دسته بندی

در حال حاضر محصولی در سبد خرید شما وجود ندارد.

پنل کاربری

رمز خود را فراموش کرده اید؟ اگر اولین بار است از سایت جدید استفاده میکنید باید پسورد خود را ریست نمایید.

آموزش مطالعه موردی Data Mining در زبان R

دانلود Udemy Case Studies in Data Mining with R

27,900 تومان
بیش از یک محصول به صورت دانلودی میخواهید؟ محصول را به سبد خرید اضافه کنید.
افزودن به سبد خرید
خرید دانلودی فوری

در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

با مشاهده این کورس آموزشی از طریق انجام پروژه های واقعی و به زبانی بسیار ساده مطالب فراوانی را در رابطه با داده یابی یاد می گیرید.

عنوان اصلی : Case Studies in Data Mining with R

این مجموعه آموزش ویدیویی محصول موسسه آموزشی Udemy است که بر روی 4 حلقه دیسک به همراه فایلهای تمرینی ارائه شده و به مدت زمان 21 ساعت و 56 دقیقه در اختیار علاقه مندان قرار می گیرد.

در ادامه با برخی از سرفصل های درسی این مجموعه آموزش آشنا می شویم :


A Brief Introduction to R and RStudio using Scripts :
Course Overview
Introduction to R for Data Mining
Data Structures: Vectors (part 1)
Data Structures: Vectors (part 2)
Factors (part 1)
Factors (part 2)
Generating Sequences
Indexing (aka Subscripting or Subsetting)
Data Structures: Matrices and Arrays (part 1)
Data Structures: Matrices and Arrays (part 2)
Data Structures: Lists
Data Structures: Dataframes (part 1)
Data Structures: Dataframes (part 2)
Creating New Functions

Inputting and Outputting Data and Text :
Using the scan() Function for Input (part 1)
Using the scan() Function for Input (part 2)
Using readline(), cat() and print() Functions
Using readLines() Function and Text Data
Example Program: powers.r
Example Program: quartiles1.r
Example Program: quad2b.r
Reading and Writing Files (part 1)
Reading and Writing Files (part 2)

Introduction to Predicting Algae Blooms :
Predicting Algae Blooms
Data Visualization and Summarization: Histograms
Data Visualization: Boxplot and Identity Plot
Data Visualization: Conditioning Plots
Imputation: Dealing with Unknown or Missing Values
Imputation: Removing Rows with Missing Values
Imputation: Replace Missing Values with Central Measures
Imputation: Replace Missing Values through Correlation
Visualizing other Imputations with Lattice Plots

Obtaining Prediction Models :
Read in Data Files
Creating Prediction Models
Examine Alternative Regression Models
Regression Trees
Strategy for Pruning Trees

Evaluating and Selecting Models :
Alternative Model Evaluation Criteria
Introduction to K-Fold Cross-Validation
Setting up K-Fold Evaluation (part 1)
Setting up K-Fold Evaluation (part 2)
Best Model (part 1)
Best Model (part 2)
Finish Evaluating Models
Predicting from the Models
Comparing the Predictions

Examine the Data in the Fraudulent Transactions Case Study :
Exercise Solution from Evaluating and Selecting Models
Fraudulent Case Study Introduction
Prelude to Exploring the Data
Exploring the Data
Exploring the Data Continued (part 1)
Exploring the Data Continued (part 2)
Exploring the Data Continued (part 3)
Dealing with Missing Data (part 1)
Dealing with Missing Data (part 2)
Dealing with Missing Data (part 3)

Pre-Processing the Data to Apply Methodology :
Review the Data and the Focus of the Fraudulent Transactions Case
Pre-Processing the Data (part 1)
Pre-Processing the Data (part 2)
Pre-Processing the Data (part 3)
Defining Data Mining Tasks
Semi-Supervised Techniques
Precision and Recall
Lift Charts and Precision Recall Curves

Methodology to Find Outliers (Fraudulent Transactions) :
Exercise from Previous Session
Review Precision and Recall
Review Lift Charts and Precision Recall Curves
Cumulative Recall Chart
Creating More Functions for the Experimental Methodology
Experimental Methodology to find Outliers (part 1)
Experimental Methodology to find Outliers (part 2)
Experimental Methodology to find Outliers (part 3)
Experimental Methodology to find Outliers (part 4)
Experimental Methodology to find Outliers (part 5)

The Data Mining Tasks to Find the Fraudulent Transactions :
Review of Fraud Case (part 1)
Review of Fraud Case (part 2)
Review of Fraud Case (part 3)
Baseline Boxplot Rule
Local Outlier Factors
Plotting Everything
Supervised and Unsupervised Approaches
SMOTE and Naive Bayes (part 1)
SMOTE and Naive Bayes (part 2)

Sidebar on Boosting :
Introduction to Boosting (from Rattle course)
Boosting Demo Basics using R
Replicating Adaboost using Rpart (Recursive Partitioning) Package
Replicating Adaboost using Rpart (part 2)
Boosting Extensions and Variants
Boosting Exercise

Introduction to Stock Market Prediction Case Study :
Introduction to Stock Market Case Study and Materials
Case Study Background and Data (part 1)
Case Study Background and Data (part 2)
Accessing the Data (part 1)
Accessing the Data (part 2)
Defining the Prediction Tasks (part 1)
Defining the Prediction Tasks (part 2)
Defining the Prediction Tasks (part 3)
Defining the Prediction Tasks (part 4)
Defining the Prediction Tasks (part 5)

Prediction Tasks and Models :
Prelude to Modeling Stock Market Indices
Decision Trees as Applicable to Case Study Tasks
Decision Trees (part 2)
Decision Trees (part 3)
Decision Trees (part 4)
Random Forests Review
Create Initial Model (part 1)
Create Initial Model (part 2)
The Prediction Tasks
Precision and Recall and Confusion Matrices
Neural Network Prediction Technique (part 1)
Neural Network Prediction Technique (part 2)

Prediction Models and Support Vector Machines (SVMs) :
Review Support Vector Machines (SVMs) using Weather Data (part 1)
Review Support Vector Machines (SVMs) using Weather Data (part 2)
Review Support Vector Machines (SVMs) using Weather Data (part 3)
SVMs Applied to Stock Market Case
Kernel Functions
Multivariate Adaptive Regressive Splines
How Will the Predictions be Used ?
Two Strategies
Writing a Simulated Trader Function (part 1)
Writing a Simulated Trader Function (part 2)
Evaluating our Simulated Trades

Model Evaluation and Selection :
Quick Review of Case Study; Support Vector Machines (SVMs)
Begin Evaluating Models
Evaluating Policy One and Policy Two
Why You Cannot Randomly Resample Records
So What Approach is Recommended ?
Experimental Model Comparisons (part 1)
Experimental Model Comparisons (part 2)
Set Up Ranksystems
Continue Evaluating (part 1)
Continue Evaluating (part 2)
Continue Evaluating (part 3)

Wrap Up Stock Market Case Study :
Prologue to Last Session Wrap-Up
Last Session Wrap-Up (part 1)
Last Session Wrap-Up (part 2)

مشخصات این مجموعه :
زبان آموزش ها انگلیسی روان و ساده
دارای آموزشهای ویدیویی و دسته بندی شده
ارائه شده بر روی 4 حلقه دیسک به همراه فایلهای تمرینی
مدت زمان آموزش 21 ساعت و 56 دقیقه !
محصول موسسه آموزشی Udemy