وب سایت تخصصی شرکت فرین
دسته بندی دوره ها

در حال حاضر محصولی در سبد خرید شما وجود ندارد.

رمز خود را فراموش کرده اید؟
وب سایت تخصصی شرکت فرین
خانه » فروشگاه » آموزش علوم درسی و دانشگاهی » آموزش Data Science » Complete Python for Data Science & Machine Learning from A-Z

Complete Python for Data Science & Machine Learning from A-Z

سرفصل های دوره

Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z


1. Installations
  • 1. Installing Anaconda Distribution for Windows
  • 2. Installing Anaconda Distribution for MacOs
  • 3. Installing Anaconda Distribution for Linux
  • 4. Reviewing The Jupyter Notebook
  • 5. Reviewing The Jupyter Lab

    • 2. First Step to Coding
  • 1. Python Introduction
  • 2. Project Files.html
  • 3. First Step to Coding
  • 4. Using Quotation Marks in Python Coding
  • 5. How Should the Coding Form and Style Be (Pep8)
  • 6. Quiz.html

    • 3. Basic Operations with Python
  • 1. Introduction to Basic Data Structures in Python
  • 2. Performing Assignment to Variables
  • 3. Performing Complex Assignment to Variables
  • 4. Type Conversion
  • 5. Arithmetic Operations in Python
  • 6. Examining the Print Function in Depth
  • 7. Escape Sequence Operations
  • 8. Quiz.html

    • 4. Boolean Data Type in Python Programming Language
  • 1. Boolean Logic Expressions
  • 2. Order Of Operations In Boolean Operators
  • 3. Practice with Python
  • 4. Quiz.html

    • 5. String Data Type in Python Programming Language
  • 1. Examining Strings Specifically
  • 2. Accessing Length Information (Len Method)
  • 3. Search Method In Strings Startswith(), Endswith()
  • 4. Character Change Method In Strings Replace()
  • 5. Spelling Substitution Methods in String
  • 6. Character Clipping Methods in String
  • 7. Indexing and Slicing Character String
  • 8. Complex Indexing and Slicing Operations
  • 9. String Formatting with Arithmetic Operations
  • 10. String Formatting With % Operator
  • 11. String Formatting With String.Format Method
  • 12. String Formatting With f-string Method
  • 13. Quiz.html

    • 6. List Data Structure in Python Programming Language
  • 1. Creation of List
  • 2. Reaching List Elements Indexing and Slicing
  • 3. Adding And Modifying And Deleting Elements of List
  • 4. Adding and Deleting by Methods
  • 5. Adding and Deleting by Index
  • 6. Other List Methods
  • 7. Quiz.html

    • 7. Tuple Data Structure in Python Programming Language
  • 1. Creation of Tuple
  • 2. Reaching Tuple Elements Indexing And Slicing
  • 3. Quiz.html

    • 8. Dictionary Data Structure in Python Programming Language
  • 1. Creation of Dictionary
  • 2. Reaching Dictionary Elements
  • 3. Adding And Changing And Deleting Elements in Dictionary
  • 4. Dictionary Methods
  • 5. Quiz.html

    • 9. Set Data Structure in Python Programming Language
  • 1. Creation of Set
  • 2. Adding And Removing Elements Methods in Sets
  • 3. Difference Operation Methods In Sets
  • 4. Intersection And Union Methods In Sets
  • 5. Asking Questions to Sets with Methods
  • 6. Quiz.html

    • 10. Conditional Expressions in Python Programming Language
  • 1. Comparison Operators
  • 2. Structure of if Statements
  • 3. Structure of if-else Statements
  • 4. Structure of if-elif-else Statements
  • 5. Structure of Nested if-elif-else Statements
  • 6. Coordinated Programming with IF and INPUT
  • 7. Ternary Condition
  • 8. Quiz.html

    • 11. For Loop in Python Programming Language
  • 1. For Loop in Python
  • 2. For Loop in Python(Reinforcing the Topic)
  • 3. Using Conditional Expressions and For Loop Together
  • 4. Continue Command
  • 5. Break Command
  • 6. List Comprehension
  • 7. Quiz.html

    • 12. While Loop in Python Programming Language
  • 1. While Loop in Python
  • 2. While Loops in Python Reinforcing the Topic
  • 3. Quiz.html

