[FreeCourseLab.com] Udemy - Machine Learning & Deep Learning in Python & R
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2023-12-9 09:22 | 2024-11-12 07:18 | 125 | 13.15 GB | 278 |
迅雷链接
二维码链接
种子下载(838888不存储任何种子文件)
文件列表
- 01 Introduction/001 Introduction.mp429.39MB
- 02 Setting up Python and Jupyter Notebook/001 Installing Python and Anaconda.mp416.27MB
- 02 Setting up Python and Jupyter Notebook/002 This is a milestone!.mp420.66MB
- 02 Setting up Python and Jupyter Notebook/003 Opening Jupyter Notebook.mp465.19MB
- 02 Setting up Python and Jupyter Notebook/004 Introduction to Jupyter.mp440.91MB
- 02 Setting up Python and Jupyter Notebook/005 Arithmetic operators in Python_ Python Basics.mp412.74MB
- 02 Setting up Python and Jupyter Notebook/006 Strings in Python_ Python Basics.mp464.43MB
- 02 Setting up Python and Jupyter Notebook/007 Lists, Tuples and Directories_ Python Basics.mp460.32MB
- 02 Setting up Python and Jupyter Notebook/008 Working with Numpy Library of Python.mp443.87MB
- 02 Setting up Python and Jupyter Notebook/009 Working with Pandas Library of Python.mp446.88MB
- 02 Setting up Python and Jupyter Notebook/010 Working with Seaborn Library of Python.mp440.36MB
- 03 Setting up R Studio and R crash course/001 Installing R and R studio.mp435.71MB
- 03 Setting up R Studio and R crash course/002 Basics of R and R studio.mp438.84MB
- 03 Setting up R Studio and R crash course/003 Packages in R.mp482.94MB
- 03 Setting up R Studio and R crash course/004 Inputting data part 1_ Inbuilt datasets of R.mp440.74MB
- 03 Setting up R Studio and R crash course/005 Inputting data part 2_ Manual data entry.mp425.52MB
- 03 Setting up R Studio and R crash course/006 Inputting data part 3_ Importing from CSV or Text files.mp460.1MB
- 03 Setting up R Studio and R crash course/007 Creating Barplots in R.mp496.73MB
- 03 Setting up R Studio and R crash course/008 Creating Histograms in R.mp442.02MB
- 04 Basics of Statistics/001 Types of Data.mp421.76MB
- 04 Basics of Statistics/002 Types of Statistics.mp410.93MB
- 04 Basics of Statistics/003 Describing data Graphically.mp465.39MB
- 04 Basics of Statistics/004 Measures of Centers.mp438.57MB
- 04 Basics of Statistics/005 Measures of Dispersion.mp422.85MB
- 05 Introduction to Machine Learning/001 Introduction to Machine Learning.mp4109.17MB
- 05 Introduction to Machine Learning/002 Building a Machine Learning Model.mp439.48MB
- 06 Data Preprocessing/001 Gathering Business Knowledge.mp422.28MB
- 06 Data Preprocessing/002 Data Exploration.mp420.5MB
- 06 Data Preprocessing/003 The Dataset and the Data Dictionary.mp469.28MB
- 06 Data Preprocessing/004 Importing Data in Python.mp427.83MB
- 06 Data Preprocessing/005 Importing the dataset into R.mp413.11MB
- 06 Data Preprocessing/006 Univariate analysis and EDD.mp424.18MB
- 06 Data Preprocessing/007 EDD in Python.mp461.8MB
- 06 Data Preprocessing/008 EDD in R.mp496.98MB
- 06 Data Preprocessing/009 Outlier Treatment.mp424.49MB
- 06 Data Preprocessing/010 Outlier Treatment in Python.mp470.25MB
- 06 Data Preprocessing/011 Outlier Treatment in R.mp430.74MB
- 06 Data Preprocessing/012 Missing Value Imputation.mp424.99MB
- 06 Data Preprocessing/013 Missing Value Imputation in Python.mp423.42MB
