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[FreeCourseLab.com] Udemy - Machine Learning & Deep Learning in Python & R
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2023-12-9 09:22
2024-12-23 09:47
139
13.15 GB
278
磁力链接
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Udemy
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Machine
Learning
&
Deep
Learning
in
Python
&
R
文件列表
01 Introduction/001 Introduction.mp4
29.39MB
02 Setting up Python and Jupyter Notebook/001 Installing Python and Anaconda.mp4
16.27MB
02 Setting up Python and Jupyter Notebook/002 This is a milestone!.mp4
20.66MB
02 Setting up Python and Jupyter Notebook/003 Opening Jupyter Notebook.mp4
65.19MB
02 Setting up Python and Jupyter Notebook/004 Introduction to Jupyter.mp4
40.91MB
02 Setting up Python and Jupyter Notebook/005 Arithmetic operators in Python_ Python Basics.mp4
12.74MB
02 Setting up Python and Jupyter Notebook/006 Strings in Python_ Python Basics.mp4
64.43MB
02 Setting up Python and Jupyter Notebook/007 Lists, Tuples and Directories_ Python Basics.mp4
60.32MB
02 Setting up Python and Jupyter Notebook/008 Working with Numpy Library of Python.mp4
43.87MB
02 Setting up Python and Jupyter Notebook/009 Working with Pandas Library of Python.mp4
46.88MB
02 Setting up Python and Jupyter Notebook/010 Working with Seaborn Library of Python.mp4
40.36MB
03 Setting up R Studio and R crash course/001 Installing R and R studio.mp4
35.71MB
03 Setting up R Studio and R crash course/002 Basics of R and R studio.mp4
38.84MB
03 Setting up R Studio and R crash course/003 Packages in R.mp4
82.94MB
03 Setting up R Studio and R crash course/004 Inputting data part 1_ Inbuilt datasets of R.mp4
40.74MB
03 Setting up R Studio and R crash course/005 Inputting data part 2_ Manual data entry.mp4
25.52MB
03 Setting up R Studio and R crash course/006 Inputting data part 3_ Importing from CSV or Text files.mp4
60.1MB
03 Setting up R Studio and R crash course/007 Creating Barplots in R.mp4
96.73MB
03 Setting up R Studio and R crash course/008 Creating Histograms in R.mp4
42.02MB
04 Basics of Statistics/001 Types of Data.mp4
21.76MB
04 Basics of Statistics/002 Types of Statistics.mp4
10.93MB
04 Basics of Statistics/003 Describing data Graphically.mp4
65.39MB
04 Basics of Statistics/004 Measures of Centers.mp4
38.57MB
04 Basics of Statistics/005 Measures of Dispersion.mp4
22.85MB
05 Introduction to Machine Learning/001 Introduction to Machine Learning.mp4
109.17MB
05 Introduction to Machine Learning/002 Building a Machine Learning Model.mp4
39.48MB
06 Data Preprocessing/001 Gathering Business Knowledge.mp4
22.28MB
06 Data Preprocessing/002 Data Exploration.mp4
20.5MB
06 Data Preprocessing/003 The Dataset and the Data Dictionary.mp4
69.28MB
06 Data Preprocessing/004 Importing Data in Python.mp4
27.83MB
06 Data Preprocessing/005 Importing the dataset into R.mp4
13.11MB
06 Data Preprocessing/006 Univariate analysis and EDD.mp4
24.18MB
06 Data Preprocessing/007 EDD in Python.mp4
61.8MB
06 Data Preprocessing/008 EDD in R.mp4
96.98MB
06 Data Preprocessing/009 Outlier Treatment.mp4
24.49MB
06 Data Preprocessing/010 Outlier Treatment in Python.mp4
70.25MB
06 Data Preprocessing/011 Outlier Treatment in R.mp4
30.74MB
06 Data Preprocessing/012 Missing Value Imputation.mp4
24.99MB
06 Data Preprocessing/013 Missing Value Imputation in Python.mp4
23.42MB
06 Data Preprocessing/014 Missing Value imputation in R.mp4
26MB
06 Data Preprocessing/015 Seasonality in Data.mp4
17.01MB
06 Data Preprocessing/016 Bi-variate analysis and Variable transformation.mp4
100.39MB
06 Data Preprocessing/017 Variable transformation and deletion in Python.mp4
44.11MB
06 Data Preprocessing/018 Variable transformation in R.mp4
55.42MB
