The Complete Artificial Intelligence and Data Science in English
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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
1) Professional Social Meida
2) Linkedin-1
3) Linkedin-2
4) Linkedin-3
5) Github-1
6) Github-2
6) Github-2
7) How to write notes
Week-1-Travell Plan
1) What is AI How its created end goal
2) Where to Sell Ai Projects thumb rule to integrate Ai in any Department
3) Comparison Between AI and Human
4) Relationship Between Ai Machine Learning DL NLP TSA 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) Real Time Applications
10) Baby Step-1
11) How to select domain for AI Projects
12) Why Data Science
13) Relationship between AI and Python
14) Road Map to complete AI
Week-2-Python
1) Python Tool
2) Where to Download Anaconda
3) How to open Jupyter notebook
4) Introduction to Programming
5) Concepts Print
6) Print Name Error
7) Print hands-0n
8) Variable and Assignment
9) Variable-Hands-on
10) Rules to write Variable Name
11) String
12) How to write Efficient program
13) Input statement
14) Recall Session
15) Control Structures
16) If Statement
17) if-else
18) if-elif
19) if-Thumbrule
20) For Loop
21) How to Finish Assignments
22) OOPs
23) Function-1
24) Function-2
25) Function-3
26) Function-4
27) Function-5
28) Function Assignments
29) Class-1
30) Class-2
31) Class-3
32) Class Assignments
Week3-Machine Learning-Regression
1) Problem Identification
2) How to identify Supervised Learning
3) How to identify Unsupervised Learning
4) Difference btw supervised and unsupervised
5) Semi Supervised Learning
6) Supervised Classification and Regression
6) Supervised Classification and Regression
7) Scenario Based Exmaple-1
8) Scenario Based Example-2
9) Problem Identification-Assignments
10) Two Phases of Artificial Intelligence
11) Model Creation Learning Phase-1
12) Deployment Phase-2
13) Algorithms
14) Simple Linear Regression-1
15) Problem Identification-SLR
16) Detailed Explanation of Model creation
17) Evaluation Metrics-SSE,SSR,SST
18) R_Square Adjusted R_Square
19) The purpose of Training and Test Set
20) AI in HR-Req-Problem Identification
21) Mapping Theory and Coding
22) Hands-On Coding-1-Train-Test
23) Hands-on Coding-2-Model Creation
24) Hands-on-3-Evaluation Metric
25) How to save model
26) Part-2-How to save
27) Hands-on Deployment
28) Baby Step-2
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
38) Decision Tree
39) Hands-on-Decision Tree
40) Random Forest
41) Hands-on-Random
42) Assignment
43) Boosting Algorithm
44) How to install a library
45) Cross Validation
46) GridSearchCV
Week-4-Machine Learning-Classification
1) Intro to Classification and problem statement
2) Hands-on Walk through
3) Confusion Matrix-1
4) Confusion matrix-2
5) Hands-on-DT, SVM
6) Logistic Regression
7) Logistic-Hands-on
8) KNN
9) KNN Hands-on
10) Navie Bayes
11) Hands-on-NB
12) All Algorithms
13) Grid Search Classification-1
14) GridSearch-2
15) Assignment-Classification
16) Virtual Environment
17) Virtual Creation
Week-5- Machine Learning-Clustering
1) K-Means
2) Problem Statement
3) K-Means overview-hands-on
4) Hands-on K-Means
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 QuanQual-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-Percentile
17) IQR
18) Hands-on-IQR-1
19) Hands-on-IQR-2
20) Hands-on-IQR-3
21) Frequecy
22) Relative Frequency
23) Variance and Standard Deviation
24) Hands-on Variance and Std
25) Skewness
26) Hands-on Skewness
27) Kurtosis
28) Hands-on Kurtosis
29) Skewness VS kurtosis
30) Normal Distribution
Course Instructor
Ramisha Rani K
5 Courses • 4030 Students
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