Mini Course in Deep Learning with #PyTorch for AIMS
#course #DL
____
@Machine_learn
___
https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
#course #DL
____
@Machine_learn
___
https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
GitHub
GitHub - Atcold/NYU-DLSP20: NYU Deep Learning Spring 2020
NYU Deep Learning Spring 2020. Contribute to Atcold/NYU-DLSP20 development by creating an account on GitHub.
@Machine_learn
Course Curriculm #course
#ML
1. Welcome to the Applied Machine Learning Course
2. Introduction to Data Science and Machine Learning
3. Introduction to the Course
4. Setting up your system
5. Python for Data Science
6. Statistics For Data Science
7. Basics Steps of Machine Learning and EDA
8. Data Manipulation and Visualization
9. Project: EDA - Customer Churn Analysis
10. Share your Learnings
11. Build Your First Predictive Model
12. Evaluation Metrics
13. Build Your First ML Model: k-NN
14. Selecting the Right Model
15. Linear Models
16. Project: Customer Churn Prediction
17. Dimensionality Reduction (Part I)
18. Decision Tree
19. Feature Engineering
20. Share your Learnings
21. Project: NYC Taxi Trip Duration prediction
22. Working with Text Data
23. Naïve Bayes
24. Multiclass and Multilabel
25. Project: Web Page Classification
26. Basics of Ensemble Techniques
27. Advance Ensemble Techniques
28. Project: Ensemble Model on NYC Taxi Trip Duration Prediction
29. Share your Learnings
30. Advance Dimensionality Reduction
31. Support Vector Machine
32. Unsupervised Machine Learning Methods
33 AutoML and Dask
34. Neural Network
35. Model Deployment
36. Interpretability of Machine Learning Models
.https://courses.analyticsvidhya.com/courses/applied-machine-learning-beginner-to-professional?utm_source=sendinblue&utm_campaign=July_Newsletter_2019&utm_medium=email
Course Curriculm #course
#ML
1. Welcome to the Applied Machine Learning Course
2. Introduction to Data Science and Machine Learning
3. Introduction to the Course
4. Setting up your system
5. Python for Data Science
6. Statistics For Data Science
7. Basics Steps of Machine Learning and EDA
8. Data Manipulation and Visualization
9. Project: EDA - Customer Churn Analysis
10. Share your Learnings
11. Build Your First Predictive Model
12. Evaluation Metrics
13. Build Your First ML Model: k-NN
14. Selecting the Right Model
15. Linear Models
16. Project: Customer Churn Prediction
17. Dimensionality Reduction (Part I)
18. Decision Tree
19. Feature Engineering
20. Share your Learnings
21. Project: NYC Taxi Trip Duration prediction
22. Working with Text Data
23. Naïve Bayes
24. Multiclass and Multilabel
25. Project: Web Page Classification
26. Basics of Ensemble Techniques
27. Advance Ensemble Techniques
28. Project: Ensemble Model on NYC Taxi Trip Duration Prediction
29. Share your Learnings
30. Advance Dimensionality Reduction
31. Support Vector Machine
32. Unsupervised Machine Learning Methods
33 AutoML and Dask
34. Neural Network
35. Model Deployment
36. Interpretability of Machine Learning Models
.https://courses.analyticsvidhya.com/courses/applied-machine-learning-beginner-to-professional?utm_source=sendinblue&utm_campaign=July_Newsletter_2019&utm_medium=email
@Machine_learn
#NLP #DL #course
New fast.ai course: A Code-First Introduction to Natural Language Processing
https://www.fast.ai/2019/07/08/fastai-nlp/
Github: https://github.com/fastai/course-nlp
Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9
#NLP #DL #course
New fast.ai course: A Code-First Introduction to Natural Language Processing
https://www.fast.ai/2019/07/08/fastai-nlp/
Github: https://github.com/fastai/course-nlp
Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9