/014 Missing Value imputation in R.mp4 26.0 MB
36 - Time Series Analysis and Forecasting/002 Time Series Forecasting - Use cases.mp4 25.9 MB
14 - Simple Decision Trees/005 Importing the Data set into Python.mp4 25.8 MB
22 - Creating Support Vector Machine Model in Python/003 Importing data for regression model.mp4 25.8 MB
10 - Logistic Regression/003 Training a Simple Logistic model in R.mp4 25.6 MB
08 - Classification Models_ Data Preparation/007 Outlier Treatment in R.mp4 25.4 MB
07 - Linear Regression/013 Bias Variance trade-off.mp4 25.1 MB
06 - Data Preprocessing/012 Missing Value Imputation.mp4 25.0 MB
14 - Simple Decision Trees/008 Dummy Variable creation in Python.mp4 24.9 MB
14 - Simple Decision Trees/010 Test-Train split in Python.mp4 24.9 MB
22 - Creating Support Vector Machine Model in Python/005 Test-Train Split.mp4 24.9 MB
32 - Project _ Creating CNN model from scratch/003 Project in R - Training.mp4 24.6 MB
06 - Data Preprocessing/009 Outlier Treatment.mp4 24.5 MB
06 - Data Preprocessing/006 Univariate analysis and EDD.mp4 24.2 MB
39 - Time Series - Implementation in Python/006 Moving Average model -Basics.mp4 24.1 MB
32 - Project _ Creating CNN model from scratch/006 Project in R - Validation Performance.mp4 23.7 MB
06 - Data Preprocessing/013 Missing Value Imputation in Python.mp4 23.4 MB
22 - Creating Support Vector Machine Model in Python/013 Polynomial Kernel with Hyperparameter Tuning.mp4 22.9 MB
04 - Basics of Statistics/005 Measures of Dispersion.mp4 22.8 MB
08 - Classification Models_ Data Preparation/008 Missing Value Imputation in Python.mp4 22.6 MB
07 - Linear Regression/009 Interpreting results of Categorical variables.mp4 22.5 MB
19 - Maximum Margin Classifier/003 Maximum Margin Classifier.mp4 22.5 MB
08 - Classification Models_ Data Preparation/002 Data Import in Python.mp4 22.1 MB
04 - Basics of Statistics/001 Types of Data.mp4 21.8 MB
14 - Simple Decision Trees/015 Plotting decision tree in Python.mp4 21.5 MB
34 - Transfer Learning _ Basics/004 GoogLeNet.mp4 21.4 MB
38 - Time Series - Important Concepts/002 Random Walk.mp4 21.2 MB
10 - Logistic Regression/008 Confusion Matrix.mp4 21.1 MB
34 - Transfer Learning _ Basics/001 ILSVRC.mp4 20.9 MB
02 - Setting up Python and Jupyter Notebook/002 This is a milestone_.mp4 20.7 MB
09 - The Three classification models/001 Three Classifiers and the problem statement.mp4 20.3 MB
06 - Data Preprocessing/019 Non-usable variables.mp4 20.2 MB
06 - Data Preprocessing/002 Data Exploration.mp4 20.1 MB
26 - ANN in Python/002 Installing Tensorflow and Keras.mp4 20.1 MB
08 - Classification Models_ Data Preparation/009 Missing Value imputation in R.mp4 19.0 MB
15 - Simple Classification Tree/002 The Data set for Classification problem.mp4 18.6 MB
22 - Creating Support Vector Machine Model in Python/008 The Data set for the Classification problem.mp4 18.5 MB
14 - Simple Decision Trees/016 Pruning a tree.mp4 18.5 MB
14 - Simple Decision Trees/007 Missing value treatment in Python.mp4 17.9 MB
14 - Simple Decision Trees/012 Creating Decision tree in Python.mp4 17.9 MB
06 - Data Preprocessing/015 Seasonality in Data.mp4 17.0 MB
37 - Time Series - Preprocessing in Python/006 Time Series - Upsampling and Downsampling.mp4 17.0 MB
07 - Linear Regression/016 Regression models other than OLS.mp4 16.5 MB
14 - Simple Decision Trees/014 Evaluating model performance in Python.mp4 16.4 MB
02 - Setting up Python and Jupyter Notebook/001 Installing Python and Anaconda.mp4 16.3 MB
10 - Logistic Regression/007 Training multiple predictor Logistic model in R.mp4 15.8 MB
22 - Creating Support Vector Machine Model in Python/004 X-y Split.mp4 15.2 MB
14 - Simple Decision Trees/009 Dependent- Independent Data split in Python.mp4 15.2 MB
26 - ANN in Python/001 Keras and Tensorflow.mp4 14.9 MB
37 - Time Series - Preprocessing in Python/008 Time Series - Power Transformation.mp4 14.8 MB
35 - Transfer Learning in R/002 Project - Transfer Learning - VGG16 (Performance).mp4 14.6 MB
