Machine Learning In Python – From A To Z Machine Learning

سرفصل های دوره

1 - Python Fundamentals

1 - Print & Comments

2 - Variables part 1

3 - Variables part 2

4 - Data types part 1

5 - Data types part 2

6 - Operators

7 - If Statements

8 - Loops

9 - Functions

2 - Pandas

10 - Pandas

11 - Pandas 2

12 - Pandas 3

3 - Feature Engineering

13 - Feature Scaling

14 - Feature Scaling in Python

15 - Label Encoding

16 - One Hot Encoding

17 - Outlier Detection

4 - Evaluation of the model performances

18 - TrainTest Split

19 - MSE RMSE

20 - Confusion Matrix Accuracy Score

5 - Machine Learning Supervised vs Unsupervised

21 - Supervised vs Unsupervised Machine Learning

6 - Data set we are going to use in regression tasks

22 - EDA

23 - Feature Engineering

7 - Data set we are going to use in classification algorithms

24 - EDA

25 - Feature Engineering

8 - Linear Regression

26 - Linear Regression

27 - Linear Regression 2

28 - Linear Regression 3

29 - Linear Regression Coding

9 - Logistic Regression

30 - Logistic Regression

31 - Logistic Regression Coding

10 - K Nearest Neighbors

32 - K Nearest Neighbors

33 - KNearest Neighbors Coding Elbow Method

34 - KNearest Neighbors Coding

11 - Support Vector Machines

35 - Support Vector Machines

36 - Support Vector Regression Coding

12 - Decision Tree

37 - Decision Tree

13 - Random Forest

38 - Random Forest

39 - Random Forest Regression

14 - Finding the best performing algorithm

40 - About this section.html

41 - For regression data

42 - For classification data

43 - Classification part 2

15 - Kmeans Clustering

44 - Kmeans Clustering

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تولید کننده: Udemy-Training