ML Fundamentals

deeplearning.ai Learner Community Event ft. Roger Smith

Ines & Sofie — Building Industrial-Strength NLP Pipelines

Encode categorical features using OneHotEncoder or OrdinalEncoder

Getting ready to learn Python, Mac edition #4: Install and run Python

7.7 Stacking (L07: Ensemble Methods)

7.6 Random Forests (L07: Ensemble Methods)

Getting ready to learn Python, Mac edition #3: Creating and editing text files

Accelerating Large-Scale Recommenders with NVIDIA Merlin on DGX A100

We're hosting a Machine Learning Conference!

Updated Best YouTube Channels To Learn Data Science For Free

W&B Deep Learning Salon - Richard Craib & Aleksa Milosevic

Class 1: Intro and Key Technological Trends Affecting Financial Services

Class 3: Artificial Intelligence in Finance

Data Science and Spend Data Classification with Susan | 365 Data Use Cases

Four reasons to use scikit-learn (not pandas) for ML preprocessing

Operationalize Open Source Models with SAS Open Model Manager // Ivan Nardini // MLOps Meetup #39

Machine Learning for Sustainable Agriculture

Inside TensorFlow: Building ML infra

Convolution padding and stride | Deep Learning Tutorial 25 (Tensorflow2.0, Keras & Python)

Tutorial 3- Julia Basics-Strings And Numerical Operations, Commenting

Image classification using CNN (CIFAR10 dataset) | Deep Learning Tutorial 24 (Tensorflow & Python)

#TWIMLfest: AI in the Fight Against Climate Change

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

7.5 Gradient Boosting (L07: Ensemble Methods)

Richard Craib on seeking alpha and building the last hedge fund at Numerai

Class 2: Artificial Intelligence, Machine Learning, and Deep Learning

Tutorial 1- Introduction To Julia Programming Language For Machine Learning

#TWIMLfest: Fundamentals of Medical Image Processing for Deep Learning

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

1. What Makes Healthcare Unique?

3. Deep Dive Into Clinical Data

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