Dr. Satyajit Pattnaik, Satyajit Pattnaik

    Data Science & AI Masters 2026 - From Python To Gen AI

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    Python · Online Course

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    The Data Science & AI Masters 2026 - From Python To Gen AI course offers an exhaustive, end-to-end learning pathway designed to take students from absolute programming beginners to advanced Artificial Intelligence practitioners. Led by Dr. Satyajit Pattnaik, this comprehensive curriculum addresses the rapidly evolving landscape of modern tech by bridging the gap between foundational data principles and cutting-edge generative technologies. Students start by establishing a robust foundation in Python programming, exploratory data analysis (EDA), and essential statistics. From there, the course transitions into classical machine learning algorithms, deep learning neural networks, and SQL databases. Unlike outdated programs, this 2026-focused masterclass heavily prioritizes modern AI paradigms. Participants dive deep into Natural Language Processing (NLP), Transformer models, and state-of-the-art Generative AI techniques. Key practical highlights include hands-on experience building Retrieval-Augmented Generation (RAG) pipelines, working with Langchain, and deploying vector databases for semantic search. This course is ideal for software engineers, data analysts, and tech enthusiasts who want to master real-world AI applications and future-proof their careers in an increasingly automated world.

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    Dr. Satyajit Pattnaik, Satyajit Pattnaik

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    Dr. Satyajit Pattnaik, Satyajit Pattnaik
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    Dr. Satyajit Pattnaik is a seasoned educator and consultant in the domains of Artificial Intelligence, Machine Learning, and Data Science. With a deep passion for technological advancement and digital transformation, Dr. Pattnaik has dedicated his career to bridging the gap between theoretical complex mathematical concepts and practical, real-world industry applications. His flagship course, "Data Science & AI Masters 2026 - From Python To Gen AI", showcases his commitment to delivering up-to-date, forward-looking education that prepares students for the future tech landscape. As an instructor, Dr. Pattnaik believes in an active, hands-on learning methodology. Instead of relying solely on slide-based lectures,…Show more

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    Program Overview

    Online Course

    Learning format

    Python

    Subcategory

    $54.99

    Price

    Price may change · updated within 1–2 weeks

    English

    Course language

    What You'll Learn

    • Build a solid foundation in Python programming to effectively implement AI concepts and applications.
    • Learn how Machine Learning & Deep Learning works
    • Learn how transformer models revolutionize NLP tasks, and how to leverage them for various applications.
    • Gain hands-on experience with Retrieval-Augmented Generation (RAG) and Langchain for building advanced AI applications.
    • Learn how to utilize vector databases for efficient storage and retrieval of embeddings in AI projects.
    • Understand the complete pipeline of Natural Language Processing, from data preprocessing to model deployment.
    • Explore the essentials of Large Language Models (LLMs) and their applications in generative tasks.
    • Develop skills in crafting effective prompts to optimize model performance and achieve desired outputs.

    Best For

    • Aspiring data scientists looking for a modern, end-to-end pathway from Python basics to Generative AI.
    • Software developers aiming to pivot into AI engineering by mastering RAG pipelines and Langchain.
    • Data analysts who want to move beyond traditional statistics into deep learning and LLM implementation.
    • Tech professionals seeking a 2026-focused curriculum that emphasizes modern transformer architectures.

    Not For

    • Learners seeking a brief, high-level theoretical overview without intensive programming or hands-on coding.
    • Individuals looking for non-technical management or strategy-only perspectives on AI implementation.
    • Advanced researchers or PhD-level AI mathematicians looking for pure theoretical machine learning proofs.

    What's included

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