种子简介

种子名称： [FreeCourseSite.com] Udemy - Complete Data Science & Machine Learning A-Z with Python
文件类型： 视频
文件数目： 242个文件
文件大小： 10.57 GB
收录时间： 2023-7-10 05:15
已经下载： 3次
资源热度： 206
最近下载： 2024-5-21 17:39

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:995e52c707e965713e15f8be5a94177580e2717e&dn=[FreeCourseSite.com] Udemy - Complete Data Science & Machine Learning A-Z with Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[FreeCourseSite.com] Udemy - Complete Data Science & Machine Learning A-Z with Python.torrent

1. Installations/1. Installing Anaconda Distribution for Windows.mp4118.32MB

1. Installations/3. Installing Anaconda Distribution for MacOs.mp446.31MB

1. Installations/5. Installing Anaconda Distribution for Linux.mp4114.75MB

10. Element Selection Operations in DataFrame Structures/1. Element Selection Operations in Pandas DataFrames Lesson 1.mp429.9MB

10. Element Selection Operations in DataFrame Structures/2. Element Selection Operations in Pandas DataFrames Lesson 2.mp431.84MB

10. Element Selection Operations in DataFrame Structures/3. Top Level Element Selection in Pandas DataFramesLesson 1.mp438.29MB

10. Element Selection Operations in DataFrame Structures/4. Top Level Element Selection in Pandas DataFramesLesson 2.mp431.41MB

10. Element Selection Operations in DataFrame Structures/5. Top Level Element Selection in Pandas DataFramesLesson 3.mp422.11MB

10. Element Selection Operations in DataFrame Structures/6. Element Selection with Conditional Operations in.mp446.37MB

11. Structural Operations on Pandas DataFrame/1. Adding Columns to Pandas Data Frames.mp433.58MB

11. Structural Operations on Pandas DataFrame/2. Removing Rows and Columns from Pandas Data frames.mp415.56MB

11. Structural Operations on Pandas DataFrame/3. Null Values in Pandas Dataframes.mp466.96MB

11. Structural Operations on Pandas DataFrame/4. Dropping Null Values Dropna() Function.mp434.54MB

11. Structural Operations on Pandas DataFrame/5. Filling Null Values Fillna() Function.mp451.62MB

11. Structural Operations on Pandas DataFrame/6. Setting Index in Pandas DataFrames.mp439.7MB

12. Multi-Indexed DataFrame Structures/1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp442.66MB

12. Multi-Indexed DataFrame Structures/2. Element Selection in Multi-Indexed DataFrames.mp424.58MB

12. Multi-Indexed DataFrame Structures/3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp431.25MB

13. Structural Concatenation Operations in Pandas DataFrame/1. Concatenating Pandas Dataframes Concat Function.mp463.84MB

13. Structural Concatenation Operations in Pandas DataFrame/2. Merge Pandas Dataframes Merge() Function Lesson 1.mp457.29MB

13. Structural Concatenation Operations in Pandas DataFrame/3. Merge Pandas Dataframes Merge() Function Lesson 2.mp430.55MB

13. Structural Concatenation Operations in Pandas DataFrame/4. Merge Pandas Dataframes Merge() Function Lesson 3.mp460.17MB

13. Structural Concatenation Operations in Pandas DataFrame/5. Merge Pandas Dataframes Merge() Function Lesson 4.mp440.68MB