06 Data Preprocessing/019 Non-usable variables.mp4
20.24MB
06 Data Preprocessing/020 Dummy variable creation_ Handling qualitative data.mp4
36.8MB
06 Data Preprocessing/021 Dummy variable creation in Python.mp4
26.53MB
06 Data Preprocessing/022 Dummy variable creation in R.mp4
43.98MB
06 Data Preprocessing/023 Correlation Analysis.mp4
71.59MB
06 Data Preprocessing/024 Correlation Analysis in Python.mp4
55.3MB
06 Data Preprocessing/025 Correlation Matrix in R.mp4
83.13MB
07 Linear Regression/001 The Problem Statement.mp4
9.37MB
07 Linear Regression/002 Basic Equations and Ordinary Least Squares (OLS) method.mp4
43.37MB
07 Linear Regression/003 Assessing accuracy of predicted coefficients.mp4
92.11MB
07 Linear Regression/004 Assessing Model Accuracy_ RSE and R squared.mp4
43.59MB
07 Linear Regression/005 Simple Linear Regression in Python.mp4
63.43MB
07 Linear Regression/006 Simple Linear Regression in R.mp4
40.82MB
07 Linear Regression/007 Multiple Linear Regression.mp4
34.31MB
07 Linear Regression/008 The F - statistic.mp4
55.98MB
07 Linear Regression/009 Interpreting results of Categorical variables.mp4
22.5MB
07 Linear Regression/010 Multiple Linear Regression in Python.mp4
69.73MB
07 Linear Regression/011 Multiple Linear Regression in R.mp4
62.37MB
07 Linear Regression/012 Test-train split.mp4
41.88MB
07 Linear Regression/013 Bias Variance trade-off.mp4
25.09MB
07 Linear Regression/014 Test train split in Python.mp4
44.88MB
07 Linear Regression/015 Test-Train Split in R.mp4
75.6MB
07 Linear Regression/016 Regression models other than OLS.mp4
16.54MB
07 Linear Regression/017 Subset selection techniques.mp4
79.06MB
07 Linear Regression/018 Subset selection in R.mp4
63.53MB
07 Linear Regression/019 Shrinkage methods_ Ridge and Lasso.mp4
33.34MB
07 Linear Regression/020 Ridge regression and Lasso in Python.mp4
128.84MB
07 Linear Regression/021 Ridge regression and Lasso in R.mp4
103.43MB
07 Linear Regression/022 Heteroscedasticity.mp4
14.49MB
08 Classification Models_ Data Preparation/001 The Data and the Data Dictionary.mp4
79MB
08 Classification Models_ Data Preparation/002 Data Import in Python.mp4
22.06MB
08 Classification Models_ Data Preparation/003 Importing the dataset into R.mp4
13.46MB
08 Classification Models_ Data Preparation/004 EDD in Python.mp4
77.62MB
08 Classification Models_ Data Preparation/005 EDD in R.mp4
66.52MB
08 Classification Models_ Data Preparation/006 Outlier treatment in Python.mp4
47.32MB
08 Classification Models_ Data Preparation/007 Outlier Treatment in R.mp4
25.37MB
08 Classification Models_ Data Preparation/008 Missing Value Imputation in Python.mp4
22.56MB
08 Classification Models_ Data Preparation/009 Missing Value imputation in R.mp4
19.05MB
08 Classification Models_ Data Preparation/010 Variable transformation and Deletion in Python.mp4
29.25MB
08 Classification Models_ Data Preparation/011 Variable transformation in R.mp4
38.02MB
08 Classification Models_ Data Preparation/012 Dummy variable creation in Python.mp4
26.37MB
08 Classification Models_ Data Preparation/013 Dummy variable creation in R.mp4
44.35MB
09 The Three classification models/001 Three Classifiers and the problem statement.mp4
20.33MB
09 The Three classification models/002 Why can't we use Linear Regression_.mp4
16.93MB
10 Logistic Regression/001 Logistic Regression.mp4
32.92MB
10 Logistic Regression/002 Training a Simple Logistic Model in Python.mp4
47.87MB
10 Logistic Regression/003 Training a Simple Logistic model in R.mp4
25.56MB
10 Logistic Regression/004 Result of Simple Logistic Regression.mp4
26.93MB
10 Logistic Regression/005 Logistic with multiple predictors.mp4
8.53MB
10 Logistic Regression/006 Training multiple predictor Logistic model in Python.mp4
