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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