06 - Data Preprocessing/001 Gathering Business Knowledge.mp4 14.5 MB
07 - Linear Regression/022 Heteroscedasticity.mp4 14.5 MB
25 - Neural Networks - Stacking cells to create network/001 Basic Terminologies.mp4 14.4 MB
05 - Introduction to Machine Learning/002 Building a Machine Learning Model.mp4 12.6 MB
36 - Time Series Analysis and Forecasting/001 Introduction.mp4 12.3 MB
29 - Creating CNN model in Python/001 CNN model in Python - Preprocessing.mp4 11.9 MB
33 - Project _ Data Augmentation for avoiding overfitting/001 Project - Data Augmentation Preprocessing.mp4 11.8 MB
23 - Creating Support Vector Machine Model in R/002 Test-Train Split.mp4 10.7 MB
21 - Support Vector Machines/001 Kernel Based Support Vector Machines.mp4 10.7 MB
19 - Maximum Margin Classifier/004 Limitations of Maximum Margin Classifier.mp4 10.6 MB
12 - K-Nearest Neighbors classifier/002 Test-Train Split in Python.mp4 10.5 MB
34 - Transfer Learning _ Basics/003 VGG16NET.mp4 10.4 MB
16 - Ensemble technique 1 - Bagging/001 Ensemble technique 1 - Bagging.mp4 9.7 MB
22 - Creating Support Vector Machine Model in Python/010 Classification model - Standardizing the data.mp4 9.7 MB
18 - Ensemble technique 3 - Boosting/004 Ensemble technique 3b - AdaBoost in Python.mp4 9.4 MB
10 - Logistic Regression/011 Evaluating model performance in Python.mp4 9.0 MB
24 - Introduction - Deep Learning/001 Introduction to Neural Networks and Course flow.mp4 7.5 MB
01 - Introduction/001 Introduction.mp4 7.5 MB
40 - Time Series - ARIMA model/002 ARIMA model - Basics.mp4 6.6 MB
13 - Comparing results from 3 models/002 Summary of the three models.mp4 6.5 MB
09 - The Three classification models/002 Why can't we use Linear Regression_.mp4 6.2 MB
02 - Setting up Python and Jupyter Notebook/005 Arithmetic operators in Python_ Python Basics.mp4 5.8 MB
03 - Setting up R Studio and R crash course/005 Inputting data part 2_ Manual data entry.mp4 5.8 MB
17 - Ensemble technique 2 - Random Forests/001 Ensemble technique 2 - Random Forests.mp4 5.7 MB
31 - Project _ Creating CNN model from scratch in Python/005 Project in Python - model results.mp4 5.7 MB
27 - ANN in R/001 Installing Keras and Tensorflow.mp4 5.4 MB
39 - Time Series - Implementation in Python/003 Auto Regression Model - Basics.mp4 5.1 MB
32 - Project _ Creating CNN model from scratch/004 Project in R - Model Performance.mp4 5.0 MB
28 - CNN - Basics/002 Stride.mp4 4.7 MB
08 - Classification Models_ Data Preparation/003 Importing the dataset into R.mp4 4.6 MB
14 - Simple Decision Trees/003 The stopping criteria for controlling tree growth.mp4 4.6 MB
06 - Data Preprocessing/005 Importing the dataset into R.mp4 4.5 MB
11 - Linear Discriminant Analysis (LDA)/002 LDA in Python.mp4 4.0 MB
04 - Basics of Statistics/002 Types of Statistics.mp4 3.8 MB
36 - Time Series Analysis and Forecasting/003 Forecasting model creation - Steps.mp4 3.6 MB
38 - Time Series - Important Concepts/001 White Noise.mp4 3.5 MB
42 - Bonus Section/001 The final milestone_.mp4 3.3 MB
26 - ANN in Python/005 Different ways to create ANN using Keras.mp4 3.2 MB
10 - Logistic Regression/005 Logistic with multiple predictors.mp4 3.2 MB
37 - Time Series - Preprocessing in Python/010 Exponential Smoothing.mp4 2.9 MB
30 - Creating CNN model in R/001 CNN on MNIST Fashion Dataset - Model Architecture.mp4 2.9 MB
20 - Support Vector Classifier/002 Limitations of Support Vector Classifiers.mp4 2.6 MB
07 - Linear Regression/001 The Problem Statement.mp4 2.5 MB
19 - Maximum Margin Classifier/001 Content flow.mp4 2.4 MB
15 - Simple Classification Tree/006 Advantages and Disadvantages of Decision Trees.mp4 2.2 MB
41 - Time Series - SARIMA model/003 Stationary time Series.mp4 2.2 MB
34 - Transfer Learning _ Basics/002 LeNET.mp4 2.1 MB
22 - Creating Support Vector Machine Model in Python/001 Regression and Classification Models.mp4 1.2 MB
37 - Time Series - Preprocessing in Python/003 Time Series - Visualization in Python_Downloadly.ir_en.srt 28.9 KB
