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The Complete Artificial Intelligence and Data Science in English

Course Instructor Ramisha Rani K

₹55000.00 ₹60000.00 8% OFF

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Course Overview

Schedule of Classes

Course Curriculum

1 Subject

The Complete AI Course-English

156 Learning Materials

Week-0-Ground Work

0) Mind Preparation

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00:10:02

1) Professional Social Meida

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00:02:45

2) Linkedin-1

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00:06:27

3) Linkedin-2

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00:02:45

4) Linkedin-3

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00:03:45

5) Github-1

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00:01:23

6) Github-2

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00:06:15

6) Github-2

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00:06:15

7) How to write notes

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00:04:20

Week-1-Travell Plan

1) What is AI How its created end goal

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00:07:29

2) Where to Sell Ai Projects thumb rule to integrate Ai in any Department

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00:08:08

3) Comparison Between AI and Human

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00:02:56

4) Relationship Between Ai Machine Learning DL NLP TSA Data Science

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00:03:45

5) Man like AI Traditional Vs AI

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00:09:58

6) Traditional vs AI-2

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00:04:12

7) Thumb rule to make money over AI projects

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00:05:34

8) Heart of AI projects

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00:02:00

9) Real Time Applications

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00:06:46

10) Baby Step-1

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00:02:31

11) How to select domain for AI Projects

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00:05:18

12) Why Data Science

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00:01:33

13) Relationship between AI and Python

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00:01:56

14) Road Map to complete AI

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00:02:38

Week-2-Python

1) Python Tool

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00:07:28

2) Where to Download Anaconda

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00:03:41

3) How to open Jupyter notebook

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00:04:53

4) Introduction to Programming

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00:07:20

5) Concepts Print

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00:09:58

6) Print Name Error

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00:03:40

7) Print hands-0n

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00:01:13

8) Variable and Assignment

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00:06:58

9) Variable-Hands-on

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00:14:49

10) Rules to write Variable Name

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00:05:35

11) String

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00:01:23

12) How to write Efficient program

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00:10:09

13) Input statement

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00:07:48

14) Recall Session

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00:02:57

15) Control Structures

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00:07:43

16) If Statement

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00:09:13

17) if-else

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00:10:00

18) if-elif

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00:14:03

19) if-Thumbrule

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00:04:52

20) For Loop

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00:15:03

21) How to Finish Assignments

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00:09:13

22) OOPs

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00:03:26

23) Function-1

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00:03:40

24) Function-2

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00:08:51

25) Function-3

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00:06:37

26) Function-4

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27) Function-5

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28) Function Assignments

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29) Class-1

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30) Class-2

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31) Class-3

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00:10:07

32) Class Assignments

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00:00:58

Week3-Machine Learning-Regression

1) Problem Identification

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00:03:52

2) How to identify Supervised Learning

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00:06:54

3) How to identify Unsupervised Learning

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00:03:19

4) Difference btw supervised and unsupervised

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00:04:33

5) Semi Supervised Learning

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00:03:54

6) Supervised Classification and Regression

6) Supervised Classification and Regression

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00:02:44

7) Scenario Based Exmaple-1

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00:05:28

8) Scenario Based Example-2

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00:03:06

9) Problem Identification-Assignments

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00:01:44

10) Two Phases of Artificial Intelligence

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00:04:03

11) Model Creation Learning Phase-1

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00:07:24

12) Deployment Phase-2

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00:03:47

13) Algorithms

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00:01:41

14) Simple Linear Regression-1

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00:03:15

15) Problem Identification-SLR

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00:04:37

16) Detailed Explanation of Model creation

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00:10:02

17) Evaluation Metrics-SSE,SSR,SST

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00:04:42

18) R_Square Adjusted R_Square

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00:05:03

19) The purpose of Training and Test Set

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00:05:28

20) AI in HR-Req-Problem Identification

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00:08:00

21) Mapping Theory and Coding

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00:15:30

22) Hands-On Coding-1-Train-Test

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00:18:39

23) Hands-on Coding-2-Model Creation

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00:08:53

24) Hands-on-3-Evaluation Metric

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00:05:20

25) How to save model

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00:04:09

26) Part-2-How to save

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00:00:51

27) Hands-on Deployment

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00:06:13

28) Baby Step-2

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00:02:35

29) Multiple Linear Regression

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00:04:15

30) PS Ai in Business Intelligence

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00:03:57

31) Nominal and Ordinal

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00:09:32

32) Code Walkthrough

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00:02:07

33) Hands-on-MLR

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00:22:59

34) SVM

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00:14:10

35) Standard

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00:03:21

36) ML-Secret

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37) SVM-Hands-on

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00:21:39

38) Decision Tree

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00:12:03

39) Hands-on-Decision Tree

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00:04:44

40) Random Forest

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00:05:30

41) Hands-on-Random

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42) Assignment

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00:03:46

43) Boosting Algorithm

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00:11:38

44) How to install a library

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00:04:06

45) Cross Validation

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00:09:04

46) GridSearchCV

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00:24:15

Week-4-Machine Learning-Classification

1) Intro to Classification and problem statement

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00:13:39

2) Hands-on Walk through

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00:22:32

3) Confusion Matrix-1

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00:18:12

4) Confusion matrix-2

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00:23:36

5) Hands-on-DT, SVM

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00:14:34

6) Logistic Regression

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00:04:00

7) Logistic-Hands-on

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00:04:03

8) KNN

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00:05:21

9) KNN Hands-on

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00:05:19

10) Navie Bayes

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00:10:01

11) Hands-on-NB

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00:05:00

12) All Algorithms

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00:03:37

13) Grid Search Classification-1

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00:18:52

14) GridSearch-2

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00:04:43

15) Assignment-Classification

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00:01:53

16) Virtual Environment

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00:13:12

17) Virtual Creation

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00:10:47

Week-5- Machine Learning-Clustering

1) K-Means

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00:09:43

2) Problem Statement

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00:02:08

3) K-Means overview-hands-on

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00:24:42

4) Hands-on K-Means

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00:16:44

5) Agglomerative-1

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00:03:53

6) Agglomerative-2

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00:03:01

7) Clustering Assignment

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00:07:33

Week-6-Data Science- Univariate

1) Introduction to Data Science

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00:15:08

2) Inferential Analysis

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00:10:46

3) Application of Data Science

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00:08:55

4) Types of column

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00:08:41

5) Problem statement

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00:07:42

6) Hands-on QuanQual-1

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00:09:42

7) Hands-on QuanQual-2

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00:14:50

8) Faircopy

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00:16:22

9) Introduction to Univariate

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00:02:37

10) Central tendency-1

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00:06:45

11) Central tendency-2

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00:09:41

12) Central Tendency-3

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00:05:00

13) Hands-on CT-1

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00:05:17

14) Hands-on CT-2

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00:17:30

15) Percentile

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00:09:36

16) Hands-on-Percentile

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00:11:40

17) IQR

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00:12:54

18) Hands-on-IQR-1

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00:09:18

19) Hands-on-IQR-2

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00:16:16

20) Hands-on-IQR-3

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00:14:47

21) Frequecy

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00:08:52

22) Relative Frequency

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00:14:39

23) Variance and Standard Deviation

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00:10:48

24) Hands-on Variance and Std

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00:03:15

25) Skewness

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00:05:01

26) Hands-on Skewness

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00:03:16

27) Kurtosis

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00:03:10

28) Hands-on Kurtosis

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00:08:05

29) Skewness VS kurtosis

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00:02:04

30) Normal Distribution

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00:13:00

Course Instructor

tutor image

Ramisha Rani K

5 Courses   •   5791 Students