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| [FreeCourseLab.com].url |
126б |
| 001 ACF and PACF.mp4 |
41.22Мб |
| 001 Basics of Decision Trees.mp4 |
42.64Мб |
| 001 Basic Terminologies.mp4 |
40.42Мб |
| 001 Boosting.mp4 |
30.58Мб |
| 001 Classification tree.mp4 |
28.20Мб |
| 001 CNN Introduction.mp4 |
51.15Мб |
| 001 CNN model in Python - Preprocessing.mp4 |
40.63Мб |
| 001 CNN on MNIST Fashion Dataset - Model Architecture.mp4 |
7.35Мб |
| 001 Content flow.mp4 |
8.64Мб |
| 001 Data Loading in Python.mp4 |
108.86Мб |
| 001 Ensemble technique 1 - Bagging.mp4 |
28.14Мб |
| 001 Ensemble technique 2 - Random Forests.mp4 |
18.19Мб |
| 001 Gathering Business Knowledge.mp4 |
22.28Мб |
| 001 ILSVRC.mp4 |
20.92Мб |
| 001 Importing Data into R.mp4 |
53.67Мб |
| 001 Installing Keras and Tensorflow.mp4 |
22.78Мб |
| 001 Installing Python and Anaconda.mp4 |
16.27Мб |
| 001 Installing R and R studio.mp4 |
35.71Мб |
| 001 Introduction.mp4 |
29.39Мб |
| 001 Introduction.mp4 |
12.26Мб |
| 001 Introduction to Machine Learning.mp4 |
109.17Мб |
| 001 Introduction to Neural Networks and Course flow.mp4 |
29.07Мб |
| 001 Keras and Tensorflow.mp4 |
14.91Мб |
| 001 Kernel Based Support Vector Machines.mp4 |
40.12Мб |
| 001 Linear Discriminant Analysis.mp4 |
40.95Мб |
| 001 Logistic Regression.mp4 |
32.92Мб |
| 001 Project - Data Augmentation Preprocessing.mp4 |
41.41Мб |
| 001 Project in R - Data Preprocessing.mp4 |
87.76Мб |
| 001 Project - Introduction.mp4 |
49.39Мб |
| 001 Project - Transfer Learning - VGG16 (Implementation).mp4 |
101.57Мб |
| 001 Regression and Classification Models.mp4 |
4.03Мб |
| 001 SARIMA model.mp4 |
39.02Мб |
| 001 Support Vector classifiers.mp4 |
56.16Мб |
| 001 Test-Train Split.mp4 |
39.29Мб |
| 001 Test Train Split in Python.mp4 |
57.41Мб |
| 001 The Data and the Data Dictionary.mp4 |
79.00Мб |
| 001 The final milestone!.mp4 |
11.84Мб |
| 001 The Problem Statement.mp4 |
9.37Мб |
| 001 Three Classifiers and the problem statement.mp4 |
20.33Мб |
| 001 Types of Data.mp4 |
21.76Мб |
| 001 Understanding the results of classification models.mp4 |
41.64Мб |
| 001 White Noise.mp4 |
11.37Мб |
| 002 ARIMA model - Basics.mp4 |
21.36Мб |
| 002 Basic Equations and Ordinary Least Squares (OLS) method.mp4 |
43.37Мб |
| 002 Basics of R and R studio.mp4 |
38.84Мб |
| 002 Building a Machine Learning Model.mp4 |
39.48Мб |
| 002 CNN model in Python - structure and Compile.mp4 |
43.25Мб |
| 002 CNN Project in R - Structure and Compile.mp4 |
46.11Мб |
| 002 Congratulations & About your certificate.html |
2.49Кб |
| 002 Course Resources.html |
1.23Кб |
| 002 Data Exploration.mp4 |
20.50Мб |
| 002 Data for the project.html |
1.10Кб |
| 002 Data Import in Python.mp4 |
22.06Мб |
| 002 Data Normalization and Test-Train Split.mp4 |
111.78Мб |
| 002 Data Preprocessing.mp4 |
67.02Мб |
| 002 Ensemble technique 1 - Bagging in Python.mp4 |
77.30Мб |
| 002 Ensemble technique 2 - Random Forests in Python.mp4 |
46.70Мб |
| 002 Ensemble technique 3a - Boosting in Python.mp4 |
