Data Science: NLP : Sentiment Analysis - Model Building
What you'll learn
- Data Analysis and Understanding
- Data Preprocessing Techniques
- POS tagging and Lemmatization
- Word Cloud
- TF-IDF Vectorizer
- Model Building for Sentiment Analysis
- Classification Metrics
- Model Evaluation
- Running the model on a local Streamlit Server
- Pushing your notebooks and project files to GitHub repository
- Deploying the project on Heroku Cloud Platform
- Very Basic knowledge of Python and Anaconda
- Familiarity with Github
In this course I will cover, how to develop a Sentiment Analysis model to categorize a tweet as Positive or Negative using NLP techniques and Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.
This course will walk you through the initial data exploration and understanding, data analysis, data pre-processing, data preparation, model building, evaluation and deployment techniques. We will explore NLP concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.
At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.
I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.
Task 1 : Installing Packages.
Task 2 : Importing Libraries.
Task 3 : Loading the data from source.
Task 4 : Understanding the data
Task 5 : Preparing the data for pre-processing
Task 6 : Pre-processing steps overview
Task 7 : Custom Pre-processing functions
Task 8 : About POS tagging and Lemmatization
Task 9 : POS tagging and lemmatization in action.
Task 10 : Creating a word cloud of positive and negative tweets.
Task 11 : Identifying the most frequent set of words in the dataset for positive and negative cases.
Task 12 : Train Test Split
Task 13 : About TF-IDF Vectorizer
Task 14 : TF-IDF Vectorizer in action
Task 15 : About Confusion Matrix
Task 16 : About Classification Report
Task 17 : About AUC-ROC
Task 18 : Creating a common Model Evaluation function
Task 19 : Checking for model performance across a wide range of models
Task 20 : Final Inference and saving the models
Task 21 : Testing the model on unknown datasets
Task 22 : Testing the model on unknown datasets – Excel option
Task 23 : What is Streamlit and Installation steps.
Task 24 : Creating an user interface to interact with our created model.
Task 25 : Running your notebook on Streamlit Server in your local machine.
Task 26 : Pushing your project to GitHub repository.
Task 27 : Project Deployment on Heroku Platform for free.
Data Analysis, NLP techniques, Model Building and Deployment is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of NLP techniques, machine learning and deployment in just a few hours!
You will receive :
1. Certificate of completion from AutomationGig.
2. All the datasets used in the course are in the resources section.
3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.
So what are you waiting for?
Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!
Happy Learning !!
[Music : bensound]
Who this course is for:
- Students and professionals who want to learn Data Analysis, NLP techniques, Data Preparation for Model building, Evaluation and Model Deployment on Cloud.
- Students and professionals who wants to visually interact with their created models.
- Professionals who knows how to create models but wants to deploy their models on cloud platform.
My name is Jay and I am super-psyched that you are reading this!
Professionally, I am a Test Architect and Machine Learning Engineer. I have around 10+ years of experience in field of Test Automation and Data Science and Machine Learning.
I have professional experience of training students in the field of Data Science and Test Automation.
I am absolutely and utterly passionate about Artificial Intelligence and Machine Learning and I am looking forward to sharing my passion and knowledge with you.