13. Structural Concatenation Operations in Pandas DataFrame/6. Joining Pandas Dataframes Join() Function.mp456.05MB

14. Functions That Can Be Applied on a DataFrame/1. Loading a Dataset from the Seaborn Library.mp437.72MB

14. Functions That Can Be Applied on a DataFrame/2. Examining the Data Set 1.mp442.9MB

14. Functions That Can Be Applied on a DataFrame/3. Aggregation Functions in Pandas DataFrames.mp490.69MB

14. Functions That Can Be Applied on a DataFrame/4. Examining the Data Set 2.mp446.58MB

14. Functions That Can Be Applied on a DataFrame/5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp488.12MB

14. Functions That Can Be Applied on a DataFrame/6. Advanced Aggregation Functions Aggregate() Function.mp429.22MB

14. Functions That Can Be Applied on a DataFrame/7. Advanced Aggregation Functions Filter() Function.mp424.45MB

14. Functions That Can Be Applied on a DataFrame/8. Advanced Aggregation Functions Transform() Function.mp447.09MB

14. Functions That Can Be Applied on a DataFrame/9. Advanced Aggregation Functions Apply() Function.mp441.42MB

15. Pivot Tables in Pandas Library/1. Examining the Data Set 3.mp439.11MB

15. Pivot Tables in Pandas Library/2. Pivot Tables in Pandas Library.mp454.23MB

16. File Operations in Pandas Library/1. Accessing and Making Files Available.mp434.61MB

16. File Operations in Pandas Library/2. Data Entry with Csv and Txt Files.mp464.34MB

16. File Operations in Pandas Library/3. Data Entry with Excel Files.mp421.84MB

16. File Operations in Pandas Library/4. Outputting as an CSV Extension.mp435.7MB

16. File Operations in Pandas Library/5. Outputting as an Excel File.mp419.74MB

18. Introduction to Data Visualization with Python/1. Introduction to Data Visualization with Python.mp412.85MB

19. Fundamentals of Python 3/1. Data Types in Python.mp447.07MB

19. Fundamentals of Python 3/10. Exercise - Solution in Python.mp451.89MB

19. Fundamentals of Python 3/2. Operators in Python.mp435.71MB

19. Fundamentals of Python 3/3. Conditionals in Python.mp441.23MB

19. Fundamentals of Python 3/4. Loops in Python.mp458.81MB

19. Fundamentals of Python 3/5. Lists, Tuples, Dictionaries and Sets in pyhton.mp475.33MB

19. Fundamentals of Python 3/6. Data Type Operators and Methods in Python.mp443.86MB

19. Fundamentals of Python 3/7. Modules in Python.mp423.95MB

19. Fundamentals of Python 3/8. Functions in Python.mp428.93MB

19. Fundamentals of Python 3/9. Exercise - Analyse in Python.mp48.46MB

2. NumPy Library Introduction/1. Introduction to NumPy Library.mp445.27MB

2. NumPy Library Introduction/2. The Power of NumPy.mp459.87MB

20. Object Oriented Programming (OOP)/1. Logic of Object Oriented Programming.mp417.38MB

20. Object Oriented Programming (OOP)/2. Constructor in Object Oriented Programming (OOP).mp435.84MB

