Machine Learning & Data Science with Python & Kaggle | A-Z

سرفصل های دوره

Data Science & Machine Learning A-Z & Kaggle with Heart Attack Prediction projects and Machine Learning Python projects

1. 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

2. Installations for Python

1. Installing Anaconda Distribution for Windows

2. Installing Anaconda Distribution for MacOs

3. Installing Anaconda Distribution for Linux

4. Overview of Jupyter Notebook and Google Colab

3. 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

4. Supervised Learning with Machine Learning

1. What is Supervised Learning in Machine Learning

2. Quiz.html

5. 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

6. Bias Variance Trade-Off in Machine Learning

1. What is Bias Variance Trade-Off

2. Quiz.html

7. 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

8. K-fold Cross-Validation in Machine Learning A-Z

1. K-Fold Cross-Validation Theory

2. K-Fold Cross-Validation with Python

9. 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

10. Hyperparameter Optimization

1. Hyperparameter Optimization Theory

2. Hyperparameter Optimization with Python

11. 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

12. 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

13. 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

14. Unsupervised Learning with Machine Learning

1. Unsupervised Learning Overview

2. Quiz.html

15. 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

16. 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

17. 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

18. 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

19. 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

20. Competition Section on Kaggle

1. Competitions on Kaggle Lesson 1

2. Competitions on Kaggle Lesson 2

3. Quiz.html

21. Dataset Section on Kaggle

1. Datasets on Kaggle

2. Quiz.html

22. 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

23. Discussion Section on Kaggle

1. What is Discussion on Kaggle

2. Quiz.html

24. Other Most Used Options on Kaggle

1. Courses in Kaggle

2. Ranking Among Users on Kaggle

3. Blog and Documentation Sections

4. Quiz.html

25. 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

26. Introduction to Machine Learning with Real Hearth Attack Prediction Project

1. First Step to the 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

27. First Organization

1. Required Python Libraries

2. Loading the Dataset

3. Initial analysis on the dataset

4. Quiz.html

28. Preparation For Exploratory Data Analysis (EDA)

1. Examining Missing Values

2. Separating variables (Numeric or Categorical)

3. Quiz.html

29. 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

30. 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 Unique Values

7. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2

8. Examining Statistics of Variables

9. Feature Scaling with the Robust Scaler Method

10. Creating a New DataFrame with the Melt() Function

11. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1

12. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2

13. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1

14. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2

15. Relationships between variables (Analysis with Heatmap) Lesson 1

16. Relationships between variables (Analysis with Heatmap) Lesson 2

17. Quiz.html

31. 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

32. 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

33. Conclusion

1. Project Conclusion and Sharing

2. Quiz.html

34. Extra

1. Machine Learning And Data Science with Python And Kaggle A-Z.html

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