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Hands-on Artificial Intelligence & Machine Learning with Python
Introduction To Artificial Intelligence & Machine Learning
Introduction to ML & AI (5:00)
Introduction to ML & AI (2) (5:00)
Benefits & Application of AI (5:00)
Benefits & Application of AI (2) (5:00)
Benefits & Application of AI (3) (5:00)
Use of AI in IoT (5:00)
Models of ML (2:34)
Models of ML (2) (2:18)
Introduction to Python
Jupyter Notebook (1:56)
Installation of Colab (4:59)
Introduction to Python (4:20)
Data types in Python (10:32)
Lists in Python (10:11)
Tuples in Python (5:22)
Sets in Python (8:41)
Dictionary in Python (5:51)
Conditional Statements in Python (8:31)
Conditional Statement in Python-Part#2 (8:42)
Loops in Python: While Loop (6:24)
Loops in Python: For Loop (7:41)
Functions in Python (6:16)
Classes in Python (7:27)
Numpy in Python
Introduction to Numpy (7:37)
Introduction & Installation (6:59)
Creating an array (7:11)
Mathematical Operator (8:20)
Built-in Functions (5:36)
Matplotlib In Python
Introduction & Installation (8:27)
Built-in Functions (5:24)
Models OF Machine Learning
Simple Linear Regression (3:36)
Multiple Linear Regression (3:48)
Polynomial Regressison (3:59)
Support Vector Regressor (2:29)
Decision Tree Regressor (2:24)
Random Forest Regressor (3:18)
Random Forest Classifier (3:43)
KNN Classifier (3:29)
Support Vector Machine (3:36)
Naive Bayes Classifier (3:53)
Decision Tree Classifier (3:37)
Implementation of Models
Linear Regression (14:56)
Multiple Linear Regression (9:45)
Support Vector Regression (8:49)
Decision tree (6:13)
Random Forest (7:22)
KNN Classifier (12:46)
Support Vector Machine (8:44)
Creating AI Model with IBM Watson
AI Model with IBM Watson- Part 1 (8:11)
AI Model with IBM Watson- Part 2 (5:36)
AI Model with IBM Watson- Part 3 (13:30)
Random Forest Regressor
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