- 06 Data Preprocessing/014 Missing Value imputation in R.mp426MB
- 06 Data Preprocessing/015 Seasonality in Data.mp417.01MB
- 06 Data Preprocessing/016 Bi-variate analysis and Variable transformation.mp4100.39MB
- 06 Data Preprocessing/017 Variable transformation and deletion in Python.mp444.11MB
- 06 Data Preprocessing/018 Variable transformation in R.mp455.42MB
- 06 Data Preprocessing/019 Non-usable variables.mp420.24MB
- 06 Data Preprocessing/020 Dummy variable creation_ Handling qualitative data.mp436.8MB
- 06 Data Preprocessing/021 Dummy variable creation in Python.mp426.53MB
- 06 Data Preprocessing/022 Dummy variable creation in R.mp443.98MB
- 06 Data Preprocessing/023 Correlation Analysis.mp471.59MB
- 06 Data Preprocessing/024 Correlation Analysis in Python.mp455.3MB
- 06 Data Preprocessing/025 Correlation Matrix in R.mp483.13MB
- 07 Linear Regression/001 The Problem Statement.mp49.37MB
- 07 Linear Regression/002 Basic Equations and Ordinary Least Squares (OLS) method.mp443.37MB
- 07 Linear Regression/003 Assessing accuracy of predicted coefficients.mp492.11MB
- 07 Linear Regression/004 Assessing Model Accuracy_ RSE and R squared.mp443.59MB
- 07 Linear Regression/005 Simple Linear Regression in Python.mp463.43MB
- 07 Linear Regression/006 Simple Linear Regression in R.mp440.82MB
- 07 Linear Regression/007 Multiple Linear Regression.mp434.31MB
- 07 Linear Regression/008 The F - statistic.mp455.98MB
- 07 Linear Regression/009 Interpreting results of Categorical variables.mp422.5MB
- 07 Linear Regression/010 Multiple Linear Regression in Python.mp469.73MB
- 07 Linear Regression/011 Multiple Linear Regression in R.mp462.37MB
- 07 Linear Regression/012 Test-train split.mp441.88MB
- 07 Linear Regression/013 Bias Variance trade-off.mp425.09MB
- 07 Linear Regression/014 Test train split in Python.mp444.88MB
- 07 Linear Regression/015 Test-Train Split in R.mp475.6MB
- 07 Linear Regression/016 Regression models other than OLS.mp416.54MB
- 07 Linear Regression/017 Subset selection techniques.mp479.06MB
- 07 Linear Regression/018 Subset selection in R.mp463.53MB
- 07 Linear Regression/019 Shrinkage methods_ Ridge and Lasso.mp433.34MB
- 07 Linear Regression/020 Ridge regression and Lasso in Python.mp4128.84MB
- 07 Linear Regression/021 Ridge regression and Lasso in R.mp4103.43MB
- 07 Linear Regression/022 Heteroscedasticity.mp414.49MB
- 08 Classification Models_ Data Preparation/001 The Data and the Data Dictionary.mp479MB
- 08 Classification Models_ Data Preparation/002 Data Import in Python.mp422.06MB
- 08 Classification Models_ Data Preparation/003 Importing the dataset into R.mp413.46MB
- 08 Classification Models_ Data Preparation/004 EDD in Python.mp477.62MB
- 08 Classification Models_ Data Preparation/005 EDD in R.mp466.52MB
- 08 Classification Models_ Data Preparation/006 Outlier treatment in Python.mp447.32MB
- 08 Classification Models_ Data Preparation/007 Outlier Treatment in R.mp425.37MB
- 08 Classification Models_ Data Preparation/008 Missing Value Imputation in Python.mp422.56MB
- 08 Classification Models_ Data Preparation/009 Missing Value imputation in R.mp419.05MB
- 08 Classification Models_ Data Preparation/010 Variable transformation and Deletion in Python.mp429.25MB
- 08 Classification Models_ Data Preparation/011 Variable transformation in R.mp438.02MB