    • 13. Functions in Python Programming Language
  • 1. Getting know to the Functions
  • 2. How to Write Function
  • 3. Return Expression in Functions
  • 4. Writing Functions with Multiple Argument
  • 5. Writing Docstring in Functions
  • 6. Using Functions and Conditional Expressions Together
  • 7. Quiz.html

    • 14. Arguments And Parameters in Python Programming Language
  • 1. Arguments and Parameters
  • 2. High Level Operations with Arguments
  • 3. Quiz.html

    • 15. Most Used Functions in Python Programming Language
  • 1. all(), any() Functions
  • 2. map() Function
  • 3. filter() Function
  • 4. zip() Function
  • 5. enumerate() Function
  • 6. max(), min() Functions
  • 7. sum() Function
  • 8. round() Function
  • 9. Lambda Function
  • 10. Quiz.html

    • 16. Class Structure in Python Programming Language
  • 1. Local and Global Variables
  • 2. Features of Class
  • 3. Instantiation of Class
  • 4. Attribute of Instantiation
  • 5. Write Function in the Class
  • 6. Inheritance Structure

    • 17. NumPy Library Introduction
  • 1. Introduction to NumPy Library
  • 2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html
  • 3. The Power of NumPy
  • 4. Quiz.html

    • 18. Creating NumPy Array in Python
  • 1. Creating NumPy Array with The Array() Function
  • 2. Creating NumPy Array with Zeros() Function
  • 3. Creating NumPy Array with Ones() Function
  • 4. Creating NumPy Array with Full() Function
  • 5. Creating NumPy Array with Arange() Function
  • 6. Creating NumPy Array with Eye() Function
  • 7. Creating NumPy Array with Linspace() Function
  • 8. Creating NumPy Array with Random() Function
  • 9. Properties of NumPy Array
  • 10. Quiz.html

    • 19. Functions in the NumPy Library
  • 1. Reshaping a NumPy Array Reshape() Function
  • 2. Identifying the Largest Element of a Numpy Array
  • 3. Detecting Least Element of Numpy Array Min(), Ar
  • 4. Concatenating Numpy Arrays Concatenate() Functio
  • 5. Splitting One-Dimensional Numpy Arrays The Split
  • 6. Splitting Two-Dimensional Numpy Arrays Split(),
  • 7. Sorting Numpy Arrays Sort() Function
  • 8. Quiz.html

    • 20. Indexing, Slicing, and Assigning NumPy Arrays
  • 1. Indexing Numpy Arrays
  • 2. Slicing One-Dimensional Numpy Arrays
  • 3. Slicing Two-Dimensional Numpy Arrays
  • 4. Assigning Value to One-Dimensional Arrays
  • 5. Assigning Value to Two-Dimensional Array
  • 6. Fancy Indexing of One-Dimensional Arrrays
  • 7. Fancy Indexing of Two-Dimensional Arrrays
  • 8. Combining Fancy Index with Normal Indexing
  • 9. Combining Fancy Index with Normal Slicing

    • 21. Operations in Numpy Library
  • 1. Operations with Comparison Operators
  • 2. Arithmetic Operations in Numpy
  • 3. Statistical Operations in Numpy
  • 4. Solving Second-Degree Equations with NumPy

    • 22. Pandas Library Introduction
  • 1. Introduction to Pandas Library
  • 2. Pandas Project Files Link.html

    • 23. Series Structures in the Pandas Library
  • 1. Creating a Pandas Series with a List
  • 2. Creating a Pandas Series with a Dictionary
  • 3. Creating Pandas Series with NumPy Array
  • 4. Object Types in Series
  • 5. Examining the Primary Features of the Pandas Seri
  • 6. Most Applied Methods on Pandas Series
  • 7. Indexing and Slicing Pandas Series

    • 24. DataFrame Structures in Pandas Library
  • 1. Creating Pandas DataFrame with List
  • 2. Creating Pandas DataFrame with NumPy Array
  • 3. Creating Pandas DataFrame with Dictionary
  • 4. Examining the Properties of Pandas DataFrames

    • 25. Element Selection Operations in DataFrame Structures
  • 1. Element Selection Operations in Pandas DataFrames Lesson 1
  • 2. Element Selection Operations in Pandas DataFrames Lesson 2
  • 3. Top Level Element Selection in Pandas DataFramesLesson 1
  • 4. Top Level Element Selection in Pandas DataFramesLesson 2
  • 5. Top Level Element Selection in Pandas DataFramesLesson 3
  • 6. Element Selection with Conditional Operations in

