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Indicator variables

Instructor: Applied AI Course Duration: 7 mins

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Graph data
Feature binning
2.1
Calibration of Models:Need for calibration
8 min
2.2
Calibration Plots.
17 min
2.3
Platt’s Calibration/Scaling.
8 min
2.4
Isotonic Regression
11 min
2.5
Code Samples
5 min
2.6
Modeling in the presence of outliers: RANSAC
13 min
2.7
Retraining models periodically.
8 min
2.8
A/B testing.
22 min
2.9
VC dimension
22 min
2.10
Data Science Life cycle
17 min
2.11
Productionization and deployment of Machine Learning Models
17 min
2.12
Productionization and deployment + Spark
96 min
2.13
Hands on Live Session: Deploy an ML model using Flask APIs on AWS
125 min
2.14
Building web apps for ML/AI using StreamLit
101 min
2.15
Building web apps for ML/AI using StreamLit - II
66 min
2.16
ML Model productionization using Heroku
120 min
2.17
Amazon Sagemaker--Part 1
147 min
2.18
Amazon sagemaker--part 2
161 min
2.19
SageMaker Part 3: Distributed training and Deep Learning
127 min
2.20
SageMaker Part 4: Spark and Pipelines
121 min
2.21
Amazon SageMaker : Part 5 [Miscellaneous topics]
97 min
2.22
Design and Productionization of low latency ML systems
93 min
3.1
Live session on Time Series Analysis and Forecasting
3.2
Testing and Debugging ML/AI systems end to end
3.3
Interview Questions on Productionization, Deployment
3.4
Interview Questions on Productionization and Deployment-PART II
3.5
KubeFlow: Architecture and Components
3.6
KubeFlow: Installation, Setup and Config
3.7
KubeFlow: Dashboard, Notebook Servers and Pipelines
3.8
KubeFlow: Pipelines using Kale, Rok, Katib and KfServing
3.9
Design and Productionization of low latency ML systems
3.10
Live Session(20th Feb 2022): Scenario based Interview Questions for ML engineer roles
102 min
3.11
Live Session(11th Sep 2022): ML Deployment Scenarios
3.12
Live Session(29th Nov 2022): Practical Time series Forecasting