- 08 Classification Models_ Data Preparation/012 Dummy variable creation in Python.mp426.37MB
- 08 Classification Models_ Data Preparation/013 Dummy variable creation in R.mp444.35MB
- 09 The Three classification models/001 Three Classifiers and the problem statement.mp420.33MB
- 09 The Three classification models/002 Why can't we use Linear Regression_.mp416.93MB
- 10 Logistic Regression/001 Logistic Regression.mp432.92MB
- 10 Logistic Regression/002 Training a Simple Logistic Model in Python.mp447.87MB
- 10 Logistic Regression/003 Training a Simple Logistic model in R.mp425.56MB
- 10 Logistic Regression/004 Result of Simple Logistic Regression.mp426.93MB
- 10 Logistic Regression/005 Logistic with multiple predictors.mp48.53MB
- 10 Logistic Regression/006 Training multiple predictor Logistic model in Python.mp426.25MB
- 10 Logistic Regression/007 Training multiple predictor Logistic model in R.mp415.78MB
- 10 Logistic Regression/008 Confusion Matrix.mp421.1MB
- 10 Logistic Regression/009 Creating Confusion Matrix in Python.mp451.25MB
- 10 Logistic Regression/010 Evaluating performance of model.mp435.16MB
- 10 Logistic Regression/011 Evaluating model performance in Python.mp49.01MB
- 10 Logistic Regression/012 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp455.69MB
- 11 Linear Discriminant Analysis (LDA)/001 Linear Discriminant Analysis.mp440.95MB
- 11 Linear Discriminant Analysis (LDA)/002 LDA in Python.mp411.4MB
- 11 Linear Discriminant Analysis (LDA)/003 Linear Discriminant Analysis in R.mp474.35MB
- 12 K-Nearest Neighbors classifier/001 Test-Train Split.mp439.29MB
- 12 K-Nearest Neighbors classifier/002 Test-Train Split in Python.mp433.1MB
- 12 K-Nearest Neighbors classifier/003 Test-Train Split in R.mp474.23MB
- 12 K-Nearest Neighbors classifier/004 K-Nearest Neighbors classifier.mp475.42MB
- 12 K-Nearest Neighbors classifier/005 K-Nearest Neighbors in Python_ Part 1.mp437.23MB
- 12 K-Nearest Neighbors classifier/006 K-Nearest Neighbors in Python_ Part 2.mp442.35MB
- 12 K-Nearest Neighbors classifier/007 K-Nearest Neighbors in R.mp464.85MB
- 13 Comparing results from 3 models/001 Understanding the results of classification models.mp441.64MB
- 13 Comparing results from 3 models/002 Summary of the three models.mp422.21MB
- 14 Simple Decision Trees/001 Basics of Decision Trees.mp442.64MB
- 14 Simple Decision Trees/002 Understanding a Regression Tree.mp443.72MB
- 14 Simple Decision Trees/003 The stopping criteria for controlling tree growth.mp413.97MB
- 14 Simple Decision Trees/004 The Data set for this part.mp437.26MB
- 14 Simple Decision Trees/005 Importing the Data set into Python.mp425.84MB
- 14 Simple Decision Trees/006 Importing the Data set into R.mp443.7MB
- 14 Simple Decision Trees/007 Missing value treatment in Python.mp417.92MB
- 14 Simple Decision Trees/008 Dummy Variable creation in Python.mp424.94MB
- 14 Simple Decision Trees/009 Dependent- Independent Data split in Python.mp415.18MB
- 14 Simple Decision Trees/010 Test-Train split in Python.mp424.87MB
- 14 Simple Decision Trees/011 Splitting Data into Test and Train Set in R.mp443.97MB
- 14 Simple Decision Trees/012 Creating Decision tree in Python.mp417.87MB