20. Object Oriented Programming (OOP)/3. Methods in Object Oriented Programming (OOP).mp425.1MB

20. Object Oriented Programming (OOP)/4. Inheritance in Object Oriented Programming (OOP).mp434.58MB

20. Object Oriented Programming (OOP)/5. Overriding and Overloading in Object Oriented Programming (OOP).mp462.7MB

21. Matplotlib/1. What is Matplotlib.mp419.06MB

21. Matplotlib/2. Using Pyplot.mp428.22MB

21. Matplotlib/3. Pyplot – Pylab - Matplotlib.mp428.37MB

21. Matplotlib/4. Figure, Subplot and Axex.mp469.89MB

21. Matplotlib/5. Figure Customization.mp463.29MB

21. Matplotlib/6. Plot Customization.mp427.38MB

21. Matplotlib/7. Grid, Spines, Ticks.mp423.89MB

21. Matplotlib/8. Basic Plots in Matplotlib I.mp4111.17MB

21. Matplotlib/9. Basic Plots in Matplotlib II.mp454.82MB

22. Seaborn/1. What is Seaborn.mp413.59MB

22. Seaborn/2. Controlling Figure Aesthetics in Seaborn.mp441.82MB

22. Seaborn/3. Example in Seaborn.mp454.9MB

22. Seaborn/4. Color Palettes in Seaborn.mp448.32MB

22. Seaborn/5. Basic Plots in Seaborn.mp498.84MB

22. Seaborn/6. Multi-Plots in Seaborn.mp442.98MB

22. Seaborn/7. Regression Plots and Squarify in Seaborn.mp460.1MB

23. Geoplotlib/1. What is Geoplotlib.mp434.18MB

23. Geoplotlib/2. Example - 1.mp438.85MB

23. Geoplotlib/3. Example - 2.mp481.14MB

23. Geoplotlib/4. Example - 3.mp451.28MB

24. First Contact with Machine Learning/1. What is Machine Learning.mp427.58MB

24. First Contact with Machine Learning/2. Machine Learning Terminology.mp414.03MB

25. Evaluation Metrics in Machine Learning/1. Classification vs Regression in Machine Learning.mp419.89MB

25. Evaluation Metrics in Machine Learning/2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4100.26MB

25. Evaluation Metrics in Machine Learning/3. Evaluating Performance Regression Error Metrics in Python.mp445.7MB

25. Evaluation Metrics in Machine Learning/4. Machine Learning With Python.mp492.24MB

26. Supervised Learning with Machine Learning/1. What is Supervised Learning in Machine Learning.mp431.69MB

27. Linear Regression Algorithm in Machine Learning A-Z/1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp434.06MB

27. Linear Regression Algorithm in Machine Learning A-Z/2. Linear Regression Algorithm With Python Part 1.mp476.17MB

27. Linear Regression Algorithm in Machine Learning A-Z/3. Linear Regression Algorithm With Python Part 2.mp4106.94MB

27. Linear Regression Algorithm in Machine Learning A-Z/4. Linear Regression Algorithm With Python Part 3.mp470.28MB

27. Linear Regression Algorithm in Machine Learning A-Z/5. Linear Regression Algorithm With Python Part 4.mp490MB

28. Bias Variance Trade-Off in Machine Learning/1. What is Bias Variance Trade-Off.mp455.03MB

29. Logistic Regression Algorithm in Machine Learning A-Z/1. What is Logistic Regression Algorithm in Machine Learning.mp427.84MB