26.25MB
10 Logistic Regression/007 Training multiple predictor Logistic model in R.mp4
15.78MB
10 Logistic Regression/008 Confusion Matrix.mp4
21.1MB
10 Logistic Regression/009 Creating Confusion Matrix in Python.mp4
51.25MB
10 Logistic Regression/010 Evaluating performance of model.mp4
35.16MB
10 Logistic Regression/011 Evaluating model performance in Python.mp4
9.01MB
10 Logistic Regression/012 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4
55.69MB
11 Linear Discriminant Analysis (LDA)/001 Linear Discriminant Analysis.mp4
40.95MB
11 Linear Discriminant Analysis (LDA)/002 LDA in Python.mp4
11.4MB
11 Linear Discriminant Analysis (LDA)/003 Linear Discriminant Analysis in R.mp4
74.35MB
12 K-Nearest Neighbors classifier/001 Test-Train Split.mp4
39.29MB
12 K-Nearest Neighbors classifier/002 Test-Train Split in Python.mp4
33.1MB
12 K-Nearest Neighbors classifier/003 Test-Train Split in R.mp4
74.23MB
12 K-Nearest Neighbors classifier/004 K-Nearest Neighbors classifier.mp4
75.42MB
12 K-Nearest Neighbors classifier/005 K-Nearest Neighbors in Python_ Part 1.mp4
37.23MB
12 K-Nearest Neighbors classifier/006 K-Nearest Neighbors in Python_ Part 2.mp4
42.35MB
12 K-Nearest Neighbors classifier/007 K-Nearest Neighbors in R.mp4
64.85MB
13 Comparing results from 3 models/001 Understanding the results of classification models.mp4
41.64MB
13 Comparing results from 3 models/002 Summary of the three models.mp4
22.21MB
14 Simple Decision Trees/001 Basics of Decision Trees.mp4
42.64MB
14 Simple Decision Trees/002 Understanding a Regression Tree.mp4
43.72MB
14 Simple Decision Trees/003 The stopping criteria for controlling tree growth.mp4
13.97MB
14 Simple Decision Trees/004 The Data set for this part.mp4
37.26MB
14 Simple Decision Trees/005 Importing the Data set into Python.mp4
25.84MB
14 Simple Decision Trees/006 Importing the Data set into R.mp4
43.7MB
14 Simple Decision Trees/007 Missing value treatment in Python.mp4
17.92MB
14 Simple Decision Trees/008 Dummy Variable creation in Python.mp4
24.94MB
14 Simple Decision Trees/009 Dependent- Independent Data split in Python.mp4
15.18MB
14 Simple Decision Trees/010 Test-Train split in Python.mp4
24.87MB
14 Simple Decision Trees/011 Splitting Data into Test and Train Set in R.mp4
43.97MB
14 Simple Decision Trees/012 Creating Decision tree in Python.mp4
17.87MB
14 Simple Decision Trees/013 Building a Regression Tree in R.mp4
103.33MB
14 Simple Decision Trees/014 Evaluating model performance in Python.mp4
16.44MB
14 Simple Decision Trees/015 Plotting decision tree in Python.mp4
21.47MB
14 Simple Decision Trees/016 Pruning a tree.mp4
18.46MB
14 Simple Decision Trees/017 Pruning a tree in Python.mp4
73.5MB
14 Simple Decision Trees/018 Pruning a Tree in R.mp4
82.09MB
15 Simple Classification Tree/001 Classification tree.mp4
28.2MB
15 Simple Classification Tree/002 The Data set for Classification problem.mp4
18.57MB
15 Simple Classification Tree/003 Classification tree in Python _ Preprocessing.mp4
45.38MB
15 Simple Classification Tree/004 Classification tree in Python _ Training.mp4
82.71MB
15 Simple Classification Tree/005 Building a classification Tree in R.mp4
85.1MB
15 Simple Classification Tree/006 Advantages and Disadvantages of Decision Trees.mp4
6.86MB
16 Ensemble technique 1 - Bagging/001 Ensemble technique 1 - Bagging.mp4
28.14MB
16 Ensemble technique 1 - Bagging/002 Ensemble technique 1 - Bagging in Python.mp4
77.3MB
16 Ensemble technique 1 - Bagging/003 Bagging in R.mp4
58.95MB
17 Ensemble technique 2 - Random Forests/001 Ensemble technique 2 - Random Forests.mp4
18.19MB
17 Ensemble technique 2 - Random Forests/002 Ensemble technique 2 - Random Forests in Python.mp4
46.7MB
17 Ensemble technique 2 - Random Forests/003 Using Grid Search in Python.mp4