25 - Neural Networks - Stacking cells to create network/003 Back Propagation_Downloadly.ir_en.srt 24.8 KB
26 - ANN in Python/009 Building Neural Network for Regression Problem_Downloadly.ir_en.srt 23.8 KB
27 - ANN in R/008 Saving - Restoring Models and Using Callbacks_Downloadly.ir_en.srt 21.4 KB
07 - Linear Regression/020 Ridge regression and Lasso in Python_Downloadly.ir_en.srt 20.9 KB
26 - ANN in Python/011 Saving - Restoring Models and Using Callbacks_Downloadly.ir_en.srt 20.8 KB
34 - Transfer Learning _ Basics/006 Project - Transfer Learning - VGG16_Downloadly.ir_en.srt 20.4 KB
02 - Setting up Python and Jupyter Notebook/007 Lists, Tuples and Directories_ Python Basics_Downloadly.ir_en.srt 20.1 KB
05 - Introduction to Machine Learning/001 Introduction to Machine Learning_Downloadly.ir_en.srt 19.7 KB
06 - Data Preprocessing/016 Bi-variate analysis and Variable transformation_Downloadly.ir_en.srt 19.3 KB
37 - Time Series - Preprocessing in Python/005 Time Series - Feature Engineering in Python_Downloadly.ir_en.srt 19.3 KB
18 - Ensemble technique 3 - Boosting/007 XGBoosting in R_Downloadly.ir_en.srt 18.4 KB
02 - Setting up Python and Jupyter Notebook/006 Strings in Python_ Python Basics_Downloadly.ir_en.srt 18.0 KB
08 - Classification Models_ Data Preparation/004 EDD in Python_Downloadly.ir_en.srt 17.8 KB
23 - Creating Support Vector Machine Model in R/004 Classification SVM model using Linear Kernel_Downloadly.ir_en.srt 17.7 KB
37 - Time Series - Preprocessing in Python/001 Data Loading in Python_Downloadly.ir_en.srt 17.7 KB
37 - Time Series - Preprocessing in Python/007 Time Series - Upsampling and Downsampling in Python_Downloadly.ir_en.srt 17.6 KB
07 - Linear Regression/003 Assessing accuracy of predicted coefficients_Downloadly.ir_en.srt 17.4 KB
27 - ANN in R/003 Building,Compiling and Training_Downloadly.ir_en.srt 16.3 KB
38 - Time Series - Important Concepts/005 Differencing in Python_Downloadly.ir_en.srt 15.7 KB
24 - Introduction - Deep Learning/004 Python - Creating Perceptron model_Downloadly.ir_en.srt 15.7 KB
14 - Simple Decision Trees/013 Building a Regression Tree in R_Downloadly.ir_en.srt 15.5 KB
03 - Setting up R Studio and R crash course/007 Creating Barplots in R_Downloadly.ir_en.srt 15.0 KB
15 - Simple Classification Tree/004 Classification tree in Python _ Training_Downloadly.ir_en.srt 14.5 KB
40 - Time Series - ARIMA model/003 ARIMA model in Python_Downloadly.ir_en.srt 14.3 KB
07 - Linear Regression/010 Multiple Linear Regression in Python_Downloadly.ir_en.srt 14.3 KB
35 - Transfer Learning in R/001 Project - Transfer Learning - VGG16 (Implementation)_Downloadly.ir_en.srt 14.2 KB
06 - Data Preprocessing/010 Outlier Treatment in Python_Downloadly.ir_en.srt 14.1 KB
17 - Ensemble technique 2 - Random Forests/003 Using Grid Search in Python_Downloadly.ir_en.srt 13.7 KB
07 - Linear Regression/017 Subset selection techniques_Downloadly.ir_en.srt 13.7 KB
25 - Neural Networks - Stacking cells to create network/004 Some Important Concepts_Downloadly.ir_en.srt 13.7 KB
27 - ANN in R/006 Building Regression Model with Functional API_Downloadly.ir_en.srt 13.5 KB
02 - Setting up Python and Jupyter Notebook/004 Introduction to Jupyter_Downloadly.ir_en.srt 13.2 KB
06 - Data Preprocessing/008 EDD in R_Downloadly.ir_en.srt 13.2 KB
07 - Linear Regression/005 Simple Linear Regression in Python_Downloadly.ir_en.srt 13.1 KB
26 - ANN in Python/010 Using Functional API for complex architectures_Downloadly.ir_en.srt 13.0 KB
26 - ANN in Python/006 Building the Neural Network using Keras_Downloadly.ir_en.srt 12.9 KB
27 - ANN in R/002 Data Normalization and Test-Train Split_Downloadly.ir_en.srt 12.9 KB
04 - Basics of Statistics/003 Describing data Graphically_Downloadly.ir_en.srt 12.8 KB
25 - Neural Networks - Stacking cells to create network/002 Gradient Descent_Downloadly.ir_en.srt 12.7 KB
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