29. Logistic Regression Algorithm in Machine Learning A-Z/2. Logistic Regression Algorithm with Python Part 1.mp472.22MB

29. Logistic Regression Algorithm in Machine Learning A-Z/3. Logistic Regression Algorithm with Python Part 2.mp481.46MB

29. Logistic Regression Algorithm in Machine Learning A-Z/4. Logistic Regression Algorithm with Python Part 3.mp447.35MB

29. Logistic Regression Algorithm in Machine Learning A-Z/5. Logistic Regression Algorithm with Python Part 4.mp447.17MB

29. Logistic Regression Algorithm in Machine Learning A-Z/6. Logistic Regression Algorithm with Python Part 5.mp439.35MB

3. Creating NumPy Array in Python/1. Creating NumPy Array with The Array() Function.mp429.5MB

3. Creating NumPy Array in Python/2. Creating NumPy Array with Zeros() Function.mp424.06MB

3. Creating NumPy Array in Python/3. Creating NumPy Array with Ones() Function.mp415.88MB

3. Creating NumPy Array in Python/4. Creating NumPy Array with Full() Function.mp411.18MB

3. Creating NumPy Array in Python/5. Creating NumPy Array with Arange() Function.mp412.1MB

3. Creating NumPy Array in Python/6. Creating NumPy Array with Eye() Function.mp412.55MB

3. Creating NumPy Array in Python/7. Creating NumPy Array with Linspace() Function.mp47.34MB

3. Creating NumPy Array in Python/8. Creating NumPy Array with Random() Function.mp443.3MB

3. Creating NumPy Array in Python/9. Properties of NumPy Array.mp421.98MB

30. K-fold Cross-Validation in Machine Learning A-Z/1. K-Fold Cross-Validation Theory.mp417.45MB

30. K-fold Cross-Validation in Machine Learning A-Z/2. K-Fold Cross-Validation with Python.mp434.67MB

31. K Nearest Neighbors Algorithm in Machine Learning A-Z/1. K Nearest Neighbors Algorithm Theory.mp428.66MB

31. K Nearest Neighbors Algorithm in Machine Learning A-Z/2. K Nearest Neighbors Algorithm with Python Part 1.mp435.03MB

31. K Nearest Neighbors Algorithm in Machine Learning A-Z/3. K Nearest Neighbors Algorithm with Python Part 2.mp459.37MB

31. K Nearest Neighbors Algorithm in Machine Learning A-Z/4. K Nearest Neighbors Algorithm with Python Part 3.mp431.4MB

32. Hyperparameter Optimization/1. Hyperparameter Optimization Theory.mp433.14MB

32. Hyperparameter Optimization/2. Hyperparameter Optimization with Python.mp447.47MB

33. Decision Tree Algorithm in Machine Learning A-Z/1. Decision Tree Algorithm Theory.mp435.75MB

33. Decision Tree Algorithm in Machine Learning A-Z/2. Decision Tree Algorithm with Python Part 1.mp431.53MB

33. Decision Tree Algorithm in Machine Learning A-Z/3. Decision Tree Algorithm with Python Part 2.mp448.92MB

33. Decision Tree Algorithm in Machine Learning A-Z/4. Decision Tree Algorithm with Python Part 3.mp414.72MB

33. Decision Tree Algorithm in Machine Learning A-Z/5. Decision Tree Algorithm with Python Part 4.mp442.49MB

33. Decision Tree Algorithm in Machine Learning A-Z/6. Decision Tree Algorithm with Python Part 5.mp432.67MB

34. Random Forest Algorithm in Machine Learning A-Z/1. Random Forest Algorithm Theory.mp422.89MB

34. Random Forest Algorithm in Machine Learning A-Z/2. Random Forest Algorithm with Pyhon Part 1.mp438.61MB

34. Random Forest Algorithm in Machine Learning A-Z/3. Random Forest Algorithm with Pyhon Part 2.mp438.72MB

35. Support Vector Machine Algorithm in Machine Learning A-Z/1. Support Vector Machine Algorithm Theory.mp421.84MB

35. Support Vector Machine Algorithm in Machine Learning A-Z/2. Support Vector Machine Algorithm with Python Part 1.mp435.59MB

35. Support Vector Machine Algorithm in Machine Learning A-Z/3. Support Vector Machine Algorithm with Python Part 2.mp441.72MB

35. Support Vector Machine Algorithm in Machine Learning A-Z/4. Support Vector Machine Algorithm with Python Part 3.mp434.77MB

35. Support Vector Machine Algorithm in Machine Learning A-Z/5. Support Vector Machine Algorithm with Python Part 4.mp437.55MB

36. Unsupervised Learning with Machine Learning/1. Unsupervised Learning Overview.mp416.91MB

37. K Means Clustering Algorithm in Machine Learning A-Z/1. K Means Clustering Algorithm Theory.mp417.13MB

37. K Means Clustering Algorithm in Machine Learning A-Z/2. K Means Clustering Algorithm with Python Part 1.mp429.96MB

37. K Means Clustering Algorithm in Machine Learning A-Z/3. K Means Clustering Algorithm with Python Part 2.mp429.64MB

37. K Means Clustering Algorithm in Machine Learning A-Z/4. K Means Clustering Algorithm with Python Part 3.mp427.75MB

37. K Means Clustering Algorithm in Machine Learning A-Z/5. K Means Clustering Algorithm with Python Part 4.mp429.03MB

38. Hierarchical Clustering Algorithm in machine learning data science/1. Hierarchical Clustering Algorithm Theory.mp428.55MB

38. Hierarchical Clustering Algorithm in machine learning data science/2. Hierarchical Clustering Algorithm with Python Part 2.mp435.52MB

38. Hierarchical Clustering Algorithm in machine learning data science/3. Hierarchical Clustering Algorithm with Python Part 2.mp428.89MB

39. Principal Component Analysis (PCA) in Machine Learning A-Z/1. Principal Component Analysis (PCA) Theory.mp437.96MB

39. Principal Component Analysis (PCA) in Machine Learning A-Z/2. Principal Component Analysis (PCA) with Python Part 1.mp426.02MB

39. Principal Component Analysis (PCA) in Machine Learning A-Z/3. Principal Component Analysis (PCA) with Python Part 2.mp48.42MB

39. Principal Component Analysis (PCA) in Machine Learning A-Z/4. Principal Component Analysis (PCA) with Python Part 3.mp437.25MB

