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Artificial Intelligence with Python & IBM Cloud - Sona
Introduction to Artificial Intelligence
Intro Duction to Ai (9:32)
Applications of AI (13:32)
History & why AI (10:25)
History of AI (7:40)
Concepts of Machine Learning (65:52)
Python Programining
Nodered Console (32:29)
Python - Introduction (11:12)
Python - Variables (14:22)
Python - Fundamental datatypes (10:48)
Python - Strings (32:42)
Python - Lists (16:48)
Python Dictionaries (10:46)
Conditional Statements (18:18)
Loops (19:19)
functions (9:52)
File handling (24:10)
Exception handling (20:06)
Python For Datascience
Numpy Indexing and Slicing (33:01)
Numpy - Functions (39:47)
Pandas -Series and Data Frame (31:42)
Pandas - handing missing values (41:00)
Pandas - Functions (28:35)
Seaborn (60:07)
Matplot lib (52:00)
Data-PreProcessing
Data Preprocessing (111:20)
Artificial Neural Networks
Artificial neural Networks (93:46)
Artificial Neural Networks - Classification (32:26)
Doubt Clarification (52:06)
Regression and Categorical (62:07)
Evaluation Metrics (33:13)
Convolution Neural Networks
Convolution Neural Networks (52:07)
Video Analysis (10:26)
Convolution Neural Networks - practical (59:12)
Natural language processing
Naatural language Processing (21:58)
Naatural language Processing - practical (83:06)
Doubts - visualaizations (20:50)
doubts ann (83:21)
nlp-doubts (35:30)
Flask Implementation
Flask - Artificial Neural Networks (56:48)
Flask - Artificial Neural Networks part 2 (36:06)
Convolution neural networks - flask (81:23)
Miscellaneous (26:11)
RNN_-_Practical_Part_1 (41:10)
RNN_-Practical__Part_3 (30:22)
RNN_-_Tutorial_Part_1 (32:15)
RNN_-_Practical_Part_2 (16:50)
RNN_-_Tutorial_Part_2 (27:43)
IBM Cloud
IBM - AUTO AI (78:10)
Nodered Creation (34:25)
Auto AI - Nodered Integration (108:51)
Nodered -Part2 (73:09)
Convolution Neural Networks - practical
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