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خانه » فروشگاه » آموزش علوم درسی و دانشگاهی » آموزش Data Science » Complete Data Science & Machine Learning A-Z with Python

Complete Data Science & Machine Learning A-Z with Python

سرفصل های دوره

Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle


1. Installations
  • 1. Installing Anaconda Distribution for Windows
  • 2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html
  • 3. Installing Anaconda Distribution for MacOs
  • 4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html
  • 5. Installing Anaconda Distribution for Linux

    • 2. NumPy Library Introduction
  • 1. Introduction to NumPy Library
  • 2. The Power of NumPy
  • 3. Quiz.html

    • 3. 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

    • 4. 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

    • 5. 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

    • 6. 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

    • 7. Pandas Library Introduction
  • 1. Introduction to Pandas Library
  • 2. Pandas Project Files Link.html
  • 3. Quiz.html

    • 8. 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
  • 8. Quiz.html

    • 9. 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
  • 5. Quiz.html

    • 10. 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
  • 7. Quiz.html

    • 11. 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
  • 7. Quiz.html

    • 12. 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
  • 4. Quiz.html

    • 13. 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
  • 7. Quiz.html

    • 14. 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
  • 10. Quiz.html

    • 15. Pivot Tables in Pandas Library
  • 1. Examining the Data Set 3
  • 2. Pivot Tables in Pandas Library
  • 3. Quiz.html

    • 16. 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
  • 6. Quiz.html

    • 17. Code Files And Resources Python data analysis and visualization
  • 1. Data Visualisation - Matplotlib Files.html
  • 2. Data Visualisation - Seaborn Files.html
  • 3. Data Visualisation - Geoplotlib.html

    • 18. Introduction to Data Visualization with Python
  • 1. Introduction to Data Visualization with Python
  • 2. FAQ regarding Data Visualization, Python.html

    • 19. Fundamentals of Python 3
  • 1. Data Types in Python
  • 2. Operators in Python
  • 3. Conditionals in Python
  • 4. Loops in Python
  • 5. Lists, Tuples, Dictionaries and Sets in pyhton
  • 6. Data Type Operators and Methods in Python
  • 7. Modules in Python
  • 8. Functions in Python
  • 9. Exercise - Analyse in Python
  • 10. Exercise - Solution in Python
  • 11. Quiz.html

    • 20. Object Oriented Programming (OOP)
  • 1. Logic of Object Oriented Programming
  • 2. Constructor in Object Oriented Programming (OOP)
  • 3. Methods in Object Oriented Programming (OOP)
  • 4. Inheritance in Object Oriented Programming (OOP)
  • 5. Overriding and Overloading in Object Oriented Programming (OOP)
  • 6. Quiz.html

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

    • 22. 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
  • 8. Quiz.html

    • 23. Geoplotlib
  • 1. What is Geoplotlib
  • 2. Example - 1
  • 3. Example - 2
  • 4. Example - 3
  • 5. Quiz.html

    • 24. First Contact with Machine Learning
  • 1. What is Machine Learning
  • 2. Machine Learning Terminology
  • 3. Machine Learning Project Files.html
  • 4. FAQ regarding Python.html
  • 5. FAQ regarding Machine Learning.html
  • 6. Quiz.html

    • 25. 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

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

    • 27. 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

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

    • 29. 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

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

    • 31. 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

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

    • 33. 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

    • 34. 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

    • 35. 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

    • 36. Unsupervised Learning with Machine Learning
  • 1. Unsupervised Learning Overview

    • 37. 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

    • 38. Hierarchical Clustering Algorithm in machine learning data science
  • 1. Hierarchical Clustering Algorithm Theory
  • 2. Hierarchical Clustering Algorithm with Python Part 2
  • 3. Hierarchical Clustering Algorithm with Python Part 2

    • 39. 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

    • 40. Recommender System Algorithm in Machine Learning A-Z
  • 1. What is the Recommender System Part 1
  • 2. What is the Recommender System Part 2

    • 41. 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

    • 42. Competition Section on Kaggle
  • 1. Competitions on Kaggle Lesson 1
  • 2. Competitions on Kaggle Lesson 2

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

    • 44. 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

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

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

    • 47. 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

    • 48. 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

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

    • 50. 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

    • 51. 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

    • 52. 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

    • 53. 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

    • 54. 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

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

    • 56. Extra
  • 1. Complete Data Science & Machine Learning A-Z with Python.html
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