- 14 Simple Decision Trees/013 Building a Regression Tree in R.mp4103.33MB
- 14 Simple Decision Trees/014 Evaluating model performance in Python.mp416.44MB
- 14 Simple Decision Trees/015 Plotting decision tree in Python.mp421.47MB
- 14 Simple Decision Trees/016 Pruning a tree.mp418.46MB
- 14 Simple Decision Trees/017 Pruning a tree in Python.mp473.5MB
- 14 Simple Decision Trees/018 Pruning a Tree in R.mp482.09MB
- 15 Simple Classification Tree/001 Classification tree.mp428.2MB
- 15 Simple Classification Tree/002 The Data set for Classification problem.mp418.57MB
- 15 Simple Classification Tree/003 Classification tree in Python _ Preprocessing.mp445.38MB
- 15 Simple Classification Tree/004 Classification tree in Python _ Training.mp482.71MB
- 15 Simple Classification Tree/005 Building a classification Tree in R.mp485.1MB
- 15 Simple Classification Tree/006 Advantages and Disadvantages of Decision Trees.mp46.86MB
- 16 Ensemble technique 1 - Bagging/001 Ensemble technique 1 - Bagging.mp428.14MB
- 16 Ensemble technique 1 - Bagging/002 Ensemble technique 1 - Bagging in Python.mp477.3MB
- 16 Ensemble technique 1 - Bagging/003 Bagging in R.mp458.95MB
- 17 Ensemble technique 2 - Random Forests/001 Ensemble technique 2 - Random Forests.mp418.19MB
- 17 Ensemble technique 2 - Random Forests/002 Ensemble technique 2 - Random Forests in Python.mp446.7MB
- 17 Ensemble technique 2 - Random Forests/003 Using Grid Search in Python.mp480.66MB
- 17 Ensemble technique 2 - Random Forests/004 Random Forest in R.mp430.72MB
- 18 Ensemble technique 3 - Boosting/001 Boosting.mp430.58MB
- 18 Ensemble technique 3 - Boosting/002 Ensemble technique 3a - Boosting in Python.mp439.87MB
- 18 Ensemble technique 3 - Boosting/003 Gradient Boosting in R.mp469.09MB
- 18 Ensemble technique 3 - Boosting/004 Ensemble technique 3b - AdaBoost in Python.mp430.53MB
- 18 Ensemble technique 3 - Boosting/005 AdaBoosting in R.mp488.67MB
- 18 Ensemble technique 3 - Boosting/006 Ensemble technique 3c - XGBoost in Python.mp475MB
- 18 Ensemble technique 3 - Boosting/007 XGBoosting in R.mp4161.3MB
- 19 Maximum Margin Classifier/001 Content flow.mp48.64MB
- 19 Maximum Margin Classifier/002 The Concept of a Hyperplane.mp429.42MB
- 19 Maximum Margin Classifier/003 Maximum Margin Classifier.mp422.48MB
- 19 Maximum Margin Classifier/004 Limitations of Maximum Margin Classifier.mp410.6MB
- 20 Support Vector Classifier/001 Support Vector classifiers.mp456.16MB
- 20 Support Vector Classifier/002 Limitations of Support Vector Classifiers.mp410.8MB
- 21 Support Vector Machines/001 Kernel Based Support Vector Machines.mp440.12MB
- 22 Creating Support Vector Machine Model in Python/001 Regression and Classification Models.mp44.03MB
- 22 Creating Support Vector Machine Model in Python/002 The Data set for the Regression problem.mp437.2MB
- 22 Creating Support Vector Machine Model in Python/003 Importing data for regression model.mp425.84MB
- 22 Creating Support Vector Machine Model in Python/004 X-y Split.mp415.18MB
- 22 Creating Support Vector Machine Model in Python/005 Test-Train Split.mp424.86MB
- 22 Creating Support Vector Machine Model in Python/006 Standardizing the data.mp438.41MB
- 22 Creating Support Vector Machine Model in Python/007 SVM based Regression Model in Python.mp467.63MB
