About this Course

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Advanced Level
Approx. 14 hours to complete
English

Skills you will gain

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 14 hours to complete
English

Offered by

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IBM

Syllabus - What you will learn from this course

Week
1

Week 1

7 hours to complete

Model Evaluation and Performance Metrics

7 hours to complete
6 videos (Total 18 min), 19 readings, 6 quizzes
6 videos
Evaluation Metrics2m
Introduction to Predictive Linear and Logistic Regression3m
Linear Models4m
Watson Natural Language Understanding Service Overview3m
Case Study Introduction1m
19 readings
Evaluation Metrics: Through the Eyes of our Working Example3m
Evaluation Metrics3m
Regression Metrics5m
Classification Metrics10m
Multi-class and Multi-label Metrics3m
Model Performance: Through the Eyes of our Working Example3m
Generalizing Well to Unseen Data3m
Model Plots, Bias, Variance4m
Relating the Evaluation Metric to a Business Metric4m
Linear Models: Through the Eyes of our Working Example3m
Generalized Linear Models5m
Linear and Logistic Regression5m
Regularized Regression3m
Stochastic Gradient Descent Classifier3m
Watson Natural Language Understanding: Through the eyes of our Working Example3m
Watson Developer Cloud Python SDK10m
Performance and Business Metrics: Through the Eyes of our Working Example3m
Getting Started with Performance and Business Metrics Case Study (Hands-on)2h
Summary/Review10m
6 practice exercises
Check for Understanding30m
Check for Understanding30m
Check for Understanding30m
Check for Understanding30m
Check for Understanding30m
End of Module Quiz10m
Week
2

Week 2

6 hours to complete

Building Machine Learning and Deep Learning Models

6 hours to complete
5 videos (Total 15 min), 14 readings, 5 quizzes
5 videos
Introduction to Tree Based Methods2m
Neural Networks2m
Introduction to neural networks4m
IBM Watson Visual Recognition Overview2m
14 readings
Tree-based Methods: Through the Eyes of our Working Example3m
Decision Trees4m
Bagging and Random Forests4m
Boosting2m
Ensemble Learning4m
Neural networks: Through the eyes of our Working Example3m
Multilayer perceptron (MLP)4m
Neural network architectures4m
On interpretability2m
Watson Visual Recognition: Through the Eyes of our Working Example3m
Watson Developer Cloud Python SDK10m
TensorFlow: Through the Eyes of our Working Example3m
Getting Started with Convolutional Neural Networks and TensorFlow (Hands-on)2h
Summary/Review10m
5 practice exercises
Check for Understanding30m
Check for Understanding30m
Check for Understanding30m
Check for Understanding30m
End of Module Quiz10m

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