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