    • 26. Structural Operations on Pandas DataFrame
  • 1. Adding Columns to Pandas Data Frames
  • 2. Removing Rows and Columns from Pandas Data frames
  • 3. Null Values in Pandas Dataframes
  • 4. Dropping Null Values Dropna() Function
  • 5. Filling Null Values Fillna() Function
  • 6. Setting Index in Pandas DataFrames

    • 27. Multi-Indexed DataFrame Structures
  • 1. Multi-Index and Index Hierarchy in Pandas DataFrames
  • 2. Element Selection in Multi-Indexed DataFrames
  • 3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames

    • 28. Structural Concatenation Operations in Pandas DataFrame
  • 1. Concatenating Pandas Dataframes Concat Function
  • 2. Merge Pandas Dataframes Merge() Function Lesson 1
  • 3. Merge Pandas Dataframes Merge() Function Lesson 2
  • 4. Merge Pandas Dataframes Merge() Function Lesson 3
  • 5. Merge Pandas Dataframes Merge() Function Lesson 4
  • 6. Joining Pandas Dataframes Join() Function

    • 29. Functions That Can Be Applied on a DataFrame
  • 1. Loading a Dataset from the Seaborn Library
  • 2. Examining the Data Set 1
  • 3. Aggregation Functions in Pandas DataFrames
  • 4. Examining the Data Set 2
  • 5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes
  • 6. Advanced Aggregation Functions Aggregate() Function
  • 7. Advanced Aggregation Functions Filter() Function
  • 8. Advanced Aggregation Functions Transform() Function
  • 9. Advanced Aggregation Functions Apply() Function

    • 30. Pivot Tables in Pandas Library
  • 1. Examining the Data Set 3
  • 2. Pivot Tables in Pandas Library

    • 31. File Operations in Pandas Library
  • 1. Accessing and Making Files Available
  • 2. Data Entry with Csv and Txt Files
  • 3. Data Entry with Excel Files
  • 4. Outputting as an CSV Extension
  • 5. Outputting as an Excel File

    • 32. Matplotlib
  • 1. What is Matplotlib
  • 2. Using Pyplot
  • 3. Pyplot Pylab - Matplotlib
  • 4. Figure, Subplot and Axes
  • 5. Figure Customization
  • 6. Plot Customization
  • 7. Grid, Spines, Ticks
  • 8. Basic Plots in Matplotlib I
  • 9. Basic Plots in Matplotlib II

    • 33. Seaborn
  • 1. What is Seaborn
  • 2. Controlling Figure Aesthetics in Seaborn
  • 3. Example in Seaborn
  • 4. Color Palettes in Seaborn
  • 5. Basic Plots in Seaborn
  • 6. Multi-Plots in Seaborn
  • 7. Regression Plots and Squarify in Seaborn

    • 34. Geoplotlib
  • 1. What is Geoplotlib
  • 2. Example - 1
  • 3. Example - 2
  • 4. Example - 3

    • 35. Intro to Machine Learning with Python
  • 1. What is Machine Learning
  • 2. Machine Learning Terminology
  • 3. Machine Learning Project Files.html
  • 4. Quiz.html

    • 36. Evaluation Metrics in Machine Learning
  • 1. Classification vs Regression in Machine Learning
  • 2. Machine Learning Model Performance Evaluation Classification Error Metrics
  • 3. Evaluating Performance Regression Error Metrics in Python
  • 4. Machine Learning With Python
  • 5. Quiz.html

    • 37. Supervised Learning with Machine Learning
  • 1. What is Supervised Learning in Machine Learning
  • 2. Quiz.html

    • 38. Linear Regression Algorithm in Machine Learning A-Z
  • 1. Linear Regression Algorithm Theory in Machine Learning A-Z
  • 2. Linear Regression Algorithm With Python Part 1
  • 3. Linear Regression Algorithm With Python Part 2
  • 4. Linear Regression Algorithm With Python Part 3
  • 5. Linear Regression Algorithm With Python Part 4

    • 39. Bias Variance Trade-Off in Machine Learning
  • 1. What is Bias Variance Trade-Off
  • 2. Quiz.html

    • 40. Logistic Regression Algorithm in Machine Learning A-Z
  • 1. What is Logistic Regression Algorithm in Machine Learning
  • 2. Logistic Regression Algorithm with Python Part 1
  • 3. Logistic Regression Algorithm with Python Part 2
  • 4. Logistic Regression Algorithm with Python Part 3
  • 5. Logistic Regression Algorithm with Python Part 4
  • 6. Logistic Regression Algorithm with Python Part 5
  • 7. Quiz.html

