DesireCourse Net Udemy Deploy Machine Learning NLP Models with Dockers DevOps

DesireCourse Net Udemy Deploy Machine Learning NLP Models with Dockers DevOps

Name	[DesireCourse.Net] Udemy - Deploy Machine Learning & NLP Models with Dockers (DevOps)	DOWNLOAD Copy Link
Total Size	2.3 GB
Total Files	129
Hash	378F2FDE48C99A7D0EB5BBC012A5EA0588422D5E

/1. Course Overview/
1. Introduction.mp4	11.0 MB
1. Introduction.vtt	1.1 KB
1.1 Course Overview.pdf.pdf	963.0 KB
2. I have a model. Now what.mp4	6.4 MB
2. I have a model. Now what.vtt	1.3 KB
3. Skills Checklist.mp4	7.8 MB
3. Skills Checklist.vtt	1.8 KB
4. Learning Goals.mp4	4.5 MB
4. Learning Goals.vtt	0.7 KB
Must Read.txt	0.5 KB
Visit Coursedrive.org.url	0.1 KB
/2. Docker basics/
1. Why docker.mp4	21.4 MB
1. Why docker.vtt	1.8 KB
1.1 Docker basics.pdf.pdf	853.5 KB
2. What are docker containers.mp4	12.6 MB
2. What are docker containers.mp4.jpg	71.7 KB
2. What are docker containers.txt	0.2 KB
2. What are docker containers.vtt	2.3 KB
3. Importance of docker containers in machine learning.mp4	15.5 MB
3. Importance of docker containers in machine learning.vtt	3.1 KB
4. Where devops meets data science.mp4	12.2 MB
4. Where devops meets data science.vtt	1.0 KB
5. Summary.mp4	2.2 MB
5. Summary.vtt	0.4 KB
/3. Flask basics/
1. Introduction.mp4	4.3 MB
1. Introduction.vtt	0.6 KB
2. Setting up a Flask Project.mp4	9.6 MB
2. Setting up a Flask Project.vtt	1.5 KB
2.1 flask1.py.py	0.4 KB
3. Simple Flask API to add two numbers.mp4	38.2 MB
3. Simple Flask API to add two numbers.vtt	3.4 KB
4. Taking user input with GET requests.mp4	35.1 MB
4. Taking user input with GET requests.vtt	3.5 KB
5. POST request with Flask.mp4	50.3 MB
5. POST request with Flask.vtt	5.2 KB
6. Using Flask in the context of Machine Learning.mp4	31.7 MB
6. Using Flask in the context of Machine Learning.vtt	3.1 KB
/4. Exposing a Random Forest Machine Learning service as an API/
1. Introduction.mp4	4.1 MB
1. Introduction.vtt	0.7 KB
2. API & Dataset Overview.mp4	8.0 MB
2. API & Dataset Overview.vtt	1.0 KB
2.1 flask_predict_train.py.py	1.0 KB
3. Training the Random Forest model.mp4	48.0 MB
3. Training the Random Forest model.vtt	4.4 KB
4. Pickling the Random Forest model.mp4	35.5 MB
4. Pickling the Random Forest model.vtt	2.5 KB
5. Exposing the Random Forest model as a Flask API.mp4	56.9 MB
5. Exposing the Random Forest model as a Flask API.vtt	4.4 KB
5.1 flask_predict_api.py.py	1.7 KB
6. Testing the API model.mp4	38.4 MB
6. Testing the API model.vtt	3.5 KB
7. Providing file input to Flask API.mp4	79.4 MB
7. Providing file input to Flask API.vtt	5.8 KB
8. Flasgger for autogenerating UI.mp4	89.3 MB
8. Flasgger for autogenerating UI.vtt	8.4 KB
9. Summary.mp4	14.2 MB
9. Summary.vtt	1.9 KB
/5. Writing and building the Dockerfile/
1. Introduction.mp4	2.3 MB
1. Introduction.vtt	0.8 KB
2. Base Image & FROM command.mp4	15.5 MB
2. Base Image & FROM command.vtt	3.3 KB
2.1 Docker sample.zip.zip	9.1 KB
3. COPY and EXPOSE commands.mp4	22.8 MB
3. COPY and EXPOSE commands.vtt	3.5 KB
