Generative AI for Data Science from Coursera

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Syllabus

Generative AI for Data Science

Upon completing this course, you will be proficient in harnessing the transformative capabilities of generative AI (GenAI) within the data science landscape, specifically in marketing and advertising. Additionally, you will explore the ethical and operational implications of GenAI in data science. By the end of the course, you will be equipped to integrate the innovative potentials of GenAI technologies into your practices, effectively balancing innovation with integrity.

Lesson 1: Versatility and Impact of GenAI in Data Science

By the end of this lesson, you will understand how Generative AI is transforming Data Science. We'll explore how these models identify data patterns to create original content, improve fluorescence microscopy by reducing cell damage, enhance anomaly detection in datasets, and revolutionize SMS marketing to keep brand consistency. This lesson will show the wide applications and benefits of Generative AI in various data science challenges.

Lesson 2: Running and deploying an LLM locally for Data Science:

By the end of this lesson, you will learn about the applications and benefits of Generative AI in data science, especially for optimizing local LLM (Large Language Model) deployments. We'll cover the advantages of running models locally, such as faster iteration speeds, and the computational demands of large models. You'll also learn about quantization techniques to enhance training and reduce memory usage, as well as the LoRA technique for fine-tuning. Finally, you'll see a practical demo of fine-tuning an open-source model using both LoRA and quantization, giving you practical skills to improve AI model efficiency locally.

Lesson 3: Feature Engineering with High Performance SMS Campaign Data

By the end of this lesson, you will learn how generative AI improves feature engineering in SMS campaign data. This AI automates the extraction of complex patterns and relationships, making it more efficient and powerful than traditional manual methods. We'll also discuss how previous techniques required extensive domain expertise and often lacked scalability and adaptability. Additionally, you'll get a tutorial on using a generative AI model to automatically label different parts of SMS campaign messages with a step-by-step code walkthrough in Python. This approach will show you how generative AI transforms raw data into actionable insights for better campaign management.

Lesson 4: Ethical Considerations of GenAI

By the end of this lesson, you will be able to analyze the security and privacy impacts of Generative AI in data science. We'll explore ethical issues like data privacy, consent, and bias, and discuss how to develop and deploy AI responsibly. You'll learn about creating synthetic data using methods like differential privacy and data anonymization to ensure ethical compliance. This lesson aims to help you make responsible decisions and think critically about ethical issues in AI applications, preparing you to handle complex challenges in data science.

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Suitable for data scientists and AI enthusiasts seeking to enhance their skills in generative AI and its applications

Provides practical, industry-specific applications of generative AI in data science

Leverages the expertise of Microsoft, a leading technology company in the field of AI

Requires a solid foundation in Python, machine learning principles, and data science concepts

Covers ethical considerations and operational implications of generative AI in data science

May require additional resources for learners who are new to the field of generative AI

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI for Data Science with these activities:

Review basic Python syntax and concepts

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Refresh your understanding of Python syntax and programming concepts to enhance your starting point in the course.

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Review tutorials or documentation on basic Python syntax.
Complete practice exercises on data types, variables, and control flow.
Work through examples of simple Python programs to reinforce your understanding.

Organize and Review Course Materials for Future Reference

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Organizing and reviewing your course materials will help you retain information better and make it easier to find what you need when you need it

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Create a system for organizing your notes, assignments, and other materials
Regularly review your materials to reinforce your learning

Explore online tutorials on building and deploying generative AI models

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Supplement your learning by following guided tutorials to build and deploy generative AI models.

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Identify online tutorials from reputable sources covering generative AI model development.
Follow the tutorials step-by-step, implementing the techniques and algorithms.
Experiment with different parameters and settings to observe the impact on model performance.

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Read 'Interpretable Machine Learning' by Christoph Molnar

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Reading this book will provide you with a solid foundation in interpretable machine learning techniques, including SHAP, LIME, and ICE, which are essential for understanding and explaining the predictions of AI models.

View Interpretable Machine Learning on Amazon

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Read through the key concepts of interpretable machine learning
Complete the end-of-chapter exercises to test your understanding
Apply the techniques you learn to your own data science projects

Follow along with a tutorial on data cleansing and preprocessing for generative AI models

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Improve your understanding of the crucial steps involved in preparing data for generative AI, ensuring your models are trained on high-quality and relevant data.