- 22 Creating Support Vector Machine Model in Python/008 The Data set for the Classification problem.mp418.55MB
- 22 Creating Support Vector Machine Model in Python/009 Classification model - Preprocessing.mp445.37MB
- 22 Creating Support Vector Machine Model in Python/010 Classification model - Standardizing the data.mp49.72MB
- 22 Creating Support Vector Machine Model in Python/011 SVM Based classification model.mp464.12MB
- 22 Creating Support Vector Machine Model in Python/012 Hyper Parameter Tuning.mp457.74MB
- 22 Creating Support Vector Machine Model in Python/013 Polynomial Kernel with Hyperparameter Tuning.mp422.92MB
- 22 Creating Support Vector Machine Model in Python/014 Radial Kernel with Hyperparameter Tuning.mp437.21MB
- 23 Creating Support Vector Machine Model in R/001 Importing Data into R.mp453.67MB
- 23 Creating Support Vector Machine Model in R/002 Test-Train Split.mp450.48MB
- 23 Creating Support Vector Machine Model in R/004 Classification SVM model using Linear Kernel.mp4139.16MB
- 23 Creating Support Vector Machine Model in R/005 Hyperparameter Tuning for Linear Kernel.mp460.5MB
- 23 Creating Support Vector Machine Model in R/006 Polynomial Kernel with Hyperparameter Tuning.mp483.14MB
- 23 Creating Support Vector Machine Model in R/007 Radial Kernel with Hyperparameter Tuning.mp456.68MB
- 23 Creating Support Vector Machine Model in R/008 SVM based Regression Model in R.mp4106.12MB
- 24 Introduction - Deep Learning/001 Introduction to Neural Networks and Course flow.mp429.07MB
- 24 Introduction - Deep Learning/002 Perceptron.mp444.75MB
- 24 Introduction - Deep Learning/003 Activation Functions.mp434.61MB
- 24 Introduction - Deep Learning/004 Python - Creating Perceptron model.mp486.55MB
- 25 Neural Networks - Stacking cells to create network/001 Basic Terminologies.mp440.42MB
- 25 Neural Networks - Stacking cells to create network/002 Gradient Descent.mp460.34MB
- 25 Neural Networks - Stacking cells to create network/003 Back Propagation.mp4122.2MB
- 25 Neural Networks - Stacking cells to create network/004 Some Important Concepts.mp462.18MB
- 25 Neural Networks - Stacking cells to create network/005 Hyperparameter.mp445.35MB
- 26 ANN in Python/001 Keras and Tensorflow.mp414.91MB
- 26 ANN in Python/002 Installing Tensorflow and Keras.mp420.06MB
- 26 ANN in Python/003 Dataset for classification.mp456.19MB
- 26 ANN in Python/004 Normalization and Test-Train split.mp444.2MB
- 26 ANN in Python/005 Different ways to create ANN using Keras.mp410.81MB
- 26 ANN in Python/006 Building the Neural Network using Keras.mp479.11MB
- 26 ANN in Python/007 Compiling and Training the Neural Network model.mp481.63MB
- 26 ANN in Python/008 Evaluating performance and Predicting using Keras.mp469.91MB
- 26 ANN in Python/009 Building Neural Network for Regression Problem.mp4155.9MB
- 26 ANN in Python/010 Using Functional API for complex architectures.mp492.1MB
- 26 ANN in Python/011 Saving - Restoring Models and Using Callbacks.mp4151.58MB
- 26 ANN in Python/012 Hyperparameter Tuning.mp460.63MB
- 27 ANN in R/001 Installing Keras and Tensorflow.mp422.78MB
- 27 ANN in R/002 Data Normalization and Test-Train Split.mp4111.78MB
- 27 ANN in R/003 Building,Compiling and Training.mp4130.73MB