39.87Мб |
| 002 Gradient Descent.mp4 |
60.34Мб |
| 002 Installing Tensorflow and Keras.mp4 |
20.06Мб |
| 002 LDA in Python.mp4 |
11.40Мб |
| 002 LeNET.mp4 |
7.00Мб |
| 002 Limitations of Support Vector Classifiers.mp4 |
10.80Мб |
| 002 Naive (Persistence) model in Python.mp4 |
43.37Мб |
| 002 Perceptron.mp4 |
44.75Мб |
| 002 Project - Data Augmentation Training and Results.mp4 |
53.04Мб |
| 002 Project - Transfer Learning - VGG16 (Performance).mp4 |
64.11Мб |
| 002 Random Walk.mp4 |
21.16Мб |
| 002 SARIMA model in Python.mp4 |
66.23Мб |
| 002 Stride.mp4 |
16.58Мб |
| 002 Summary of the three models.mp4 |
22.21Мб |
| 002 Test-Train Split.mp4 |
50.48Мб |
| 002 Test-Train Split in Python.mp4 |
33.10Мб |
| 002 The Concept of a Hyperplane.mp4 |
29.42Мб |
| 002 The Data set for Classification problem.mp4 |
18.57Мб |
| 002 The Data set for the Regression problem.mp4 |
37.20Мб |
| 002 This is a milestone!.mp4 |
20.66Мб |
| 002 Time Series Forecasting - Use cases.mp4 |
25.91Мб |
| 002 Time Series - Visualization Basics.mp4 |
63.72Мб |
| 002 Training a Simple Logistic Model in Python.mp4 |
47.87Мб |
| 002 Types of Statistics.mp4 |
10.93Мб |
| 002 Understanding a Regression Tree.mp4 |
43.72Мб |
| 002 Why can't we use Linear Regression_.mp4 |
16.93Мб |
| 003 Activation Functions.mp4 |
34.61Мб |
| 003 ARIMA model in Python.mp4 |
74.43Мб |
| 003 Assessing accuracy of predicted coefficients.mp4 |
92.11Мб |
| 003 Auto Regression Model - Basics.mp4 |
16.88Мб |
| 003 Back Propagation.mp4 |
122.20Мб |
| 003 Bagging in R.mp4 |
58.95Мб |
| 003 Building,Compiling and Training.mp4 |
130.73Мб |
| 003 Classification tree in Python _ Preprocessing.mp4 |
45.38Мб |
| 003 CNN model in Python - Training and results.mp4 |
55.15Мб |
| 003 Creating Model Architecture.mp4 |
71.60Мб |
| 003 Dataset for classification.mp4 |
56.19Мб |
| 003 Decomposing Time Series in Python.mp4 |
59.84Мб |
| 003 Describing data Graphically.mp4 |
65.39Мб |
| 003 Forecasting model creation - Steps.mp4 |
10.11Мб |
| 003 Gradient Boosting in R.mp4 |
69.09Мб |
| 003 Importing data for regression model.mp4 |
25.84Мб |
| 003 Importing the dataset into R.mp4 |
13.46Мб |
| 003 Linear Discriminant Analysis in R.mp4 |
74.35Мб |
| 003 Maximum Margin Classifier.mp4 |
22.48Мб |
| 003 More about test-train split.html |
1.43Кб |
| 003 Opening Jupyter Notebook.mp4 |
65.19Мб |
| 003 Packages in R.mp4 |
82.94Мб |
| 003 Padding.mp4 |
31.63Мб |
| 003 Project - Data Preprocessing in Python.mp4 |
71.83Мб |
| 003 Project in R - Training.mp4 |
24.58Мб |
| 003 Stationary time Series.mp4 |
5.58Мб |
| 003 Test-Train Split in R.mp4 |
74.23Мб |
| 003 The Dataset and the Data Dictionary.mp4 |
69.28Мб |
| 003 The stopping criteria for controlling tree growth.mp4 |
13.97Мб |
| 003 Time Series - Visualization in Python.mp4 |
165.19Мб |
| 003 Training a Simple Logistic model in R.mp4 |
25.56Мб |
| 003 Using Grid Search in Python.mp4 |
80.66Мб |
| 003 VGG16NET.mp4 |
10.35Мб |
| 004 ARIMA model with Walk Forward Validation in Python.mp4 |
