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[ TutSala com ] Udemy - Data Science - Sentiment Analysis - Model Building Deployment
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1. Introduction and Getting Started
1. Project Overview.mp4
1. Project Overview.srt
2. About NLP and Sentiment Analysis.mp4
2. About NLP and Sentiment Analysis.srt
3. High Level Overview of the steps to be performed.mp4
3. High Level Overview of the steps to be performed.srt
4. Installing Packages.mp4
4. Installing Packages.srt
10. Deploying the project on Heroku Platform
1. Updating your Project directory.mp4
1. Updating your Project directory.srt
2. Pushing your code to Github repository.mp4
2. Pushing your code to Github repository.srt
3. Project deployment on Heroku Platform.mp4
3. Project deployment on Heroku Platform.srt
11. Project Files and Code
1. Full Project Code.html
sentiment-analysis-app
Procfile
README.md
app.py
data
sentiment.jpg
testdata.csv
training.1600000.processed.noemoticon.csv
gitignore
models
Sentiment-LR.pickle
vectoriser.pickle
nltk.txt
notebooks
Sentiment_Analysis_Tweet_LemmatizingWithPOS.ipynb
ipynb_checkpoints
Sentiment_Analysis_Tweet_DefaultLemmatizer-checkpoint.ipynb
Sentiment_Analysis_Tweet_LemmatizingWithPOS-checkpoint.ipynb
requirements.txt
setup.sh
2. Data Understanding, Exploration & Preparation
1. Importing Libraries.mp4
1. Importing Libraries.srt
2. Loading the data from source.mp4
2. Loading the data from source.srt
3. Understanding the data.mp4
3. Understanding the data.srt
4. Preparing the data for pre-processing.mp4
4. Preparing the data for pre-processing.srt
3. Data Pre-processing
1. Pre-processing steps overview.mp4
1. Pre-processing steps overview.srt
2. Custom Pre-processing functions.mp4
2. Custom Pre-processing functions.srt
3. About POS tagging and Lemmatization.mp4
3. About POS tagging and Lemmatization.srt
4. POS tagging and lemmatization in action.mp4
4. POS tagging and lemmatization in action.srt
4. Data Analysis
1. Creating a word cloud of positive and negative tweets.mp4
1. Creating a word cloud of positive and negative tweets.srt
2. Most frequent set of words in the dataset for positive and negative cases.mp4
2. Most frequent set of words in the dataset for positive and negative cases.srt
5. Data Preparation
1. Train Test Split.mp4
1. Train Test Split.srt
2. About TF-IDF Vectorizer.mp4
2. About TF-IDF Vectorizer.srt
3. TF-IDF Vectorizer in action.mp4
3. TF-IDF Vectorizer in action.srt
6. Classification Metrics
1. About Confusion Matrix.mp4
1. About Confusion Matrix.srt
2. About Classification Report.mp4
2. About Classification Report.srt
3. About AUC-ROC.mp4
3. About AUC-ROC.srt
7. Model Building and Evaluation
1. Creating a common Model Evaluation function.mp4
1. Creating a common Model Evaluation function.srt
2. Checking for model performance across a wide range of models.mp4
2. Checking for model performance across a wide range of models.srt
3. Final Inference and saving the models.mp4
3. Final Inference and saving the models.srt
8. Model in Action
1. Testing the model on unknown datasets.mp4
1. Testing the model on unknown datasets.srt
2. Testing the model on unknown datasets – Excel option.mp4
2. Testing the model on unknown datasets – Excel option.srt
9. Running the model on a local Server
1. What is Streamlit and Installation steps.mp4
1. What is Streamlit and Installation steps.srt
2. Creating an user interface to interact with our created model.mp4
2. Creating an user interface to interact with our created model.srt
3. Running the model on Local Streamlit Server.mp4
3. Running the model on Local Streamlit Server.srt
Bonus Resources.txt
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