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Applied Data Science - Externship
Introduction to Artificial Intelligence
Introduction to AI and History of AI (11:28)
Applications of AI (7:11)
BUZZ Words Associated with AI (6:02)
Machine Learning Life Cycle (12:21)
Machine Learning Concepts (10:40)
Python Programing
1.Introduction to Python (4:45)
2.Set-up Python Environment (10:47)
3.Keywords and Identifiers (7:50)
4.Python Comments ,statements,Indendation (8:23)
5. Python Variables and Literals (13:03)
6. Fundamental data Types - Numeric and StringData Types (28:11)
7. Python Lists (11:39)
8. Tuple (3:24)
9.Dictionary , Set , Type Conversion (16:11)
10.Conditional Statements (17:06)
11. Looping Statements (19:33)
12. Python - Functions (20:26)
13.File handling (12:09)
14.Exception handling (20:04)
15.OOPS Concept - Introduction (1:34)
16.Class , Objects , Init and Self (17:18)
17. Inheritance (20:57)
18. Polymorphism (12:13)
19. Encapsulation and Abstarction (15:36)
Python For Data Science
1. Python For data Science Introduction (4:06)
2. Python for data Science - Creating Numpy Arrays (11:16)
3. Python for Data Science - Numpy Array Indexing and slicing (8:51)
4. Python for Data science - Numpy Inbuilt Methods (22:05)
5. Python For Data Science - Numpy Operations (3:34)
1. Introduction to Pands - Series (4:32)
2. Pandas - Data Frame (15:04)
3. Pandas - Handling Missing Values (10:21)
4. Pandas - Groupby (7:25)
5. Pandas - Operations (9:16)
6. Pandas - Input and Output (11:13)
1.DataVisualization - matplotlib (27:44)
2.Data Visualization - Seaborn (27:04)
Data Preprocessing
Introduction to DataPreprocessing (2:14)
Spliting Data to train and test (8:34)
OneHotEncoding & Colum tranform (25:57)
Label Encoding (7:16)
Feature Scaling (3:59)
Quiz for the Data Preprocessing Section
Supervised Regression Analysis
1.Introduction to Regression Analysis (4:41)
3.Linear Regression intution (3:52)
4. Mathematical Implementation of Linear Regression (6:50)
Linear Regression Practicals (21:20)
Multi Linear Regression (7:21)
Multilinear Regression Practical (30:29)
Polynomial Regression (5:12)
Polynomial Regression Practical (17:10)
DecisionTree and Random Forest Regression (5:11)
Decision Tree and random Forest Practical (19:37)
Quiz for Regression Algorithms
Supervised Classification
1.Introduction to Classification Algorithms (3:41)
2. Logistic Regression Intuition (11:18)
3.Logistic Regression Practical (34:45)
4.Descision Tree classification (20:39)
5.Random forest Classification (6:04)
6.Descision Tree and Random Forest Classification Practical (43:46)
7. KNN TIntuition (4:34)
8. K nearest Neighbors - Practical (22:13)
9. Naive Bayes Algorithm - intuition (9:17)
10. NaiveBayes Practicals (5:34)
Quiz for Classification Algorithms
Clustering Algorithms
Kmeans Clustering (41:28)
Flask Deployment
Introduction to Flask (27:15)
Machine Learning mdel Integration with Flask (23:54)
Model Building With IBM
Introduction to Watson AI services
Watson Studio Service Creation (2:07)
Watson Studio Project Creation (2:46)
Create An Auto AI Experiment (3:35)
Auto AI for Insurance premium cost prediction (1:36)
Run Auto Ai Experiment (8:56)
Save Auto Ai Model (1:40)
Deploy ML Auto Ai Model (3:09)
Node-Red Creation (6:36)
Basics of Node-RED (38:14)
Node-RED Integration with Auto Ai Model (34:51)
Python Flask Deployment (22:29)
Spliting Data to train and test
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