- 27 ANN in R/004 Evaluating and Predicting.mp499.28MB
- 27 ANN in R/005 ANN with NeuralNets Package.mp484.42MB
- 27 ANN in R/006 Building Regression Model with Functional API.mp4131.12MB
- 27 ANN in R/007 Complex Architectures using Functional API.mp479.57MB
- 27 ANN in R/008 Saving - Restoring Models and Using Callbacks.mp4216.03MB
- 28 CNN - Basics/001 CNN Introduction.mp451.15MB
- 28 CNN - Basics/002 Stride.mp416.58MB
- 28 CNN - Basics/003 Padding.mp431.63MB
- 28 CNN - Basics/004 Filters and Feature maps.mp452.71MB
- 28 CNN - Basics/005 Channels.mp467.77MB
- 28 CNN - Basics/006 PoolingLayer.mp446.87MB
- 29 Creating CNN model in Python/001 CNN model in Python - Preprocessing.mp440.63MB
- 29 Creating CNN model in Python/002 CNN model in Python - structure and Compile.mp443.25MB
- 29 Creating CNN model in Python/003 CNN model in Python - Training and results.mp455.15MB
- 29 Creating CNN model in Python/004 Comparison - Pooling vs Without Pooling in Python.mp457.97MB
- 30 Creating CNN model in R/001 CNN on MNIST Fashion Dataset - Model Architecture.mp47.35MB
- 30 Creating CNN model in R/002 Data Preprocessing.mp467.02MB
- 30 Creating CNN model in R/003 Creating Model Architecture.mp471.6MB
- 30 Creating CNN model in R/004 Compiling and training.mp432.2MB
- 30 Creating CNN model in R/005 Model Performance.mp468.08MB
- 30 Creating CNN model in R/006 Comparison - Pooling vs Without Pooling in R.mp444.6MB
- 31 Project _ Creating CNN model from scratch in Python/001 Project - Introduction.mp449.39MB
- 31 Project _ Creating CNN model from scratch in Python/003 Project - Data Preprocessing in Python.mp471.83MB
- 31 Project _ Creating CNN model from scratch in Python/004 Project - Training CNN model in Python.mp465.98MB
- 31 Project _ Creating CNN model from scratch in Python/005 Project in Python - model results.mp421.02MB
- 32 Project _ Creating CNN model from scratch/001 Project in R - Data Preprocessing.mp487.76MB
- 32 Project _ Creating CNN model from scratch/002 CNN Project in R - Structure and Compile.mp446.11MB
- 32 Project _ Creating CNN model from scratch/003 Project in R - Training.mp424.58MB
- 32 Project _ Creating CNN model from scratch/004 Project in R - Model Performance.mp423.18MB
- 32 Project _ Creating CNN model from scratch/005 Project in R - Data Augmentation.mp456.38MB
- 32 Project _ Creating CNN model from scratch/006 Project in R - Validation Performance.mp423.69MB
- 33 Project _ Data Augmentation for avoiding overfitting/001 Project - Data Augmentation Preprocessing.mp441.41MB
- 33 Project _ Data Augmentation for avoiding overfitting/002 Project - Data Augmentation Training and Results.mp453.04MB
- 34 Transfer Learning _ Basics/001 ILSVRC.mp420.92MB
- 34 Transfer Learning _ Basics/002 LeNET.mp47MB
- 34 Transfer Learning _ Basics/003 VGG16NET.mp410.35MB
- 34 Transfer Learning _ Basics/004 GoogLeNet.mp421.37MB
- 34 Transfer Learning _ Basics/005 Transfer Learning.mp429.99MB
- 34 Transfer Learning _ Basics/006 Project - Transfer Learning - VGG16.mp4129.09MB
- 35 Transfer Learning in R/001 Project - Transfer Learning - VGG16 (Implementation).mp4101.57MB
- 35 Transfer Learning in R/002 Project - Transfer Learning - VGG16 (Performance).mp464.11MB
- 36 Time Series Analysis and Forecasting/001 Introduction.mp412.26MB
