Data Science with Generative AI

The Data Science with Generative AI Program is a cutting-edge course designed to equip learners with a comprehensive understanding of data science, machine learning, and the rapidly growing field of Generative AI. This program combines core data science skills with the power of Generative AI techniques to build intelligent systems that can create, analyze, and transform data in innovative ways. Participants will learn to leverage advanced deep learning frameworks like TensorFlow, explore machine learning models, and gain practical experience through hands-on projects, all while building a strong foundation in programming and statistical analysis.

By the end of the course, you will have the skills to develop and deploy sophisticated AI-driven applications, such as text generation, sentiment analysis, and predictive modeling, making you ready for the evolving demands of the AI industry.

Key Skills and Outcomes:

• Master the fundamentals of Data Science and AI, including key programming languages like Python and R.
• Understand and apply Generative AI techniques in real-world scenarios.
• Build and train neural networks from scratch using advanced deep learning concepts.
• Gain proficiency in machine learning algorithms and their application in AI-driven solutions.
• Learn how to preprocess data for AI and Natural Language Processing (NLP) tasks.
• Complete a Capstone Project showcasing your expertise in Generative AI applications.

Target Audience:
This program is suitable for aspiring data scientists, machine learning engineers, AI enthusiasts, and professionals looking to enhance their expertise in generative AI. A foundational knowledge of programming is recommended but not mandatory.

Course Content:

• Introduction to Data Science and AI: Learn the fundamentals of Data Science, its role in various industries, and its integration with Artificial Intelligence (AI) to drive business intelligence, automation, and predictive insights.
• Programming Essentials for AI with Python: Dive into Python, one of the most widely used languages in AI, to master essential programming skills required for AI model development and data manipulation.
• SQL: Data Handling for AI Model Training and Analysis: Gain proficiency in SQL, focusing on its application in data extraction, transformation, and analysis for training AI models, ensuring efficient handling of large datasets.
• Statistics and Probability for AI: Learn foundational statistics and probability concepts essential for AI model evaluation, including hypothesis testing, distributions, regression, and classification, to understand and interpret AI results.
• R Programming Language: Understand and apply R programming, which is widely used for statistical analysis and visualizations, offering a complementary skill set to Python for data analysis and model development.
• Generative AI Foundations: Discover the core principles of Generative AI, understanding how these models can create data (images, text, sound) based on patterns learned from training datasets.
• Machine Learning with Generative AI: Apply machine learning techniques to Generative AI problems, learning how to build models that can generate new content, make predictions, and enhance data-driven decision-making.
• Data Visualization Tools with Generative AI Insights: Master data visualization tools like Tableau and Power BI to represent insights derived from Generative AI models, helping businesses interpret complex data and AI predictions.
• Advanced Deep Learning with Generative AI Applications: Explore Deep Learning applications within Generative AI, including models like GANs (Generative Adversarial Networks), to create new data and sophisticated AI-driven content.
• Building Neural Networks from Scratch: Learn the process of building and training neural networks from the ground up, gaining a deep understanding of the architecture behind deep learning and AI systems.
• Deep Learning with TensorFlow 2.0: Use TensorFlow 2.0, a popular deep learning framework, to build, train, and optimize neural networks, and learn the nuances of TensorFlow for complex AI model development.
• Advanced Deep Learning and Generative AI: Delve deeper into advanced deep learning techniques, focusing on their application in Generative AI models to enhance their capabilities and improve accuracy.
• Generative Models and Applications: Explore various Generative Models such as GANs, VAEs (Variational Autoencoders), and Recurrent Neural Networks (RNNs) to generate realistic data, images, and text for diverse applications.
• Deep Learning Applications: Apply Deep Learning techniques to solve real-world problems in fields like healthcare, finance, marketing, and entertainment, showcasing the versatility of AI applications.
• Preprocessing for Generative Models and NLP: Learn how to preprocess and clean data for Generative Models and Natural Language Processing (NLP) tasks, such as text generation and sentiment analysis, which are integral to modern AI applications.
• Text Classification, Generation, Semantic Analysis, and Sentiment Analysis: Dive into advanced NLP techniques such as text classification, text generation, semantic analysis, and sentiment analysis to build AI-driven text-based applications.
• Case Study and CAPSTONE Project: Apply your learning to a real-world Capstone Project, where you will solve a complex problem using Generative AI and present your solution as part of your portfolio. The project will involve designing and developing an AI model, interpreting results, and deriving actionable insights.

Learning Format:

The course combines theoretical learning with practical hands-on training, including live coding sessions, assignments, and projects. You will work on real-world AI problems, ensuring you are well-prepared to apply your knowledge in the field of Generative AI.

Certification and Career Impact:

Upon successful completion, you will receive a certification in Data Science with Generative AI, validating your expertise in the field. This certification will open up career opportunities in AI research, data science, machine learning, and Generative AI development.