آموزش Packt Publishing – Step-by-Step Programming with Python and R

توضیحات : آیا میخواهید دو تا از بهترین زبان های برنامه نویسی برای علم داده ها را بیاموزید ؟ این دوره آموزشی شما را در این راه قرار می دهد . این دوره به شما کمک می کند مفاهیم پایه و اساسی این دو زبان قدرتمند را یاد بگیرید تا در آینده بتوانید از آنها در پروژه های خود استفاده نمایید .

نیازمندی ها : دانستن مفاهیم اولیه برنامه نویسی ، این دوره برای کسانی طراحی شده است که می خواهند زبان R و Python را از ابتدا بیاموزند … این دوره فوق العاده را از دست ندهید . سرفصل دوره به صورت کامل در ادامه مطلب موجود است .

در این آموزش خواهید آموخت :

http://tutdl.ir/wp-content/uploads/icons/category.png Beginning Python (4h 20m)

http://tutdl.ir/wp-content/uploads/icons/category.png Mastering Python – Second Edition (5h 21m)

http://tutdl.ir/wp-content/uploads/icons/category.png Introduction to R Programming (3h 46m)

http://tutdl.ir/wp-content/uploads/icons/category.png Mastering R Programming (5h 21m)

منتشر شده در :

http://tutdl.ir/wp-content/uploads/icons/company.png Packt Publishing

 

ارزش مادی آموزش :

dollar_currency_sign32هزار تومان

 

مدرس ویدیو

http://tutdl.ir/wp-content/uploads/icons/authors-icon.pngAnkita Thakur

 

زمان ویدیو

http://tutdl.ir/wp-content/uploads/icons/video.png ۱۸ ساعت و ۴۰ دقیقه

 

حجم ویدیو

http://tutdl.ir/wp-content/uploads/icons/bandwidth.png  ۴٫۱ گیگابایت

 

دانلود آموزش

http://tutdl.ir/wp-content/uploads/icons/Button-Download-icon.png دانلود بخش اول

http://tutdl.ir/wp-content/uploads/icons/Button-Download-icon.png دانلود بخش دوم

http://tutdl.ir/wp-content/uploads/icons/Button-Download-icon.png دانلود بخش سوم

http://tutdl.ir/wp-content/uploads/icons/Button-Download-icon.png دانلود بخش چهارم

 

 

 