32.15Мб |
| 004 Assessing Model Accuracy_ RSE and R squared.mp4 |
43.59Мб |
| 004 Auto Regression Model creation in Python.mp4 |
53.49Мб |
| 004 Classification SVM model using Linear Kernel.mp4 |
139.16Мб |
| 004 Classification tree in Python _ Training.mp4 |
82.71Мб |
| 004 Comparison - Pooling vs Without Pooling in Python.mp4 |
57.97Мб |
| 004 Compiling and training.mp4 |
32.20Мб |
| 004 Differencing.mp4 |
32.35Мб |
| 004 EDD in Python.mp4 |
77.62Мб |
| 004 Ensemble technique 3b - AdaBoost in Python.mp4 |
30.53Мб |
| 004 Evaluating and Predicting.mp4 |
99.28Мб |
| 004 Filters and Feature maps.mp4 |
52.71Мб |
| 004 Forecasting model creation - Steps 1 (Goal).mp4 |
34.50Мб |
| 004 GoogLeNet.mp4 |
21.37Мб |
| 004 Importing Data in Python.mp4 |
27.83Мб |
| 004 Inputting data part 1_ Inbuilt datasets of R.mp4 |
40.74Мб |
| 004 Introduction to Jupyter.mp4 |
40.91Мб |
| 004 K-Nearest Neighbors classifier.mp4 |
75.42Мб |
| 004 Limitations of Maximum Margin Classifier.mp4 |
10.60Мб |
| 004 Measures of Centers.mp4 |
38.57Мб |
| 004 Normalization and Test-Train split.mp4 |
44.20Мб |
| 004 Project in R - Model Performance.mp4 |
23.18Мб |
| 004 Project - Training CNN model in Python.mp4 |
65.98Мб |
| 004 Python - Creating Perceptron model.mp4 |
86.55Мб |
| 004 Random Forest in R.mp4 |
30.72Мб |
| 004 Result of Simple Logistic Regression.mp4 |
26.93Мб |
| 004 Some Important Concepts.mp4 |
62.18Мб |
| 004 The Data set for this part.mp4 |
37.26Мб |
| 004 Time Series - Feature Engineering Basics.mp4 |
59.47Мб |
| 004 X-y Split.mp4 |
15.18Мб |
| 005 AdaBoosting in R.mp4 |
88.67Мб |
| 005 ANN with NeuralNets Package.mp4 |
84.42Мб |
| 005 Arithmetic operators in Python_ Python Basics.mp4 |
12.74Мб |
| 005 Auto Regression with Walk Forward validation in Python.mp4 |
49.59Мб |
| 005 Building a classification Tree in R.mp4 |
85.10Мб |
| 005 Channels.mp4 |
67.77Мб |
| 005 Differencing in Python.mp4 |
113.00Мб |
| 005 Different ways to create ANN using Keras.mp4 |
10.81Мб |
| 005 EDD in R.mp4 |
66.52Мб |
| 005 Hyperparameter.mp4 |
45.35Мб |
| 005 Hyperparameter Tuning for Linear Kernel.mp4 |
60.50Мб |
| 005 Importing the Data set into Python.mp4 |
25.84Мб |
| 005 Importing the dataset into R.mp4 |
13.11Мб |
| 005 Inputting data part 2_ Manual data entry.mp4 |
25.52Мб |
| 005 K-Nearest Neighbors in Python_ Part 1.mp4 |
37.23Мб |
| 005 Logistic with multiple predictors.mp4 |
8.53Мб |
| 005 Measures of Dispersion.mp4 |
22.85Мб |
| 005 Model Performance.mp4 |
68.08Мб |
| 005 Project in Python - model results.mp4 |
21.02Мб |
| 005 Project in R - Data Augmentation.mp4 |
56.38Мб |
| 005 Simple Linear Regression in Python.mp4 |
63.43Мб |
| 005 Test-Train Split.mp4 |
24.86Мб |
| 005 Time Series - Basic Notations.mp4 |
62.48Мб |
| 005 Time Series - Feature Engineering in Python.mp4 |
112.69Мб |
| 005 Transfer Learning.mp4 |
29.99Мб |
| 006 Advantages and Disadvantages of Decision Trees.mp4 |
6.86Мб |
| 006 Building Regression Model with Functional API.mp4 |
131.12Мб |
