ML Fundamentals

TensorFlow Tutorial 10 - Recurrent Neural Nets (RNN & LSTM & GRU)

#TWIMLfest: Show & Tell: Art Training: How Artistic Practices Can Adopt Machine Learning

1. What Makes Healthcare Unique?

3. Deep Dive Into Clinical Data

U Caldas | Coursera for Campus #LearnerStories

Use "fit_transform" on training data, but "transform" (only) on testing/new data

Google Datasets Search Engine- Search All Datasets From One Place For Data Science,Machine Learning

7.4 Boosting and AdaBoost (L07: Ensemble Methods)

NVIDIA Broadcast App: AI-based application for streaming / broadcasting / meeting (all features)

Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard

7.3 Bagging (L07: Ensemble Methods)

Xgboost Regression In-Depth Intuition Explained- Machine Learning Algorithms 🔥🔥🔥🔥

Neural Networks Pt. 2: Backpropagation Main Ideas

Daeil Kim — The Unreasonable Effectiveness of Synthetic Data

5 Machine Learning BEGINNER Projects (+ Datasets & Solutions)

Getting ready to learn Python, Mac edition #2: Navigating from the command line

Getting ready to learn Python, Mac edition #1: Files and directories

Introduction to Stacks and Queues (Data Structures & Algorithms #12)

Jetson AI Fundamentals - S2E3 - JetBot Collision Avoidance

Seven ways to select columns using ColumnTransformer

Fireside Chat: Jeff Maggioncalda, CEO, Coursera and Professor Anju Seth, Director, IIM Calcutta

Clinical Research with FHIR

YogAI: TensorFlow powered yoga instructor - Made with TensorFlow.js

13. Machine Learning for Mammography

21. Automating Clinical Work Flows

19. Disease Progression Modeling and Subtyping, Part 2

14. Causal Inference, Part 1

20. Precision Medicine

8. Natural Language Processing (NLP), Part 2

25. Interpretability

23. Fairness

15. Causal Inference, Part 2

2. Overview of Clinical Care

7. Natural Language Processing (NLP), Part 1

12. Machine Learning for Pathology

11. Differential Diagnosis

16. Reinforcement Learning, Part 1

9. Translating Technology Into the Clinic

4. Risk Stratification, Part 1

6. Physiological Time-Series

5. Risk Stratification, Part 2

18. Disease Progression Modeling and Subtyping, Part 1

17. Reinforcement Learning, Part 2

7.2 Majority Voting (L07: Ensemble Methods)

7.1 Intro to ensemble methods (L07: Ensemble Methods)

6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)

6.6 Improvements & dealing with overfitting (L06: Decision Trees)

6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)