Table of Contents

  1. Chapter 1 : Beginning Python
    1. The Course Overview and Installing Python 00:06:15
    2. Setting Up a Programming Environment 00:06:46
    3. Variables 00:05:29
    4. Introduction to Types 00:06:04
    5. Basic Operators 00:05:53
    6. Introduction to Strings 00:06:25
    7. String Functions 00:06:42
    8. Advanced String Manipulation 00:05:23
    9. String Formatting 00:08:18
    10. User Input 00:05:18
    11. Introduction to Lists 00:06:24
    12. List Methods 00:04:51
    13. Advanced List Methods 00:05:41
    14. Built-in List Functions 00:04:25
    15. ۲D Arrays and Array References 00:07:45
    16. List Slicing 00:05:46
    17. Control Flow 00:05:46
    18. Comparison Operators 00:04:44
    19. Else and Elif 00:06:44
    20. and, or, and not 00:06:09
    21. Conditional Examples 00:05:22
    22. Mini Program 00:07:45
    23. For Loop 00:07:25
    24. While Loop 00:06:44
    25. Iterables 00:04:52
    26. Loops and Conditionals 00:05:00
    27. Prime Number Checker 00:06:55
    28. Function Basics 00:05:29
    29. Parameters and Arguments 00:06:57
    30. Return versus Void Functions 00:03:34
    31. Working with Examples 00:08:30
    32. Advanced Examples 00:06:46
    33. Recursion 00:04:26
    34. Recursion Examples 00:09:11
    35. Import, as, and from 00:04:43
    36. Python API and Modules 00:06:48
    37. Creating Modules 00:04:59
    38. Modules and Testing 00:05:28
    39. Installing PIL/Pillow 00:06:29
    40. Basics of Using PIL/Pillow 00:06:25
    41. Picture Manipulations 00:06:30
    42. Custom Picture Manipulation 00:06:20
    43. Wrapping Up 00:03:03
  2. Chapter 2 : Mastering Python – Second Edition
    1. The Course Overview 00:03:25
    2. Python Basic Syntax and Block Structure 00:11:54
    3. Built-in Data Structures and Comprehensions 00:08:55
    4. First-Class Functions and Classes 00:05:50
    5. Extensive Standard Library 00:05:56
    6. New in Python 3.5 00:06:02
    7. Downloading and Installing Python 00:05:17
    8. Using the Command-Line and the Interactive Shell 00:04:01
    9. Installing Packages with pip 00:03:16
    10. Finding Packages in the Python Package Index 00:04:29
    11. Creating an Empty Package 00:05:50
    12. Adding Modules to the Package 00:05:31
    13. Importing One of the Package’s Modules from Another 00:05:26
    14. Adding Static Data Files to the Package 00:02:53
    15. PEP 8 and Writing Readable Code 00:07:51
    16. Using Version Control 00:04:48
    17. Using venv to Create a Stable and Isolated Work Area 00:04:41
    18. Getting the Most Out of docstrings 1: PEP 257 and docutils 00:08:00
    19. Getting the Most Out of docstrings 2: doctest 00:04:04
    20. Making a Package Executable via python -m 00:05:52
    21. Handling Command-Line Arguments with argparse 00:06:22
    22. Interacting with the User 00:04:39
    23. Executing Other Programs with Subprocess 00:09:10
    24. Using Shell Scripts or Batch Files to Run Our Programs 00:03:01
    25. Using concurrent.futures 00:13:53
    26. Using Multiprocessing 00:11:22
    27. Understanding Why This Isn’t Like Parallel Processing 00:08:02
    28. Using the asyncio Event Loop and Coroutine Scheduler 00:06:52
    29. Waiting for Data to Become Available 00:03:30
    30. Synchronizing Multiple Tasks 00:06:18
    31. Communicating Across the Network 00:03:45
    32. Using Function Decorators 00:06:45
    33. Function Annotations 00:07:09
    34. Class Decorators 00:05:53
    35. Metaclasses 00:05:35
    36. Context Managers 00:05:52
    37. Descriptors 00:05:38
    38. Understanding the Principles of Unit Testing 00:05:07
    39. Using the unittest Package 00:07:28
    40. Using unittest.mock 00:06:12
    41. Using unittest’s Test Discovery 00:04:30
    42. Using Nose for Unified Test Discover and Reporting 00:03:42
    43. What Does Reactive Programming Mean? 00:02:50
    44. Building a Simple Reactive Programming Framework 00:07:22
    45. Using the Reactive Extensions for Python (RxPY) 00:10:22
    46. Microservices and the Advantages of Process Isolation 00:04:13
    47. Building a High-Level Microservice with Flask 00:09:59
    48. Building a Low-Level Microservice with nameko 00:06:25
    49. Advantages and Disadvantages of Compiled Code 00:04:42
    50. Accessing a Dynamic Library Using ctypes 00:07:59
    51. Interfacing with C Code Using Cython 00:12:35
  3. Chapter 3 : Introduction to R Programming
    1. The Course Overview 00:04:54
    2. Installing R 00:03:46
    3. Installing RStudio 00:04:36
    4. Installing Packages 00:04:50
    5. Data Types and Data Structures 00:03:05
    6. Vectors 00:05:44
    7. Random Numbers, Rounding, and Binning 00:04:00
    8. Missing Values 00:02:47
    9. The which() Operator 00:03:11
    10. Lists 00:04:35
    11. Set Operations 00:02:09
    12. Sampling and Sorting 00:02:52
    13. Check Conditions 00:02:17
    14. For Loops 00:02:34
    15. Dataframes 00:08:30
    16. Importing and Exporting Data 00:06:30
    17. Matrices and Frequency Tables 00:03:41
    18. Merging Dataframes 00:02:26
    19. Aggregation 00:02:48
    20. Melting and Cross Tabulations with dcast() 00:03:58
    21. Dates 00:05:35