4. Functions in the NumPy Library/1. Reshaping a NumPy Array Reshape() Function.mp426.16MB

4. Functions in the NumPy Library/2. Identifying the Largest Element of a Numpy Array.mp415.12MB

4. Functions in the NumPy Library/3. Detecting Least Element of Numpy Array Min(), Ar.mp410.17MB

4. Functions in the NumPy Library/4. Concatenating Numpy Arrays Concatenate() Functio.mp438.36MB

4. Functions in the NumPy Library/5. Splitting One-Dimensional Numpy Arrays The Split.mp420.9MB

4. Functions in the NumPy Library/6. Splitting Two-Dimensional Numpy Arrays Split(),.mp435.73MB

4. Functions in the NumPy Library/7. Sorting Numpy Arrays Sort() Function.mp417.02MB

40. Recommender System Algorithm in Machine Learning A-Z/1. What is the Recommender System Part 1.mp423.04MB

40. Recommender System Algorithm in Machine Learning A-Z/2. What is the Recommender System Part 2.mp417.96MB

41. First Contact with Kaggle/1. What is Kaggle.mp4129.67MB

41. First Contact with Kaggle/3. Registering on Kaggle and Member Login Procedures.mp443.48MB

41. First Contact with Kaggle/5. Getting to Know the Kaggle Homepage.mp4122.93MB

42. Competition Section on Kaggle/1. Competitions on Kaggle Lesson 1.mp4188.17MB

42. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4191.68MB

43. Dataset Section on Kaggle/1. Datasets on Kaggle.mp4133.23MB

44. Code Section on Kaggle/1. Examining the Code Section in Kaggle Lesson 1.mp479.53MB

44. Code Section on Kaggle/2. Examining the Code Section in Kaggle Lesson 2.mp4105.81MB

44. Code Section on Kaggle/3. Examining the Code Section in Kaggle Lesson 3.mp4159.89MB

45. Discussion Section on Kaggle/1. What is Discussion on Kaggle.mp440.63MB

46. Other Most Used Options on Kaggle/1. Courses in Kaggle.mp452.14MB

46. Other Most Used Options on Kaggle/2. Ranking Among Users on Kaggle.mp4107.04MB

46. Other Most Used Options on Kaggle/3. Blog and Documentation Sections.mp440.85MB

47. Details on Kaggle/1. User Page Review on Kaggle.mp481.5MB

47. Details on Kaggle/2. Treasure in The Kaggle.mp474.64MB

47. Details on Kaggle/3. Publishing Notebooks on Kaggle.mp438.2MB

47. Details on Kaggle/4. What Should Be Done to Achieve Success in Kaggle.mp458.48MB

48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Hearth Attack Prediction Project.mp4117.14MB

48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4104.93MB

48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp476.51MB

48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4126.87MB

49. First Organization/1. Required Python Libraries.mp463.55MB

49. First Organization/2. Loading the Statistics Dataset in Data Science.mp410MB

49. First Organization/3. Initial analysis on the dataset.mp463.96MB

5. Indexing, Slicing, and Assigning NumPy Arrays/1. Indexing Numpy Arrays,.mp426.56MB

5. Indexing, Slicing, and Assigning NumPy Arrays/2. Slicing One-Dimensional Numpy Arrays.mp422.27MB

5. Indexing, Slicing, and Assigning NumPy Arrays/3. Slicing Two-Dimensional Numpy Arrays.mp434.27MB

5. Indexing, Slicing, and Assigning NumPy Arrays/4. Assigning Value to One-Dimensional Arrays.mp418.2MB

5. Indexing, Slicing, and Assigning NumPy Arrays/5. Assigning Value to Two-Dimensional Array.mp435.4MB

5. Indexing, Slicing, and Assigning NumPy Arrays/6. Fancy Indexing of One-Dimensional Arrrays.mp420.49MB

5. Indexing, Slicing, and Assigning NumPy Arrays/7. Fancy Indexing of Two-Dimensional Arrrays.mp445.75MB

5. Indexing, Slicing, and Assigning NumPy Arrays/8. Combining Fancy Index with Normal Indexing.mp412.65MB

5. Indexing, Slicing, and Assigning NumPy Arrays/9. Combining Fancy Index with Normal Slicing.mp416.46MB

50. Preparation For Exploratory Data Analysis (EDA) in Data Science/1. Examining Missing Values.mp445.79MB

50. Preparation For Exploratory Data Analysis (EDA) in Data Science/2. Examining Unique Values.mp444.54MB

50. Preparation For Exploratory Data Analysis (EDA) in Data Science/3. Separating variables (Numeric or Categorical).mp415.81MB

