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Artificial Intelligence (AI) In Python: A H2O Approach
Welcome To The World of Python AI
What is this course about? (3:29)
Data and code
What is AI? (9:51)
Introduction to the Python Data Science Environment (10:57)
Upgraded Python3 Installation (5:44)
The IPython Ecosystem (19:13)
Read in and Preprocess Data From External Data Sources
Introduction to Pandas (12:06)
Read CSV (5:42)
Read Excel (5:31)
Read HTML (5:58)
Introduction to Pandas For Basic EDA (4:30)
Introduction to H2O
What is H2O?
More H2O Installation (2:11)
Getting Used To the H2O Framework (2:12)
Read in Data as H2O Frame (4:23)
Convert To H2O Frame (2:43)
What is Machine Learning (ML)?
Theory Behind ML (5:32)
Supervised Learning With H2O
What Is Supervised Classification? (10:10)
Supervised Classification Accuracy (4:19)
Theory of GLM (5:25)
Set up GLMs (11:35)
Test GLM Performance (9:48)
Select Optimum GLM Parameters: Grid search (10:03)
Random Forest For Binary Classification (17:50)
Implement a Random Forest Model (7:49)
Gradient Boosting Machine (GBM) For Regression (11:11)
Search or GBM Parameters (7:02)
XGB Theory (2:02)
XGBoost for Binary Classification (5:15)
XGBoost For Multiclass Classification (5:12)
Search For the Best H2O Model:Select the Best Machine Learning Model (5:20)
Unsupervised Learning
What Is Unsupervised Classification? (1:38)
Principal Component Analysis (PCA) Theory (2:37)
PCA (6:14)
k-means theory (1:57)
k-means (11:08)
Neural Networks With H2O
Theoretical Introduction (9:17)
What are Activation Functions? (5:50)
Implement Deep Learning for Binary Classification (8:01)
Theory Behind Autoencoders (1:46)
Set up Autoencoder (4:06)
Implement the Autoencoder (3:11)
Select Optimum GLM Parameters: Grid search
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