What you'll Learn
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Build a complete sentiment classification pipeline using Goodreads Reviews, from data cleaning to predictions.
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Leverage DistilBERT for efficient, high-performance sentiment analysis training.
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Seamlessly manage complex workflows using Apache Airflow to orchestrate the entire process.
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Create an intuitive interface with Streamlit to display sentiment predictions, all running locally for simplicity.
Who Should Enroll?
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Professionals: Ideal for data scientists and AI practitioners, this course covers NLP model training, workflow orchestration with Apache Airflow, and real-time deployment using Streamlit. Build a complete sentiment analysis pipeline with ease.
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Aspiring Students: Perfect for students exploring machine learning and NLP. Gain hands-on experience with DistilBERT, sentiment analysis, and workflow automation in a beginner-friendly setup—no cloud required.
About the Instructor
Priyanka Asnani, Senior Machine Learning Engineer at Fidelity Investments
Priyanka is a Senior Machine Learning Engineer at Fidelity Investments with over 7 years of experience. She specializes in building end-to-end machine learning pipelines, focusing on recommender and ranking systems. Her expertise spans large language models, deep learning, and time-series forecasting. Priyanka excels at applying machine learning techniques to solve complex problems across industries. She is an active community contributor who shares her knowledge through public speaking, webinars, and technical content, helping aspiring data scientists stay updated with industry trends.
FAQ's
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What is Apache Airflow, and why is it used in this course?
Apache Airflow is a workflow orchestration tool that automates and manages tasks efficiently. This course will help to streamline the sentiment classification pipeline.
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What is the role of Streamlit in this project?
Streamlit is used to create a user-friendly web interface, allowing you to interact with the sentiment classifier and visualize predictions easily.
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Will I receive a certificate upon completing the course?
Yes, the course provides a certification upon completion.
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Will there be hands-on projects?
Yes! This is a practical, project-based course where you will build and deploy an end-to-end sentiment classification pipeline.
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What programming languages and tools will be used?
You’ll work with Python, TensorFlow/PyTorch (for DistilBERT), Apache Airflow, and Streamlit to build the sentiment classification pipeline.