80.66MB
17 Ensemble technique 2 - Random Forests/004 Random Forest in R.mp4
30.72MB
18 Ensemble technique 3 - Boosting/001 Boosting.mp4
30.58MB
18 Ensemble technique 3 - Boosting/002 Ensemble technique 3a - Boosting in Python.mp4
39.87MB
18 Ensemble technique 3 - Boosting/003 Gradient Boosting in R.mp4
69.09MB
18 Ensemble technique 3 - Boosting/004 Ensemble technique 3b - AdaBoost in Python.mp4
30.53MB
18 Ensemble technique 3 - Boosting/005 AdaBoosting in R.mp4
88.67MB
18 Ensemble technique 3 - Boosting/006 Ensemble technique 3c - XGBoost in Python.mp4
75MB
18 Ensemble technique 3 - Boosting/007 XGBoosting in R.mp4
161.3MB
19 Maximum Margin Classifier/001 Content flow.mp4
8.64MB
19 Maximum Margin Classifier/002 The Concept of a Hyperplane.mp4
29.42MB
19 Maximum Margin Classifier/003 Maximum Margin Classifier.mp4
22.48MB
19 Maximum Margin Classifier/004 Limitations of Maximum Margin Classifier.mp4
10.6MB
20 Support Vector Classifier/001 Support Vector classifiers.mp4
56.16MB
20 Support Vector Classifier/002 Limitations of Support Vector Classifiers.mp4
10.8MB
21 Support Vector Machines/001 Kernel Based Support Vector Machines.mp4
40.12MB
22 Creating Support Vector Machine Model in Python/001 Regression and Classification Models.mp4
4.03MB
22 Creating Support Vector Machine Model in Python/002 The Data set for the Regression problem.mp4
37.2MB
22 Creating Support Vector Machine Model in Python/003 Importing data for regression model.mp4
25.84MB
22 Creating Support Vector Machine Model in Python/004 X-y Split.mp4
15.18MB
22 Creating Support Vector Machine Model in Python/005 Test-Train Split.mp4
24.86MB
22 Creating Support Vector Machine Model in Python/006 Standardizing the data.mp4
38.41MB
22 Creating Support Vector Machine Model in Python/007 SVM based Regression Model in Python.mp4
67.63MB
22 Creating Support Vector Machine Model in Python/008 The Data set for the Classification problem.mp4
18.55MB
22 Creating Support Vector Machine Model in Python/009 Classification model - Preprocessing.mp4
45.37MB
22 Creating Support Vector Machine Model in Python/010 Classification model - Standardizing the data.mp4
9.72MB
22 Creating Support Vector Machine Model in Python/011 SVM Based classification model.mp4
64.12MB
22 Creating Support Vector Machine Model in Python/012 Hyper Parameter Tuning.mp4
57.74MB
22 Creating Support Vector Machine Model in Python/013 Polynomial Kernel with Hyperparameter Tuning.mp4
22.92MB
22 Creating Support Vector Machine Model in Python/014 Radial Kernel with Hyperparameter Tuning.mp4
37.21MB
23 Creating Support Vector Machine Model in R/001 Importing Data into R.mp4
53.67MB
23 Creating Support Vector Machine Model in R/002 Test-Train Split.mp4
50.48MB
23 Creating Support Vector Machine Model in R/004 Classification SVM model using Linear Kernel.mp4
139.16MB
23 Creating Support Vector Machine Model in R/005 Hyperparameter Tuning for Linear Kernel.mp4
60.5MB
23 Creating Support Vector Machine Model in R/006 Polynomial Kernel with Hyperparameter Tuning.mp4
83.14MB
23 Creating Support Vector Machine Model in R/007 Radial Kernel with Hyperparameter Tuning.mp4
56.68MB
23 Creating Support Vector Machine Model in R/008 SVM based Regression Model in R.mp4
106.12MB
24 Introduction - Deep Learning/001 Introduction to Neural Networks and Course flow.mp4
29.07MB
24 Introduction - Deep Learning/002 Perceptron.mp4
44.75MB
24 Introduction - Deep Learning/003 Activation Functions.mp4
34.61MB
24 Introduction - Deep Learning/004 Python - Creating Perceptron model.mp4
86.55MB
25 Neural Networks - Stacking cells to create network/001 Basic Terminologies.mp4
40.42MB
25 Neural Networks - Stacking cells to create network/002 Gradient Descent.mp4
