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Udemy - Deployment of Machine Learning Models in Production | Python

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Udemy - Deployment of Machine Learning Models in Production | Python

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Files:

030 DistilBERT-App.zip 235.2 MB
TutsNode.com.txt 63 bytes
003 Sentiment-Classification-using-BERT.zip 326.9 KB
069 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip 95.4 KB
060 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip 86.6 KB
068 Congrats! You Have Deployed ML Model in Production.en.srt 24.5 KB
003 DO NOT SKIP IT _ Download Working Files.html 1.8 KB
070 FastText Research Paper Review.en.srt 20.5 KB
041 Deploy DistilBERT Model at Your Local Machine.en.srt 20.1 KB
079 Preparing Prediction APIs.en.srt 20.0 KB
050 Make Your ML Model Accessible to the World.en.srt 17.7 KB
049 Deploy ML Model on EC2 Server.en.srt 17.7 KB
072 Data Preparation.en.srt 17.3 KB
057 Install TensorFlow 2 and KTRAIN.en.srt 16.5 KB
012 BERT Model Training.en.srt 15.1 KB
046 Install TensorFlow 2 and KTRAIN.en.srt 14.7 KB
008 Must Read.html 1.7 KB
019 Number of Characters Distribution in Tweets.en.srt 14.6 KB
016 BERT Intro - Disaster Tweets Dataset Understanding.en.srt 14.2 KB
027 Word Embeddings and Classification with Deep Learning Part 2.en.srt 14.1 KB
058 Create Extra RAM from SSD by Memory Swapping.en.srt 13.7 KB
059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.en.srt 13.7 KB
037 Flask App Preparation.en.srt 2.1 KB
015 Resources Folder.html 926 bytes
0 153 bytes
070 FastText Research Paper Review.mp4 160.1 MB
040 Build Predict API.en.srt 13.6 KB
081 Testing Prediction API at AWS Ubuntu Machine.en.srt 13.5 KB
067 Configuring NGINX with uWSGI, and Flask Server.en.srt 13.5 KB
029 BERT Model Evaluation.en.srt 13.1 KB
075 Creating Fresh Ubuntu Machine.en.srt 13.0 KB
032 Data Preparation.en.srt 12.7 KB
030 What is DistilBERT_.en.srt 12.5 KB
063 Setting Up uWSGI Server.en.srt 12.5 KB
011 Train-Test Split and Preprocess with BERT.en.srt 11.9 KB
083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.en.srt 11.6 KB
033 DistilBERT Model Training.en.srt 11.6 KB
069 What is Multi-Label Classification_.en.srt 11.6 KB
026 Word Embeddings and Classification with Deep Learning Part 1.en.srt 11.3 KB
025 Classification with Word2Vec and SVM.en.srt 11.1 KB
038 Run Your First Flask Application.en.srt 11.0 KB
028 BERT Model Building and Training.en.srt 10.9 KB
051 Install Git Bash and Commander Terminal on Local Computer.en.srt 10.7 KB
014 Saving and Loading Fine Tuned Model.en.srt 10.5 KB
030 Sentiment-Classification-using-DistilBERT.zip 10.5 KB
047 Run Your First Flask Application on AWS EC2.en.srt 10.5 KB
066 Start API Services at System Startup.en.srt 10.0 KB
071 Notebook Setup.en.srt 9.9 KB
076 Setting Python3 and PIP3 Alias.en.srt 9.8 KB
024 Classification with TFIDF and SVM.en.srt 9.8 KB
073 FastText Model Training.en.srt 9.8 KB
078 Making Your Server Ready.en.srt 9.7 KB
080 Testing Prediction API at Local Machine.en.srt 9.6 KB
082 Configuring uWSGI Server.en.srt 9.6 KB
042 Create AWS Account.en.srt 9.4 KB
052 Create AWS Account.en.srt 9.4 KB
044 Connect EC2 Instance from Windows 10.en.srt 9.3 KB
061 Virtual Environment Setup.en.srt 9.2 KB