| 006 Building the Neural Network using Keras.mp4 |
79.11Мб |
| 006 Comparison - Pooling vs Without Pooling in R.mp4 |
44.60Мб |
| 006 Ensemble technique 3c - XGBoost in Python.mp4 |
75.00Мб |
| 006 Importing the Data set into R.mp4 |
43.70Мб |
| 006 Inputting data part 3_ Importing from CSV or Text files.mp4 |
60.10Мб |
| 006 K-Nearest Neighbors in Python_ Part 2.mp4 |
42.35Мб |
| 006 Moving Average model -Basics.mp4 |
24.09Мб |
| 006 Outlier treatment in Python.mp4 |
47.32Мб |
| 006 Polynomial Kernel with Hyperparameter Tuning.mp4 |
83.14Мб |
| 006 PoolingLayer.mp4 |
46.87Мб |
| 006 Project in R - Validation Performance.mp4 |
23.69Мб |
| 006 Project - Transfer Learning - VGG16.mp4 |
129.09Мб |
| 006 Simple Linear Regression in R.mp4 |
40.82Мб |
| 006 Standardizing the data.mp4 |
38.41Мб |
| 006 Strings in Python_ Python Basics.mp4 |
64.43Мб |
| 006 Time Series - Upsampling and Downsampling.mp4 |
16.95Мб |
| 006 Training multiple predictor Logistic model in Python.mp4 |
26.25Мб |
| 006 Univariate analysis and EDD.mp4 |
24.18Мб |
| 007 Compiling and Training the Neural Network model.mp4 |
81.63Мб |
| 007 Complex Architectures using Functional API.mp4 |
79.57Мб |
| 007 Creating Barplots in R.mp4 |
96.73Мб |
| 007 EDD in Python.mp4 |
61.80Мб |
| 007 K-Nearest Neighbors in R.mp4 |
64.85Мб |
| 007 Lists, Tuples and Directories_ Python Basics.mp4 |
60.32Мб |
| 007 Missing value treatment in Python.mp4 |
17.92Мб |
| 007 Moving Average model in Python.mp4 |
56.65Мб |
| 007 Multiple Linear Regression.mp4 |
34.31Мб |
| 007 Outlier Treatment in R.mp4 |
25.37Мб |
| 007 Radial Kernel with Hyperparameter Tuning.mp4 |
56.68Мб |
| 007 SVM based Regression Model in Python.mp4 |
67.63Мб |
| 007 Time Series - Upsampling and Downsampling in Python.mp4 |
100.67Мб |
| 007 Training multiple predictor Logistic model in R.mp4 |
15.78Мб |
| 007 XGBoosting in R.mp4 |
161.30Мб |
| 008 Confusion Matrix.mp4 |
21.10Мб |
| 008 Creating Histograms in R.mp4 |
42.02Мб |
| 008 Dummy Variable creation in Python.mp4 |
24.94Мб |
| 008 EDD in R.mp4 |
96.98Мб |
| 008 Evaluating performance and Predicting using Keras.mp4 |
69.91Мб |
| 008 Missing Value Imputation in Python.mp4 |
22.56Мб |
| 008 Saving - Restoring Models and Using Callbacks.mp4 |
216.03Мб |
| 008 SVM based Regression Model in R.mp4 |
106.12Мб |
| 008 The Data set for the Classification problem.mp4 |
18.55Мб |
| 008 The F - statistic.mp4 |
55.98Мб |
| 008 Time Series - Power Transformation.mp4 |
14.85Мб |
| 008 Working with Numpy Library of Python.mp4 |
43.87Мб |
| 009 Building Neural Network for Regression Problem.mp4 |
155.90Мб |
| 009 Classification model - Preprocessing.mp4 |
45.37Мб |
| 009 Creating Confusion Matrix in Python.mp4 |
51.25Мб |
| 009 Dependent- Independent Data split in Python.mp4 |
15.18Мб |
| 009 Interpreting results of Categorical variables.mp4 |
22.50Мб |
| 009 Missing Value imputation in R.mp4 |
19.05Мб |
| 009 Moving Average.mp4 |
38.70Мб |
| 009 Outlier Treatment.mp4 |
24.49Мб |