4. WORKDIR, RUN and CMD commands.mp4	31.0 MB
4. WORKDIR, RUN and CMD commands.vtt	3.9 KB
5. Preparing the flask scripts for dockerizing.mp4	22.6 MB
5. Preparing the flask scripts for dockerizing.vtt	1.7 KB
6. Writing the Dockerfile.mp4	48.2 MB
6. Writing the Dockerfile.vtt	8.3 KB
7. Building the docker image.mp4	78.4 MB
7. Building the docker image.vtt	7.9 KB
8. Running the Random Forest model on Docker.mp4	82.6 MB
8. Running the Random Forest model on Docker.vtt	8.3 KB
/6. Building a production grade Docker application/
1. Introduction.mp4	27.8 MB
1. Introduction.vtt	4.4 KB
2. Overall Architecture.mp4	24.8 MB
2. Overall Architecture.vtt	4.4 KB
3. Configuring the WSGI file.mp4	65.5 MB
3. Configuring the WSGI file.vtt	7.6 KB
3.1 Docker deployment.zip.zip	11.8 KB
4. Writing a production grade Dockerfile.mp4	64.4 MB
4. Writing a production grade Dockerfile.vtt	7.1 KB
5. Running and debugging a docker container in production.mp4	90.2 MB
5. Running and debugging a docker container in production.vtt	10.3 KB
6. Docker Quiz 1 – Basic Concepts, Commands.html	0.2 KB
/7. Building NLP based Text Clustering application/
1. Introduction.mp4	19.0 MB
1. Introduction.vtt	2.1 KB
10. Dockerizing the text clustering app.html	0.2 KB
2. Stemming & Lemmatization for cleaner text.mp4	95.3 MB
2. Stemming & Lemmatization for cleaner text.vtt	10.1 KB
2.1 text_cluster_api.py.py	3.7 KB
3. Converting unstructured to structured data.mp4	100.3 MB
3. Converting unstructured to structured data.mp4.jpg	79.3 KB
3. Converting unstructured to structured data.txt	0.2 KB
3. Converting unstructured to structured data.vtt	9.1 KB
4. KMeans Clustering.mp4	86.2 MB
4. KMeans Clustering.vtt	7.2 KB
5. Preparing the excel output.mp4	114.1 MB
5. Preparing the excel output.vtt	9.9 KB
6. Making the output Downloadable.mp4	68.7 MB
6. Making the output Downloadable.vtt	5.2 KB
7. Finding top keywords for kmeans clusters.mp4	78.6 MB
7. Finding top keywords for kmeans clusters.vtt	5.7 KB
8. Final output with charts.mp4	80.1 MB
8. Final output with charts.vtt	5.7 KB
9. Summary.mp4	17.3 MB
9. Summary.vtt	1.6 KB
/8. API for image recognition with deep learning/
1. Introduction.mp4	5.3 MB
1. Introduction.vtt	0.8 KB
2. Visualizing the input images.mp4	71.7 MB
2. Visualizing the input images.vtt	6.4 KB
2.1 img_reco_train.py.py	2.0 KB
3. Preparing the input images.mp4	93.9 MB
3. Preparing the input images.vtt	7.8 KB
4. Building the deep learning model.mp4	97.7 MB
4. Building the deep learning model.vtt	10.3 KB
5. Training and saving the trained deep learning model.mp4	32.5 MB
5. Training and saving the trained deep learning model.vtt	3.0 KB
6. Generating test images.mp4	35.9 MB
6. Generating test images.vtt	3.2 KB
6.1 img_reco_test.py.py	1.1 KB
7. Flask API wrapper for making predictions.mp4	82.1 MB
7. Flask API wrapper for making predictions.vtt	6.5 KB
8. Summary.mp4	19.3 MB
8. Summary.vtt	2.3 KB
9. Dockerizing the deep learning app.html	0.2 KB
/
[CourseClub.Me].url	0.0 KB
[DesireCourse.Net].url	0.1 KB
Total files 129