Data Science & Machine Learning Course

Learn from experts about the fastest-growing job requirement in the industry!

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Click on Details for Syllabus

An expert trainer will help you learn Data Science with Machine Learning!

Features of the course:

1) A detailed day-wise schedule will be provided to get maximum results.

2) Certification after completion of course

3) Create industry-ready projects at the end of course.

4) You can watch courses at any time on a laptop as well as mobile. It's a recorded course.

5) The course has been recorded as an interaction between trainer and one demo student to make it interactive.

Click on the details to check the syllabus.

Projects Included in Course:

1. Bank Loan interest rate prediction model

2. Income classification model

3. Spam detection classification model

4. Crude or money text classification model

Complete Curriculum of Data Science & Machine Learning

1. Applications of Machine Learning

2. Why Machine Learning is the Future

3. Update: Recommended Anaconda Version

Part 1: Data Preprocessing 

4. Welcome to Part 1 - Data Preprocessing

5. Get the dataset

6. Importing the Libraries

7. Importing the Dataset

8. For Python learners, summary of Object-oriented programming: classes & objects

9. Missing Data

10. Categorical Data

11. Splitting the Dataset into the Training set and Test set

12. Feature Scaling

13. And here is our Data Preprocessing Template!


14. Mean, Median, Mode

15. Standard Deviation, Variance

16. Quartile, Range

 17. Central Limit Theorem

18. Correlation

19. Covariance

20. Mean Square Error

 21. Probability Distributions

 Part 2: Regression

22. Welcome to Part 2 - Regression

Simple Linear Regression

23. How to get the dataset

24. Dataset + Business Problem Description

25. Simple Linear Regression Intuition

26. Simple Linear Regression in Python

Quiz 2: Simple Linear Regression

Decision Tree Regression

27. Decision Tree Regression Intuition

28. How to get the dataset

29. Decision Tree Regression in Python

Random Forest Regression

30. Random Forest Regression Intuition

31. How to get the dataset

32. Random Forest Regression in Python

Evaluating Regression Models Performance

33. R-Squared Intuition

34. Evaluating Regression Models Performance

35. Interpreting Linear Regression Coefficients

36. Conclusion of Part 2 - Regression

Part 3: Classification

37. Welcome to Part 3 - Classification

Logistic Regression

38. Logistic Regression Intuition

39. How to get the dataset

40. Logistic Regression in Python

41. Python Classification Template

Quiz 4: Logistic Regression

Evaluating Classification Models Performance

42. False Positives & False Negatives

43. Confusion Matrix

44. Accuracy Paradox

45. CAP Curve

46. CAP Curve Analysis

47. Conclusion of Part 3 - Classification

Decision Tree Classification

48. Decision Tree Classification Intuition

49. How to get the dataset

50. Decision Tree Classification in Python

Random Forest Classification

51. Random Forest Classification Intuition

52. How to get the dataset

53. Random Forest Classification in Python

 Part 4: Model Selection & Boosting

54. Welcome to Part 10 - Model Selection & Boosting

Model Selection

55. How to get the dataset

56. k-Fold Cross Validation in Python

57 Grid Search in Python

58. Random Search in Python


59. How to get the dataset

60 XGBoost in Python

K-Nearest Neighbors (K-NN)

61 K-Nearest Neighbor Intuition

 62. How to get the dataset

63. K-NN in Python

Quiz 5: K-Nearest Neighbor

Support Vector Machine (SVM)

64. SVM Intuition

65. How to get the dataset

66. SVM in Python

Naive Bayes

 67. Bayes Theorem

68. Naive Bayes Intuition

69. How to get the dataset

70 Naive Bayes in Python

Part 5: Clustering

71. Welcome to Part 4 - Clustering

K-Means Clustering

72. K-Means Clustering Intuition

73. K-Means Random Initialization Trap

74. K-Means Selecting The Number Of Clusters

75. How to get the dataset

76. K-Means Clustering in Python

Quiz 6: K-Means Clustering

Hierarchical Clustering

77. Hierarchical Clustering Intuition

78. Hierarchical Clustering How Dendrograms Work

79. Hierarchical Clustering Using Dendrograms

80. How to get the dataset

81. HC in Python

Quiz 7: Hierarchical Clustering

82. Conclusion of Part 4 - Clustering

Data Science & Machine Learning Course
₹4999 ₹2499 
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This package includes :


Data Science with Machine Learning

Data Science with Machine Learning

Learn from experts about the fastest-growing job requirement in the industry!