DesireCourse Net Udemy - Deploy Machine Learning & NLP Models with Dockers DevOps

1. 1. Course Overview/1. Introduction.mp410.46MB
2. 1. Course Overview/2. I have a model. Now what.mp46.09MB
3. 1. Course Overview/3. Skills Checklist.mp47.41MB
4. 1. Course Overview/4. Learning Goals.mp44.3MB
5. 2. Docker basics/1. Why docker.mp420.39MB
6. 2. Docker basics/2. What are docker containers.mp412.05MB
7. 2. Docker basics/3. Importance of docker containers in machine learning.mp414.79MB
8. 2. Docker basics/4. Where devops meets data science.mp411.62MB
9. 2. Docker basics/5. Summary.mp42.05MB
10. 3. Flask basics/1. Introduction.mp44.09MB
11. 3. Flask basics/2. Setting up a Flask Project.mp49.19MB
12. 3. Flask basics/3. Simple Flask API to add two numbers.mp436.45MB
13. 3. Flask basics/4. Taking user input with GET requests.mp433.46MB
14. 3. Flask basics/5. POST request with Flask.mp447.95MB
15. 3. Flask basics/6. Using Flask in the context of Machine Learning.mp430.27MB
16. 4. Exposing a Random Forest Machine Learning service as an API/1. Introduction.mp43.94MB
17. 4. Exposing a Random Forest Machine Learning service as an API/2. API & Dataset Overview.mp47.58MB
18. 4. Exposing a Random Forest Machine Learning service as an API/3. Training the Random Forest model.mp445.78MB
19. 4. Exposing a Random Forest Machine Learning service as an API/4. Pickling the Random Forest model.mp433.86MB
20. 4. Exposing a Random Forest Machine Learning service as an API/5. Exposing the Random Forest model as a Flask API.mp454.26MB
21. 4. Exposing a Random Forest Machine Learning service as an API/6. Testing the API model.mp436.61MB
22. 4. Exposing a Random Forest Machine Learning service as an API/7. Providing file input to Flask API.mp475.71MB
23. 4. Exposing a Random Forest Machine Learning service as an API/8. Flasgger for autogenerating UI.mp485.17MB
24. 4. Exposing a Random Forest Machine Learning service as an API/9. Summary.mp413.49MB
25. 5. Writing and building the Dockerfile/1. Introduction.mp42.22MB
26. 5. Writing and building the Dockerfile/2. Base Image & FROM command.mp414.76MB
27. 5. Writing and building the Dockerfile/3. COPY and EXPOSE commands.mp421.74MB
28. 5. Writing and building the Dockerfile/4. WORKDIR, RUN and CMD commands.mp429.55MB
29. 5. Writing and building the Dockerfile/5. Preparing the flask scripts for dockerizing.mp421.59MB
30. 5. Writing and building the Dockerfile/6. Writing the Dockerfile.mp445.99MB
31. 5. Writing and building the Dockerfile/7. Building the docker image.mp474.79MB
32. 5. Writing and building the Dockerfile/8. Running the Random Forest model on Docker.mp478.73MB
33. 6. Building a production grade Docker application/1. Introduction.mp426.52MB
34. 6. Building a production grade Docker application/2. Overall Architecture.mp423.62MB
35. 6. Building a production grade Docker application/3. Configuring the WSGI file.mp462.43MB
36. 6. Building a production grade Docker application/4. Writing a production grade Dockerfile.mp461.41MB
37. 6. Building a production grade Docker application/5. Running and debugging a docker container in production.mp486.01MB
38. 7. Building NLP based Text Clustering application/1. Introduction.mp418.13MB
39. 7. Building NLP based Text Clustering application/2. Stemming & Lemmatization for cleaner text.mp490.87MB
40. 7. Building NLP based Text Clustering application/3. Converting unstructured to structured data.mp495.68MB
41. 7. Building NLP based Text Clustering application/4. KMeans Clustering.mp482.2MB
42. 7. Building NLP based Text Clustering application/5. Preparing the excel output.mp4108.84MB
43. 7. Building NLP based Text Clustering application/6. Making the output Downloadable.mp465.5MB
44. 7. Building NLP based Text Clustering application/7. Finding top keywords for kmeans clusters.mp474.96MB
45. 7. Building NLP based Text Clustering application/8. Final output with charts.mp476.39MB
46. 7. Building NLP based Text Clustering application/9. Summary.mp416.46MB
47. 8. API for image recognition with deep learning/1. Introduction.mp45.07MB
48. 8. API for image recognition with deep learning/2. Visualizing the input images.mp468.33MB
49. 8. API for image recognition with deep learning/3. Preparing the input images.mp489.54MB
50. 8. API for image recognition with deep learning/4. Building the deep learning model.mp493.14MB
51. 8. API for image recognition with deep learning/5. Training and saving the trained deep learning model.mp431.02MB
52. 8. API for image recognition with deep learning/6. Generating test images.mp434.23MB
53. 8. API for image recognition with deep learning/7. Flask API wrapper for making predictions.mp478.29MB
54. 8. API for image recognition with deep learning/8. Summary.mp418.42MB