062 Setting Up Flask Server.en.srt 9.1 KB
[TGx]Downloaded from torrentgalaxy.to .txt 585 bytes
054 Connect AWS Ubuntu (Linux) from Windows Computer.en.srt 9.1 KB
064 Installing TensorFlow 2 and KTRAIN.en.srt 8.9 KB
021 Most and Least Common Words.en.srt 8.7 KB
018 Target Class Distribution.en.srt 8.6 KB
004 What is BERT.en.srt 8.5 KB
020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.en.srt 8.4 KB
043 Create Free Windows EC2 Instance.en.srt 7.9 KB
055 Install PIP3 on AWS Ubuntu.en.srt 7.6 KB
039 Predict Sentiment at Your Local Machine.en.srt 7.2 KB
074 FastText Model Evaluation and Saving at Google Drive.en.srt 7.1 KB
031 Notebook Setup.en.srt 7.1 KB
013 Testing Fine Tuned BERT Model.en.srt 7.0 KB
034 Save Model at Google Drive.en.srt 7.0 KB
036 Download Fine Tuned DistilBERT Model.en.srt 2.0 KB
006 Going Deep Inside ktrain Package.en.srt 6.9 KB
009 Installing ktrain.en.srt 6.8 KB
005 What is ktrain.en.srt 6.8 KB
060 NGINX Introduction.en.srt 6.7 KB
010 Loading Dataset.en.srt 6.5 KB
022 One-Shot Data Cleaning.en.srt 6.2 KB
053 Launch Ubuntu Machine on EC2.en.srt 6.2 KB
001 Welcome.en.srt 6.2 KB
048 Transfer DistilBERT Model to EC2 Flask Server.en.srt 6.0 KB
065 Configuring uWSGI Server.en.srt 6.0 KB
002 Introduction.en.srt 6.0 KB
023 Disaster Words Visualization with Word Cloud.en.srt 5.9 KB
077 Creating 4GB Extra RAM by Memory Swapping.en.srt 5.6 KB
017 Download Dataset.en.srt 5.5 KB
035 Model Evaluation.en.srt 4.6 KB
069 FastText-Multi-Label-Text-Classification.zip 4.5 KB
045 Install Python on EC2 Windows 10.en.srt 4.3 KB
056 Update and Upgrade Your Ubuntu Packages.en.srt 3.5 KB
007 Notebook Setup.en.srt 3.2 KB
1 385.5 KB
016 BERT Intro - Disaster Tweets Dataset Understanding.mp4 109.8 MB
2 203.9 KB
063 Setting Up uWSGI Server.mp4 101.7 MB
3 262.3 KB
057 Install TensorFlow 2 and KTRAIN.mp4 93.6 MB
4 425.4 KB
067 Configuring NGINX with uWSGI, and Flask Server.mp4 91.8 MB
5 224.5 KB
068 Congrats! You Have Deployed ML Model in Production.mp4 84.9 MB
6 98.7 KB
058 Create Extra RAM from SSD by Memory Swapping.mp4 83.7 MB
7 287.2 KB
019 Number of Characters Distribution in Tweets.mp4 83.5 MB
8 483.7 KB
079 Preparing Prediction APIs.mp4 80.8 MB
9 244.5 KB
081 Testing Prediction API at AWS Ubuntu Machine.mp4 77.5 MB
10 562.3 KB
078 Making Your Server Ready.mp4 76.5 MB
11 524.5 KB
030 What is DistilBERT_.mp4 74.1 MB
12 969.5 KB
027 Word Embeddings and Classification with Deep Learning Part 2.mp4 73.6 MB
13 438.7 KB
049 Deploy ML Model on EC2 Server.mp4 71.0 MB
14 1.9 KB
041 Deploy DistilBERT Model at Your Local Machine.mp4 69.5 MB
15 544.1 KB
072 Data Preparation.mp4 67.4 MB
16 594.7 KB
050 Make Your ML Model Accessible to the World.mp4 66.8 MB
17 197.7 KB
046 Install TensorFlow 2 and KTRAIN.mp4 66.6 MB
18 436.3 KB
075 Creating Fresh Ubuntu Machine.mp4 59.3 MB
19 717.0 KB
083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.mp4 58.6 MB
20 384.8 KB
029 BERT Model Evaluation.mp4 58.4 MB
21 581.3 KB
082 Configuring uWSGI Server.mp4 58.3 MB
22 743.9 KB
066 Start API Services at System Startup.mp4 58.1 MB
23 880.0 KB
061 Virtual Environment Setup.mp4 57.7 MB
24 311.1 KB
012 BERT Model Training.mp4 56.8 MB
25 166.5 KB
040 Build Predict API.mp4 56.2 MB
26 838.5 KB
064 Installing TensorFlow 2 and KTRAIN.mp4 56.1 MB