50. Preparation For Exploratory Data Analysis (EDA) in Data Science/4. Examining Statistics of Variables.mp491.37MB

51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp480.35MB

51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/2. Numeric Variables (Analysis with Distplot) Lesson 2.mp419.75MB

51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp474.74MB

51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp484.06MB

51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp453.78MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp449.37MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp468.08MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp438.07MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp435.47MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp436.36MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp490.67MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp435.64MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp424.15MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp456.27MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp428.31MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp447.14MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/7. Feature Scaling with the Robust Scaler Method.mp435.2MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp452.89MB

52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp441.72MB

53. Preparation for Modelling in Machine Learning/1. Dropping Columns with Low Correlation.mp426.83MB

53. Preparation for Modelling in Machine Learning/10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp411.45MB

53. Preparation for Modelling in Machine Learning/11. Separating Data into Test and Training Set.mp429.75MB

53. Preparation for Modelling in Machine Learning/2. Visualizing Outliers.mp434.89MB

53. Preparation for Modelling in Machine Learning/3. Dealing with Outliers – Trtbps Variable Lesson 1.mp442.82MB

53. Preparation for Modelling in Machine Learning/4. Dealing with Outliers – Trtbps Variable Lesson 2.mp443.91MB

53. Preparation for Modelling in Machine Learning/5. Dealing with Outliers – Thalach Variable.mp436.24MB

53. Preparation for Modelling in Machine Learning/6. Dealing with Outliers – Oldpeak Variable.mp436.06MB

53. Preparation for Modelling in Machine Learning/7. Determining Distributions of Numeric Variables.mp425.17MB

53. Preparation for Modelling in Machine Learning/8. Transformation Operations on Unsymmetrical Data.mp424.01MB

53. Preparation for Modelling in Machine Learning/9. Applying One Hot Encoding Method to Categorical Variables.mp424.09MB

54. Modelling for Machine Learning/1. Logistic Regression.mp429.34MB

54. Modelling for Machine Learning/2. Cross Validation.mp430.21MB

54. Modelling for Machine Learning/3. Roc Curve and Area Under Curve (AUC).mp441.71MB

54. Modelling for Machine Learning/4. Hyperparameter Optimization (with GridSearchCV).mp458.77MB

54. Modelling for Machine Learning/5. Decision Tree Algorithm.mp425.7MB

54. Modelling for Machine Learning/6. Support Vector Machine Algorithm.mp424.52MB

54. Modelling for Machine Learning/7. Random Forest Algorithm.mp429.78MB

54. Modelling for Machine Learning/8. Hyperparameter Optimization (with GridSearchCV).mp452.65MB

55. Conclusion/1. Project Conclusion and Sharing.mp428.66MB

6. Operations in Numpy Library/1. Operations with Comparison Operators.mp421.14MB

6. Operations in Numpy Library/2. Arithmetic Operations in Numpy.mp471.82MB

6. Operations in Numpy Library/3. Statistical Operations in Numpy.mp432.02MB

6. Operations in Numpy Library/4. Solving Second-Degree Equations with NumPy.mp424.2MB

7. Pandas Library Introduction/1. Introduction to Pandas Library.mp433.93MB

8. Series Structures in the Pandas Library/1. Creating a Pandas Series with a List.mp439.19MB

8. Series Structures in the Pandas Library/2. Creating a Pandas Series with a Dictionary.mp418.29MB

8. Series Structures in the Pandas Library/3. Creating Pandas Series with NumPy Array.mp411.97MB

8. Series Structures in the Pandas Library/4. Object Types in Series.mp419.58MB

8. Series Structures in the Pandas Library/5. Examining the Primary Features of the Pandas Seri.mp418.94MB

8. Series Structures in the Pandas Library/6. Most Applied Methods on Pandas Series.mp448.21MB

8. Series Structures in the Pandas Library/7. Indexing and Slicing Pandas Series.mp429.89MB

9. DataFrame Structures in Pandas Library/1. Creating Pandas DataFrame with List.mp422.57MB

9. DataFrame Structures in Pandas Library/2. Creating Pandas DataFrame with NumPy Array.mp412.1MB

9. DataFrame Structures in Pandas Library/3. Creating Pandas DataFrame with Dictionary.mp415.84MB

9. DataFrame Structures in Pandas Library/4. Examining the Properties of Pandas DataFrames.mp425.94MB