    22. String Manipulation 00:05:14
    23. Functions 00:05:34
    24. Debugging and Error Handling 00:04:30
    25. Fast Loops with apply() 00:04:27
    26. Fast Loops with sapply(), lapply() and vapply() 00:02:00
    27. Creating and Customizing an R Plot 00:07:03
    28. Drawing Plots with 2 Y Axes 00:02:23
    29. Multiplots and Custom Layouts 00:03:08
    30. Creating Basic Graph Types 00:04:47
    31. Univariate Analysis 00:06:16
    32. Normal Distribution, Central Limit Theorem, and Confidence Intervals 00:05:32
    33. Correlation and Covariance 00:03:03
    34. Chi-sq Statistic 00:04:42
    35. ANOVA 00:04:54
    36. Statistical Tests 00:05:14
    37. Project 1 – Data Munging and Summarizing 00:11:31
    38. Project 2 – Visualization with Base Graphics 00:05:42
    39. Project 3 – Statistical Inference 00:03:50
    40. Pipes with Magrittr 00:05:21
    41. The 7 Data Manipulation Verbs 00:05:19
    42. Aggregation and Special Functions 00:03:36
    43. Two Table Verbs 00:02:43
    44. Working With Databases 00:05:30
    45. Understanding Basics, Filter, and Select 00:07:34
    46. Understanding Syntax, Creating and Updating Columns 00:04:06
    47. Aggregating Data, .N, and .I 00:04:21
    48. data.table 00:04:17
    49. Fast Loops with set(), Keys, and Joins 00:09:13
  4. Chapter 4 : Mastering R Programming
    1. The Course Overview 00:07:45
    2. Performing Univariate Analysis 00:05:22
    3. Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA 00:05:43
    4. Detecting and Treating Outlier 00:03:21
    5. Treating Missing Values with `mice` 00:03:59
    6. Building Linear Regressors 00:07:35
    7. Interpreting Regression Results and Interactions Terms 00:05:19
    8. Performing Residual Analysis and Extracting Extreme Observations With Cook’s Distance 00:03:25
    9. Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA 00:04:39
    10. Validating Model Performance on New Data with k-Fold Cross Validation 00:02:29
    11. Building Non-Linear Regressors with Splines and GAMs 00:05:20
    12. Building Logistic Regressors, Evaluation Metrics, and ROC Curve 00:12:38
    13. Understanding the Concept and Building Naive Bayes Classifier 00:09:24
    14. Building k-Nearest Neighbors Classifier 00:07:01
    15. Building Tree Based Models Using RPart, cTree, and C5.0 00:06:33
    16. Building Predictive Models with the caret Package 00:08:11
    17. Selecting Important Features with RFE, varImp, and Boruta 00:05:19
    18. Building Classifiers with Support Vector Machines 00:08:04
    19. Understanding Bagging and Building Random Forest Classifier 00:05:07
    20. Implementing Stochastic Gradient Boosting with GBM 00:05:18
    21. Regularization with Ridge, Lasso, and Elasticnet 00:08:53
    22. Building Classifiers and Regressors with XGBoost 00:10:10
    23. Dimensionality Reduction with Principal Component Analysis 00:05:05
    24. Clustering with k-means and Principal Components 00:03:16
    25. Determining Optimum Number of Clusters 00:05:25
    26. Understanding and Implementing Hierarchical Clustering 00:02:36
    27. Clustering with Affinity Propagation 00:05:25
    28. Building Recommendation Engines 00:09:01
    29. Understanding the Components of a Time Series, and the xts Package 00:05:42
    30. Stationarity, De-Trend, and De-Seasonalize 00:04:07
    31. Understanding the Significance of Lags, ACF, PACF, and CCF 00:03:49
    32. Forecasting with Moving Average and Exponential Smoothing 00:02:25
    33. Forecasting with Double Exponential and Holt Winters 00:03:23
    34. Forecasting with ARIMA Modelling 00:05:26
    35. Scraping Web Pages and Processing Texts 00:09:24
    36. In this video, we’ll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data. 00:09:07
    37. Cosine Similarity and Latent Semantic Analysis 00:07:20
    38. Extracting Topics with Latent Dirichlet Allocation 00:05:07
    39. Sentiment Scoring with tidytext and Syuzhet 00:04:23
    40. Classifying Texts with RTextTools 00:03:57
    41. Building a Basic ggplot2 and Customizing the Aesthetics and Themes 00:07:18
    42. Manipulating Legend, AddingText, and Annotation 00:03:31
    43. Drawing Multiple Plots with Faceting and Changing Layouts 00:03:18
    44. Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots 00:05:25
    45. ggplot2 Extensions and ggplotly 00:03:11
    46. Implementing Best Practices to Speed Up R Code 00:05:47
    47. Implementing Parallel Computing with doParallel and foreach 00:04:22
    48. Writing Readable and Fast R Code with Pipes and DPlyR 00:05:40
    49. Writing Super Fast R Code with Minimal Keystrokes Using Data.Table 00:06:38
    50. Interface C++ in R with RCpp 00:11:09
    51. Understanding the Structure of an R Package 00:05:02
    52. Build, Document, and Host an R Package on GitHub 00:07:10
    53. Performing Important Checks Before Submitting to CRAN 00:04:06
    54. Submitting an R Package to CRAN 00:03:11

 

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دانلود آموزش C# Basics: Learn to Code the Right Way

توضیحات : در این دوره آموزشی مقدمات زبان قدرتمند سی شارپ را بیاموزید . آیا …

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