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Data Science & Machine Learning Mastery: From Python Basics to Deep Learning, CNN, RNN, NLP & Transformers | Step-by-Step Guide for Beginners to Advanced AI Engineers - Couverture souple

Livre 2 sur 3: The Complete Data Science & AI Engineering Series

Kumar, Abhishek

 
9798254712428: Data Science & Machine Learning Mastery: From Python Basics to Deep Learning, CNN, RNN, NLP & Transformers | Step-by-Step Guide for Beginners to Advanced AI Engineers

Synopsis

Unlock the power of Artificial Intelligence and Data Science with this complete step-by-step guide designed for beginners, students, and aspiring AI engineers.

“Data Science & Machine Learning Mastery” takes you from the absolute basics of Python and Machine Learning to advanced Deep Learning architectures like CNN, RNN, LSTM, NLP, and Transformers—the same technologies powering ChatGPT and modern AI systems.

This book is structured in a practical, easy-to-understand way with real-world examples and Python implementations in every major topic.


What You Will Learn:

Fundamentals of Machine Learning
Supervised & Unsupervised Learning
Regression & Classification Techniques
Clustering Algorithms
Dimensionality Reduction (PCA)
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN & LSTM)
Natural Language Processing (NLP)
Transformers & Attention Mechanism


Why This Book is Different:

Beginner-friendly explanations
Real Python code examples
Industry-relevant concepts
Step-by-step learning path
Covers modern AI (ChatGPT-level concepts)


Who This Book Is For:

Beginners in Data Science
College students
Software testers moving to AI
Python developers
Anyone wanting to enter AI/ML field


Outcome After Reading:

By the end of this book, you will be able to:

  • Understand how AI models work
  • Build machine learning models in Python
  • Work with deep learning systems
  • Understand ChatGPT-like architectures
  • Start your journey as an AI Engineer

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.