    • 41. K-fold Cross-Validation in Machine Learning A-Z
  • 1. K-Fold Cross-Validation Theory
  • 2. K-Fold Cross-Validation with Python

    • 42. K Nearest Neighbors Algorithm in Machine Learning A-Z
  • 1. K Nearest Neighbors Algorithm Theory
  • 2. K Nearest Neighbors Algorithm with Python Part 1
  • 3. K Nearest Neighbors Algorithm with Python Part 2
  • 4. K Nearest Neighbors Algorithm with Python Part 3
  • 5. Quiz.html

    • 43. Hyperparameter Optimization
  • 1. Hyperparameter Optimization Theory
  • 2. Hyperparameter Optimization with Python

    • 44. Decision Tree Algorithm in Machine Learning A-Z
  • 1. Decision Tree Algorithm Theory
  • 2. Decision Tree Algorithm with Python Part 1
  • 3. Decision Tree Algorithm with Python Part 2
  • 4. Decision Tree Algorithm with Python Part 3
  • 5. Decision Tree Algorithm with Python Part 4
  • 6. Decision Tree Algorithm with Python Part 5
  • 7. Quiz.html

    • 45. Random Forest Algorithm in Machine Learning A-Z
  • 1. Random Forest Algorithm Theory
  • 2. Random Forest Algorithm with Pyhon Part 1
  • 3. Random Forest Algorithm with Pyhon Part 2

    • 46. Support Vector Machine Algorithm in Machine Learning A-Z
  • 1. Support Vector Machine Algorithm Theory
  • 2. Support Vector Machine Algorithm with Python Part 1
  • 3. Support Vector Machine Algorithm with Python Part 2
  • 4. Support Vector Machine Algorithm with Python Part 3
  • 5. Support Vector Machine Algorithm with Python Part 4
  • 6. Quiz.html

    • 47. Unsupervised Learning with Machine Learning
  • 1. Unsupervised Learning Overview
  • 2. Quiz.html

    • 48. K Means Clustering Algorithm in Machine Learning A-Z
  • 1. K Means Clustering Algorithm Theory
  • 2. K Means Clustering Algorithm with Python Part 1
  • 3. K Means Clustering Algorithm with Python Part 2
  • 4. K Means Clustering Algorithm with Python Part 3
  • 5. K Means Clustering Algorithm with Python Part 4
  • 6. Quiz.html

    • 49. Hierarchical Clustering Algorithm in machine learning data science
  • 1. Hierarchical Clustering Algorithm Theory
  • 2. Hierarchical Clustering Algorithm with Python Part 1
  • 3. Hierarchical Clustering Algorithm with Python Part 2
  • 4. Quiz.html

    • 50. Principal Component Analysis (PCA) in Machine Learning A-Z
  • 1. Principal Component Analysis (PCA) Theory
  • 2. Principal Component Analysis (PCA) with Python Part 1
  • 3. Principal Component Analysis (PCA) with Python Part 2
  • 4. Principal Component Analysis (PCA) with Python Part 3

    • 51. Recommender System Algorithm in Machine Learning A-Z
  • 1. What is the Recommender System Part 1
  • 2. What is the Recommender System Part 2
  • 3. Quiz.html

    • 52. First Contact with Kaggle
  • 1. What is Kaggle
  • 2. FAQ about Kaggle.html
  • 3. Registering on Kaggle and Member Login Procedures
  • 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html
  • 5. Getting to Know the Kaggle Homepage
  • 6. Quiz.html

    • 53. Competition Section on Kaggle
  • 1. Competitions on Kaggle Lesson 1
  • 2. Competitions on Kaggle Lesson 2
  • 3. Quiz.html

    • 54. Dataset Section on Kaggle
  • 1. Datasets on Kaggle
  • 2. Quiz.html

    • 55. Code Section on Kaggle
  • 1. Examining the Code Section in Kaggle Lesson 1
  • 2. Examining the Code Section in Kaggle Lesson 2
  • 3. Examining the Code Section in Kaggle Lesson 3
  • 4. Quiz.html

    • 56. Discussion Section on Kaggle
  • 1. What is Discussion on Kaggle
  • 2. Quiz.html

    • 57. Other Most Used Options on Kaggle
  • 1. Courses in Kaggle
  • 2. Ranking Among Users on Kaggle
  • 3. Blog and Documentation Sections
  • 4. Quiz.html