27 944.4 KB
028 BERT Model Building and Training.mp4 55.1 MB
28 875.2 KB
032 Data Preparation.mp4 54.6 MB
29 402.6 KB
025 Classification with Word2Vec and SVM.mp4 52.9 MB
30 108.0 KB
026 Word Embeddings and Classification with Deep Learning Part 1.mp4 52.9 MB
31 130.7 KB
044 Connect EC2 Instance from Windows 10.mp4 52.5 MB
32 525.2 KB
011 Train-Test Split and Preprocess with BERT.mp4 51.4 MB
33 585.2 KB
062 Setting Up Flask Server.mp4 50.7 MB
34 269.4 KB
076 Setting Python3 and PIP3 Alias.mp4 49.3 MB
35 700.7 KB
043 Create Free Windows EC2 Instance.mp4 47.7 MB
36 332.1 KB
071 Notebook Setup.mp4 45.8 MB
37 248.1 KB
004 What is BERT.mp4 45.3 MB
38 738.3 KB
055 Install PIP3 on AWS Ubuntu.mp4 44.6 MB
39 397.9 KB
059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.mp4 44.2 MB
40 819.9 KB
024 Classification with TFIDF and SVM.mp4 44.2 MB
41 835.9 KB
021 Most and Least Common Words.mp4 43.4 MB
42 628.8 KB
001 Welcome.mp4 42.6 MB
43 414.7 KB
023 Disaster Words Visualization with Word Cloud.mp4 42.2 MB
44 863.5 KB
033 DistilBERT Model Training.mp4 41.6 MB
45 413.5 KB
020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.mp4 41.0 MB
46 24 bytes
051 Install Git Bash and Commander Terminal on Local Computer.mp4 40.9 MB
47 83.2 KB
080 Testing Prediction API at Local Machine.mp4 40.2 MB
48 802.8 KB
073 FastText Model Training.mp4 38.6 MB
49 387.5 KB
077 Creating 4GB Extra RAM by Memory Swapping.mp4 37.0 MB
50 993.2 KB
052 Create AWS Account.mp4 36.6 MB
51 382.2 KB
042 Create AWS Account.mp4 36.6 MB
52 387.7 KB
060 NGINX Introduction.mp4 36.6 MB
53 390.2 KB
002 Introduction.mp4 35.8 MB
54 253.5 KB
065 Configuring uWSGI Server.mp4 32.9 MB
55 145.2 KB
005 What is ktrain.mp4 32.8 MB
56 167.7 KB
069 What is Multi-Label Classification_.mp4 32.7 MB
57 265.8 KB
054 Connect AWS Ubuntu (Linux) from Windows Computer.mp4 32.5 MB
58 462.7 KB
038 Run Your First Flask Application.mp4 32.4 MB
59 633.6 KB
022 One-Shot Data Cleaning.mp4 32.0 MB
60 993.3 KB
018 Target Class Distribution.mp4 31.5 MB
61 536.5 KB
053 Launch Ubuntu Machine on EC2.mp4 31.4 MB
62 625.6 KB
006 Going Deep Inside ktrain Package.mp4 31.3 MB
63 700.1 KB
009 Installing ktrain.mp4 29.9 MB
64 57.0 KB
017 Download Dataset.mp4 29.7 MB
65 278.8 KB
047 Run Your First Flask Application on AWS EC2.mp4 29.1 MB
66 889.3 KB
014 Saving and Loading Fine Tuned Model.mp4 25.5 MB
67 552.9 KB
048 Transfer DistilBERT Model to EC2 Flask Server.mp4 24.4 MB
68 571.1 KB
031 Notebook Setup.mp4 24.4 MB
69 639.2 KB
034 Save Model at Google Drive.mp4 22.8 MB
70 246.9 KB
039 Predict Sentiment at Your Local Machine.mp4 21.9 MB
71 125.8 KB
013 Testing Fine Tuned BERT Model.mp4 21.0 MB
72 980.1 KB
010 Loading Dataset.mp4 20.2 MB
73 792.1 KB
074 FastText Model Evaluation and Saving at Google Drive.mp4 19.9 MB
74 73.3 KB
056 Update and Upgrade Your Ubuntu Packages.mp4 19.9 MB
75 131.4 KB
069 FastText-App.zip 18.5 MB
76 475.0 KB
045 Install Python on EC2 Windows 10.mp4 15.8 MB
77 223.7 KB
035 Model Evaluation.mp4 14.9 MB
78 95.4 KB
007 Notebook Setup.mp4 7.2 MB
79 868.3 KB
037 Flask App Preparation.mp4 6.2 MB
80 776.8 KB
036 Download Fine Tuned DistilBERT Model.mp4 4.9 MB
81 110.0 KB
015 Fine-Tuning-BERT-for-Disaster-Tweets-Classification.zip 2.5 MB

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