| 009 Working with Pandas Library of Python.mp4 |
46.88Мб |
| 010 Classification model - Standardizing the data.mp4 |
9.72Мб |
| 010 Evaluating performance of model.mp4 |
35.16Мб |
| 010 Exponential Smoothing.mp4 |
8.38Мб |
| 010 Multiple Linear Regression in Python.mp4 |
69.73Мб |
| 010 Outlier Treatment in Python.mp4 |
70.25Мб |
| 010 Test-Train split in Python.mp4 |
24.87Мб |
| 010 Using Functional API for complex architectures.mp4 |
92.10Мб |
| 010 Variable transformation and Deletion in Python.mp4 |
29.25Мб |
| 010 Working with Seaborn Library of Python.mp4 |
40.36Мб |
| 011 Evaluating model performance in Python.mp4 |
9.01Мб |
| 011 Multiple Linear Regression in R.mp4 |
62.37Мб |
| 011 Outlier Treatment in R.mp4 |
30.74Мб |
| 011 Saving - Restoring Models and Using Callbacks.mp4 |
151.58Мб |
| 011 Splitting Data into Test and Train Set in R.mp4 |
43.97Мб |
| 011 SVM Based classification model.mp4 |
64.12Мб |
| 011 Variable transformation in R.mp4 |
38.02Мб |
| 012 Creating Decision tree in Python.mp4 |
17.87Мб |
| 012 Dummy variable creation in Python.mp4 |
26.37Мб |
| 012 Hyperparameter Tuning.mp4 |
60.63Мб |
| 012 Hyper Parameter Tuning.mp4 |
57.74Мб |
| 012 Missing Value Imputation.mp4 |
24.99Мб |
| 012 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 |
55.69Мб |
| 012 Test-train split.mp4 |
41.88Мб |
| 013 Bias Variance trade-off.mp4 |
25.09Мб |
| 013 Building a Regression Tree in R.mp4 |
103.33Мб |
| 013 Dummy variable creation in R.mp4 |
44.35Мб |
| 013 Missing Value Imputation in Python.mp4 |
23.42Мб |
| 013 Polynomial Kernel with Hyperparameter Tuning.mp4 |
22.92Мб |
| 014 Evaluating model performance in Python.mp4 |
16.44Мб |
| 014 Missing Value imputation in R.mp4 |
26.00Мб |
| 014 Radial Kernel with Hyperparameter Tuning.mp4 |
37.21Мб |
| 014 Test train split in Python.mp4 |
44.88Мб |
| 015 Plotting decision tree in Python.mp4 |
21.47Мб |
| 015 Seasonality in Data.mp4 |
17.01Мб |
| 015 Test-Train Split in R.mp4 |
75.60Мб |
| 016 Bi-variate analysis and Variable transformation.mp4 |
100.39Мб |
| 016 Pruning a tree.mp4 |
18.46Мб |
| 016 Regression models other than OLS.mp4 |
16.54Мб |
| 017 Pruning a tree in Python.mp4 |
73.50Мб |
| 017 Subset selection techniques.mp4 |
79.06Мб |
| 017 Variable transformation and deletion in Python.mp4 |
44.11Мб |
| 018 Pruning a Tree in R.mp4 |
82.09Мб |
| 018 Subset selection in R.mp4 |
63.53Мб |
| 018 Variable transformation in R.mp4 |
55.42Мб |
| 019 Non-usable variables.mp4 |
20.24Мб |
| 019 Shrinkage methods_ Ridge and Lasso.mp4 |
33.34Мб |
| 020 Dummy variable creation_ Handling qualitative data.mp4 |
36.80Мб |
| 020 Ridge regression and Lasso in Python.mp4 |
128.84Мб |
| 021 Dummy variable creation in Python.mp4 |
26.53Мб |
| 021 Ridge regression and Lasso in R.mp4 |
103.43Мб |
| 022 Dummy variable creation in R.mp4 |
43.98Мб |
| 022 Heteroscedasticity.mp4 |
14.49Мб |
| 023 Correlation Analysis.mp4 |
71.59Мб |
| 024 Correlation Analysis in Python.mp4 |
55.30Мб |
| 025 Correlation Matrix in R.mp4 |
83.13Мб |