60.34MB
25 Neural Networks - Stacking cells to create network/003 Back Propagation.mp4
122.2MB
25 Neural Networks - Stacking cells to create network/004 Some Important Concepts.mp4
62.18MB
25 Neural Networks - Stacking cells to create network/005 Hyperparameter.mp4
45.35MB
26 ANN in Python/001 Keras and Tensorflow.mp4
14.91MB
26 ANN in Python/002 Installing Tensorflow and Keras.mp4
20.06MB
26 ANN in Python/003 Dataset for classification.mp4
56.19MB
26 ANN in Python/004 Normalization and Test-Train split.mp4
44.2MB
26 ANN in Python/005 Different ways to create ANN using Keras.mp4
10.81MB
26 ANN in Python/006 Building the Neural Network using Keras.mp4
79.11MB
26 ANN in Python/007 Compiling and Training the Neural Network model.mp4
81.63MB
26 ANN in Python/008 Evaluating performance and Predicting using Keras.mp4
69.91MB
26 ANN in Python/009 Building Neural Network for Regression Problem.mp4
155.9MB
26 ANN in Python/010 Using Functional API for complex architectures.mp4
92.1MB
26 ANN in Python/011 Saving - Restoring Models and Using Callbacks.mp4
151.58MB
26 ANN in Python/012 Hyperparameter Tuning.mp4
60.63MB
27 ANN in R/001 Installing Keras and Tensorflow.mp4
22.78MB
27 ANN in R/002 Data Normalization and Test-Train Split.mp4
111.78MB
27 ANN in R/003 Building,Compiling and Training.mp4
130.73MB
27 ANN in R/004 Evaluating and Predicting.mp4
99.28MB
27 ANN in R/005 ANN with NeuralNets Package.mp4
84.42MB
27 ANN in R/006 Building Regression Model with Functional API.mp4
131.12MB
27 ANN in R/007 Complex Architectures using Functional API.mp4
79.57MB
27 ANN in R/008 Saving - Restoring Models and Using Callbacks.mp4
216.03MB
28 CNN - Basics/001 CNN Introduction.mp4
51.15MB
28 CNN - Basics/002 Stride.mp4
16.58MB
28 CNN - Basics/003 Padding.mp4
31.63MB
28 CNN - Basics/004 Filters and Feature maps.mp4
52.71MB
28 CNN - Basics/005 Channels.mp4
67.77MB
28 CNN - Basics/006 PoolingLayer.mp4
46.87MB
29 Creating CNN model in Python/001 CNN model in Python - Preprocessing.mp4
40.63MB
29 Creating CNN model in Python/002 CNN model in Python - structure and Compile.mp4
43.25MB
29 Creating CNN model in Python/003 CNN model in Python - Training and results.mp4
55.15MB
29 Creating CNN model in Python/004 Comparison - Pooling vs Without Pooling in Python.mp4
57.97MB
30 Creating CNN model in R/001 CNN on MNIST Fashion Dataset - Model Architecture.mp4
7.35MB
30 Creating CNN model in R/002 Data Preprocessing.mp4
67.02MB
30 Creating CNN model in R/003 Creating Model Architecture.mp4
71.6MB
30 Creating CNN model in R/004 Compiling and training.mp4
32.2MB
30 Creating CNN model in R/005 Model Performance.mp4
68.08MB
30 Creating CNN model in R/006 Comparison - Pooling vs Without Pooling in R.mp4
44.6MB
31 Project _ Creating CNN model from scratch in Python/001 Project - Introduction.mp4
49.39MB
31 Project _ Creating CNN model from scratch in Python/003 Project - Data Preprocessing in Python.mp4
71.83MB
31 Project _ Creating CNN model from scratch in Python/004 Project - Training CNN model in Python.mp4
65.98MB
31 Project _ Creating CNN model from scratch in Python/005 Project in Python - model results.mp4
21.02MB
32 Project _ Creating CNN model from scratch/001 Project in R - Data Preprocessing.mp4
87.76MB
32 Project _ Creating CNN model from scratch/002 CNN Project in R - Structure and Compile.mp4
46.11MB
32 Project _ Creating CNN model from scratch/003 Project in R - Training.mp4
24.58MB
32 Project _ Creating CNN model from scratch/004 Project in R - Model Performance.mp4
23.18MB
32 Project _ Creating CNN model from scratch/005 Project in R - Data Augmentation.mp4
56.38MB
32 Project _ Creating CNN model from scratch/006 Project in R - Validation Performance.mp4
23.69MB