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Identify a comprehensive tutorial that covers data cleansing and preprocessing techniques for generative AI
Set aside dedicated time to follow the tutorial step-by-step, practicing the techniques on a suitable dataset
Experiment with different data cleaning and preprocessing methods to optimize your model's performance

Complete coding exercises on generative AI techniques

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Strengthen your programming skills and understanding of generative AI algorithms through practice.

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Find coding exercises or challenges related to generative AI techniques.
Implement the algorithms in Python or another programming language.
Test and debug your code to ensure it produces the expected results.

Create a simple data augmentation pipeline

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Develop a practical understanding of data augmentation techniques by building your own pipeline.

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Choose a dataset and identify augmentation techniques suitable for it.
Implement the data augmentation pipeline using Python libraries.
Evaluate the effectiveness of your pipeline on model performance.

Step-by-Step Guide to Building a Generative AI Model in Python

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Following this guide will give you hands-on experience in building your own generative AI model using Python, providing you with a deeper understanding of the process and the challenges involved.

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Set up your Python environment and install the necessary libraries
Load and preprocess your data
Train your generative AI model
Evaluate your model's performance

Complete Generative AI Coding Challenges on LeetCode

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Practicing coding challenges on LeetCode will help you improve your programming skills and reinforce your understanding of Generative AI concepts

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Sign up for a LeetCode account
Filter for Generative AI-related problems
Attempt to solve the problems on your own
Review the solutions and learn from your mistakes

Practice using Generative AI to enhance data augmentation for machine learning

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Solidify your understanding of how Generative AI can be leveraged to generate synthetic data, expanding your training datasets and improving model performance.

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Choose a suitable dataset and identify areas where data augmentation can be beneficial
Select a generative AI model and experiment with different hyperparameters to generate synthetic data
Incorporate the synthetic data into your machine learning pipeline and evaluate the impact on model performance

Engage in peer discussions on the ethical implications of Generative AI

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Foster critical thinking and ethical awareness by actively participating in discussions with peers, exploring the potential risks and benefits of Generative AI and its implications on society.

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Identify a relevant peer group or discussion forum focused on Generative AI ethics
Actively engage in discussions, sharing your perspectives and listening attentively to diverse viewpoints
Conduct additional research on specific ethical concerns related to Generative AI to inform your contributions

Present an overview of Generative AI use cases

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Deepen your understanding of Generative AI applications by creating a presentation that highlights its practical uses.

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Research and gather information about various Generative AI use cases across industries.
Structure your presentation to clearly explain the benefits and limitations of each use case.
Create visual aids such as charts, graphs, or examples to illustrate your points.

Develop a content piece showcasing the applications of Generative AI in data science

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Enhance your comprehension by creating a comprehensive content piece, such as a blog post or presentation, that explores the practical applications of Generative AI within the field of data science.

Browse courses on Generative AI Applications

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Conduct thorough research on the applications of Generative AI in data science, identifying specific use cases and examples
Determine the target audience and choose a suitable format for your content, such as a blog post, presentation, or infographic
Develop engaging and informative content that showcases the benefits and potential of Generative AI in data science
Share your content with the intended audience and engage in discussions to enhance your understanding

Develop a Case Study on Using Generative AI for Data Augmentation

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Creating a case study will allow you to apply your knowledge of generative AI to a real-world problem, demonstrating your understanding of the technology's capabilities and limitations.

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Identify a business problem that can be solved using data augmentation
Collect and prepare a dataset for your project
Train a generative AI model to augment your data
Evaluate the performance of your augmented data
Write up your case study, including your findings and recommendations

Build a Generative AI Application for Text Summarization

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Building a project will allow you to apply your knowledge of generative AI to a practical problem, demonstrating your ability to use the technology to solve real-world challenges

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Define the scope of your project
Gather and prepare your data
Train a generative AI model for text summarization
Build a user interface for your application
Deploy your application and test its performance

Contribute to an open-source generative AI project

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Gain practical experience and contribute to the generative AI community by participating in open-source projects.

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Identify open-source generative AI projects aligned with your interests and skills.
Review the project documentation and identify areas where you can contribute.
Submit bug reports, feature requests, or code contributions to the project.

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Learners who complete Generative AI for Data Science will develop knowledge and skills that may be useful to these careers:

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Generative AI for Data Science

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