- 36 Time Series Analysis and Forecasting/002 Time Series Forecasting - Use cases.mp425.91MB
- 36 Time Series Analysis and Forecasting/003 Forecasting model creation - Steps.mp410.11MB
- 36 Time Series Analysis and Forecasting/004 Forecasting model creation - Steps 1 (Goal).mp434.5MB
- 36 Time Series Analysis and Forecasting/005 Time Series - Basic Notations.mp462.48MB
- 37 Time Series - Preprocessing in Python/001 Data Loading in Python.mp4108.86MB
- 37 Time Series - Preprocessing in Python/002 Time Series - Visualization Basics.mp463.72MB
- 37 Time Series - Preprocessing in Python/003 Time Series - Visualization in Python.mp4165.19MB
- 37 Time Series - Preprocessing in Python/004 Time Series - Feature Engineering Basics.mp459.47MB
- 37 Time Series - Preprocessing in Python/005 Time Series - Feature Engineering in Python.mp4112.69MB
- 37 Time Series - Preprocessing in Python/006 Time Series - Upsampling and Downsampling.mp416.95MB
- 37 Time Series - Preprocessing in Python/007 Time Series - Upsampling and Downsampling in Python.mp4100.67MB
- 37 Time Series - Preprocessing in Python/008 Time Series - Power Transformation.mp414.85MB
- 37 Time Series - Preprocessing in Python/009 Moving Average.mp438.7MB
- 37 Time Series - Preprocessing in Python/010 Exponential Smoothing.mp48.38MB
- 38 Time Series - Important Concepts/001 White Noise.mp411.37MB
- 38 Time Series - Important Concepts/002 Random Walk.mp421.16MB
- 38 Time Series - Important Concepts/003 Decomposing Time Series in Python.mp459.84MB
- 38 Time Series - Important Concepts/004 Differencing.mp432.35MB
- 38 Time Series - Important Concepts/005 Differencing in Python.mp4113MB
- 39 Time Series - Implementation in Python/001 Test Train Split in Python.mp457.41MB
- 39 Time Series - Implementation in Python/002 Naive (Persistence) model in Python.mp443.37MB
- 39 Time Series - Implementation in Python/003 Auto Regression Model - Basics.mp416.88MB
- 39 Time Series - Implementation in Python/004 Auto Regression Model creation in Python.mp453.49MB
- 39 Time Series - Implementation in Python/005 Auto Regression with Walk Forward validation in Python.mp449.59MB
- 39 Time Series - Implementation in Python/006 Moving Average model -Basics.mp424.09MB
- 39 Time Series - Implementation in Python/007 Moving Average model in Python.mp456.65MB
- 40 Time Series - ARIMA model/001 ACF and PACF.mp441.22MB
- 40 Time Series - ARIMA model/002 ARIMA model - Basics.mp421.36MB
- 40 Time Series - ARIMA model/003 ARIMA model in Python.mp474.43MB
- 40 Time Series - ARIMA model/004 ARIMA model with Walk Forward Validation in Python.mp432.15MB
- 41 Time Series - SARIMA model/001 SARIMA model.mp439.02MB
- 41 Time Series - SARIMA model/002 SARIMA model in Python.mp466.23MB
- 41 Time Series - SARIMA model/003 Stationary time Series.mp45.58MB
- 42 Bonus Section/001 The final milestone!.mp411.84MB
友情提示
不会用的朋友看这里
把磁力链接复制到离线下载，或者bt下载软件里即可下载文件，或者直接复制迅雷链接到迅雷里下载！
亲，你造吗？将网页分享给您的基友，下载的人越多速度越快哦！
违规内容投诉邮箱：[email protected]
概述
838888磁力搜索是一个磁力链接搜索引擎，是学术研究的副产品，用于解决资源过度分散的问题
它通过BitTorrent协议加入DHT网络，实时的自动采集数据，仅存储文件的标题、大小、文件列表、文件标识符（磁力链接）等基础信息
838888磁力搜索不下载任何真实资源，无法判断资源的合法性及真实性，使用838888磁力搜索服务的用户需自行鉴别内容的真伪
838888磁力搜索不上传任何资源，不提供Tracker服务，不提供种子文件的下载，这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统