    • 58. Details on Kaggle
  • 1. User Page Review on Kaggle
  • 2. Treasure in The Kaggle
  • 3. Publishing Notebooks on Kaggle
  • 4. What Should Be Done to Achieve Success in Kaggle
  • 5. Quiz.html

    • 59. Introduction to Machine Learning with Real Hearth Attack Prediction Project
  • 1. First Step to the Hearth Attack Prediction Project
  • 2. FAQ about Machine Learning, Data Science.html
  • 3. Notebook Design to be Used in the Project
  • 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html
  • 5. Examining the Project Topic
  • 6. Recognizing Variables In Dataset
  • 7. Quiz.html

    • 60. First Organization
  • 1. Required Python Libraries
  • 2. Loading the Statistics Dataset in Data Science
  • 3. Initial analysis on the dataset
  • 4. Quiz.html

    • 61. Preparation For Exploratory Data Analysis (EDA) in Data Science
  • 1. Examining Missing Values
  • 2. Examining Unique Values
  • 3. Separating variables (Numeric or Categorical)
  • 4. Examining Statistics of Variables
  • 5. Quiz.html

    • 62. Exploratory Data Analysis (EDA) - Uni-variate Analysis
  • 1. Numeric Variables (Analysis with Distplot) Lesson 1
  • 2. Numeric Variables (Analysis with Distplot) Lesson 2
  • 3. Categoric Variables (Analysis with Pie Chart) Lesson 1
  • 4. Categoric Variables (Analysis with Pie Chart) Lesson 2
  • 5. Examining the Missing Data According to the Analysis Result
  • 6. Quiz.html

    • 63. Exploratory Data Analysis (EDA) - Bi-variate Analysis
  • 1. Numeric Variables Target Variable (Analysis with FacetGrid) Lesson 1
  • 2. Numeric Variables Target Variable (Analysis with FacetGrid) Lesson 2
  • 3. Categoric Variables Target Variable (Analysis with Count Plot) Lesson 1
  • 4. Categoric Variables Target Variable (Analysis with Count Plot) Lesson 2
  • 5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1
  • 6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2
  • 7. Feature Scaling with the Robust Scaler Method
  • 8. Creating a New DataFrame with the Melt() Function
  • 9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1
  • 10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2
  • 11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1
  • 12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2
  • 13. Relationships between variables (Analysis with Heatmap) Lesson 1
  • 14. Relationships between variables (Analysis with Heatmap) Lesson 2
  • 15. Quiz.html

    • 64. Preparation for Modelling in Machine Learning
  • 1. Dropping Columns with Low Correlation
  • 2. Visualizing Outliers
  • 3. Dealing with Outliers Trtbps Variable Lesson 1
  • 4. Dealing with Outliers Trtbps Variable Lesson 2
  • 5. Dealing with Outliers Thalach Variable
  • 6. Dealing with Outliers Oldpeak Variable
  • 7. Determining Distributions of Numeric Variables
  • 8. Transformation Operations on Unsymmetrical Data
  • 9. Applying One Hot Encoding Method to Categorical Variables
  • 10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms
  • 11. Separating Data into Test and Training Set
  • 12. Quiz.html

    • 65. Modelling for Machine Learning
  • 1. Logistic Regression
  • 2. Cross Validation
  • 3. Roc Curve and Area Under Curve (AUC)
  • 4. Hyperparameter Optimization (with GridSearchCV)
  • 5. Decision Tree Algorithm
  • 6. Support Vector Machine Algorithm
  • 7. Random Forest Algorithm
  • 8. Hyperparameter Optimization (with GridSearchCV)
  • 9. Quiz.html

    • 66. Conclusion
  • 1. Project Conclusion and Sharing
  • 2. Quiz.html

    • 67. Extra
  • 1. Complete Python for Data Science And Machine Learning from A-Z.html
    • 235,000 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.

    ایمیل شما:
    تولید کننده:
    مدرس:
    شناسه: 13989
    حجم: 12505 مگابایت
    مدت زمان: 2592 دقیقه
    تاریخ انتشار: ۲۹ خرداد ۱۴۰۲
    دسته بندی محصول
    Udemy-Training
    Udemy-Training
    مشاهده دوره های Udemy-Training
    دیگر آموزش های این مدرس
    طراحی سایت و خدمات سئو

    235,000 تومان
    افزودن به سبد خرید