33 Project _ Data Augmentation for avoiding overfitting/001 Project - Data Augmentation Preprocessing.mp4
41.41MB
33 Project _ Data Augmentation for avoiding overfitting/002 Project - Data Augmentation Training and Results.mp4
53.04MB
34 Transfer Learning _ Basics/001 ILSVRC.mp4
20.92MB
34 Transfer Learning _ Basics/002 LeNET.mp4
7MB
34 Transfer Learning _ Basics/003 VGG16NET.mp4
10.35MB
34 Transfer Learning _ Basics/004 GoogLeNet.mp4
21.37MB
34 Transfer Learning _ Basics/005 Transfer Learning.mp4
29.99MB
34 Transfer Learning _ Basics/006 Project - Transfer Learning - VGG16.mp4
129.09MB
35 Transfer Learning in R/001 Project - Transfer Learning - VGG16 (Implementation).mp4
101.57MB
35 Transfer Learning in R/002 Project - Transfer Learning - VGG16 (Performance).mp4
64.11MB
36 Time Series Analysis and Forecasting/001 Introduction.mp4
12.26MB
36 Time Series Analysis and Forecasting/002 Time Series Forecasting - Use cases.mp4
25.91MB
36 Time Series Analysis and Forecasting/003 Forecasting model creation - Steps.mp4
10.11MB
36 Time Series Analysis and Forecasting/004 Forecasting model creation - Steps 1 (Goal).mp4
34.5MB
36 Time Series Analysis and Forecasting/005 Time Series - Basic Notations.mp4
62.48MB
37 Time Series - Preprocessing in Python/001 Data Loading in Python.mp4
108.86MB
37 Time Series - Preprocessing in Python/002 Time Series - Visualization Basics.mp4
63.72MB
37 Time Series - Preprocessing in Python/003 Time Series - Visualization in Python.mp4
165.19MB
37 Time Series - Preprocessing in Python/004 Time Series - Feature Engineering Basics.mp4
59.47MB
37 Time Series - Preprocessing in Python/005 Time Series - Feature Engineering in Python.mp4
112.69MB
37 Time Series - Preprocessing in Python/006 Time Series - Upsampling and Downsampling.mp4
16.95MB
37 Time Series - Preprocessing in Python/007 Time Series - Upsampling and Downsampling in Python.mp4
100.67MB
37 Time Series - Preprocessing in Python/008 Time Series - Power Transformation.mp4
14.85MB
37 Time Series - Preprocessing in Python/009 Moving Average.mp4
38.7MB
37 Time Series - Preprocessing in Python/010 Exponential Smoothing.mp4
8.38MB
38 Time Series - Important Concepts/001 White Noise.mp4
11.37MB
38 Time Series - Important Concepts/002 Random Walk.mp4
21.16MB
38 Time Series - Important Concepts/003 Decomposing Time Series in Python.mp4
59.84MB
38 Time Series - Important Concepts/004 Differencing.mp4
32.35MB
38 Time Series - Important Concepts/005 Differencing in Python.mp4
113MB
39 Time Series - Implementation in Python/001 Test Train Split in Python.mp4
57.41MB
39 Time Series - Implementation in Python/002 Naive (Persistence) model in Python.mp4
43.37MB
39 Time Series - Implementation in Python/003 Auto Regression Model - Basics.mp4
16.88MB
39 Time Series - Implementation in Python/004 Auto Regression Model creation in Python.mp4
53.49MB
39 Time Series - Implementation in Python/005 Auto Regression with Walk Forward validation in Python.mp4
49.59MB
39 Time Series - Implementation in Python/006 Moving Average model -Basics.mp4
24.09MB
39 Time Series - Implementation in Python/007 Moving Average model in Python.mp4
56.65MB
40 Time Series - ARIMA model/001 ACF and PACF.mp4
41.22MB
40 Time Series - ARIMA model/002 ARIMA model - Basics.mp4
21.36MB
40 Time Series - ARIMA model/003 ARIMA model in Python.mp4
74.43MB
40 Time Series - ARIMA model/004 ARIMA model with Walk Forward Validation in Python.mp4
32.15MB
41 Time Series - SARIMA model/001 SARIMA model.mp4
39.02MB
41 Time Series - SARIMA model/002 SARIMA model in Python.mp4
66.23MB
41 Time Series - SARIMA model/003 Stationary time Series.mp4
5.58MB
42 Bonus Section/001 The final milestone!.mp4
11.84MB
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