Courses

Week

Course Details /Syllabus 

Week-0:

Ground Work

1. Professional Social Media Growth

2.Linkedin-1

3.Linkedin-2

4.Linkedin-3

5.Github-1

6.Github-2

7. How to take Notes-Must Watch

Week-1:

Travelling Path

1. What is AI, How its created, end goal of AI?

2. Where to Sell Ai Projects, the thumb rule to integrate Ai in any Department

3. Comparison Between AI and Humans?

4. Relationship Between Ai, Machine Learning, DL, NLP, TSA, and Data Science?

5. Man like AI, Traditional Vs AI?

6. Traditional vs AI-2?

7. Thumb rule to make money over AI projects?

8. Heart of AI projects?

9.RealTime Applications?

10.Baby-Step-1?

11. How to select the domain for AI Projects?

12. Why Data Science?

13. Relationship between AI and Python

14. Road Map to complete AI?

Test-1

Week-2:

Python

1. Python Tool

2. Where to Download Anaconda?

3. How to open the Jupyter notebook?

4. Introduction to Programming?

5.7-Concepts, Print?

6. Print-Name Error?

7. Print hands-0n

8. Variable and Assignment?

9. Variable-Handson?

10. Rules to write Variable Name?

11. String?

12. How to write an Efficient program?

13. input statement?

14. Recall Session?

15.ControlStructures?

16. If Statement?

17. if-else?

18.if-elif?

19. if-Thumbrule?

20. For Loop?

Test – 2

21. How to Finish Assignments

1. Baby Step-2

2. Extra Assignment-level-1

3. Extra Assignment-Level-2

4. Extra Assignment-3

22.OOPs

23.Function-1

24.Function-2

25.Function-3

25.Function-4

26.Function-5

28. Function Assignments

Function Assignments

29.Class-1

30.Class-2

31.Class-3

32. Class Assignments

Class Assignments

Test – 3

Week:3

Machine Learning-Regression

1. Problem Identification

2. How to identify – Supervised Learning

3. How to identify – Unsupervised Learning

4. Difference between supervised and unsupervised

5. Semi-Supervised Learning

6. Supervised- Classification and Regression

7. Scenario-Based Example-1

8. Scenario-Based Example- 2

9. Problem Identification- Assignments

10. Two Phases of AI

11. Model Creation-Learning Phase-1

12. Deployment Pahse-2

13. Algorithm

14. Simple Linear Regression

15. Problem Identification in SLR

16. Detailed Explanation of Model Creation

17. Evaluation Metric-SSE, SSR, SST

18.R_Square and Adjusted R_Square

19. The purpose of Training and Test Set.

20.AI in HR-Req-Problem Identification

21. Mapping with phases

22. Hands- on-1-Training Test Set

23.Hands-on-2-Model Creation

24.Hands-on-3- Evaluating

25.Hands-on-4-How to save the model

26. How to save model-2

27. Hands-on- Deployment

28. Baby step2

29. Multiple Linear regression

30.PS_AI in Business Intelligence

31. Nominal and Ordinal

32. Code Walkthrough

33. Hands-on – MLR

34. SVM

35. Standard

36. ML-Secret

37. SVM-Hands-On

SVM-Assignment

38. Decision Tree

39. Hands-On-Decision Tree

40. Random Forest

41. Random Forest-Hands-on

42. Assignment

43. Boosting Algorithm

44. How to Install the library

45. Cross Validation

46.GridSearchCV

Week:4

Machine Learning-Classification

1. Intro to Classification & Problem Statement

2. Hands-On Walkthrough

3. Confusion Matrix-1

4. Confusion Matrix-2

5. Hands-on-DT, SVM

6. Logistic Classification

7. Logistic- Hands-on

8. KNN

9. Hands-on-KNN

10. Naive Bayes

11. Hands-on-NB

12. All Algorithms

13. Grid Search-Classification-1

14. Grid Search-2

15. Assignment-Classification

Classification Assignment

Assignment Confirmation

16. Virtual Environment

17. VE Creation

Week:5

Machine Learning-Clustering

1. K-means clustering

2. Problem Statement for Clustering

3. K-Means-Code Walkthrough

4. K-Means-Hands-on

5. Agglomerative-1

6.Agglomerative-2

7. Clustering Assignment

Week:6

Data Science-Univariate

1. Introduction to Data Science

2. Inferential Analysis

3. Application of Data Science

4. Types of column

5. Problem Statement

6.Hands-on-QuanQual-1

7.Hands-on-QualQuan-2

8. Faircopy

9. Introduction to Univariate

10. Central Tendency-1

11. Central Tendency -2

12. Central Tendency-3

13. Hands-on-CT-1

14. Hands-on-CT-2

15. Percentile

16. Hands-on-Percetile-1

17. IQR

18.Hands-on-IQR-1

19.Hands-on-IQR-2

20. Hands-on-IQR-3

21. Frequency

22. Frequency-hands-on

23. Variance and Standard Deviation

24. Hands-on Variance and Std

25. Skewness

26. Hands-on Skewness

27. Kurtosis

28. Hands-on Kurotsis

29. Kurotsis Vs Skewness

30. Normal Distribution