Data Science Programming Cookbook Ravi Kore, Praveen Sahu
- Autorzy:
- Ravi Kore, Praveen Sahu
- Wydawnictwo:
- BPB Publications
- Ocena:
- Stron:
- 508
- Dostępne formaty:
-
ePubMobi
Opis
książki
:
Data Science Programming Cookbook
Data science is transforming modern tech, and mastering it is essential for anyone aiming to analyze data, build predictive models, and deploy cutting-edge AI systems. This Data Science Cookbook is your comprehensive roadmap to understanding core analytics, classical machine learning, and advanced generative AI using a hands-on, practical approach.
This book systematically guides you through the complete data science lifecycle, starting with environment setup, foundational mathematics, exploratory data analysis, and visualization. You will master classical supervised and unsupervised machine learning algorithms, ensemble methods, and neural network architectures. The guide further explores computer vision with OpenCV, natural language processing, transformer mechanics, generative AI, GANs, and parameter-efficient LLM fine-tuning using LoRA and prompt engineering. Backed by 127 recipes, it covers MLOps production deployment with Flask APIs, Azure ML, and model drift monitoring, concluding with capstone projects featuring real-time face detection, GPT-3 chatbots, synthetic image generation, and vector database image comparison.
By the end of this book, you will possess a job-ready understanding of data science, machine learning, and artificial intelligence, equipped with the practical skills needed to build, deploy, and scale real-world applications confidently. What you will learn
Perform data processing EDA with Pandas, Seaborn, and web scraping.
Train classical supervised models using Python and scikit-learn libraries.
Apply K-means clustering and reduce feature dimensions using PCA.
Preprocess text, extract embeddings, and implement BERT sentiment classifiers.
Optimize ensemble models using XGBoost, LightGBM, and hyperparameter tuning. Who this book is for
Designed for students, software developers, data analysts, and tech professionals aiming to build practical skills in AI. Readers should possess basic computer literacy and fundamental Python knowledge to comfortably navigate the structured hands-on workflows and code recipes. Table of Contents
1. Introduction to Data Science
2. Getting Started with Python and Data Science
3. Mathematics for Data Science
4. EDA, Data Handling, and Visualization
5. Getting Started with Supervised Machine Learning
6. Getting Started with Unsupervised Machine Learning
7. Intermediate Machine Learning Techniques
8. Deep Learning Fundamentals
9. Advanced Machine Learning
10. Image Processing and Computer Vision
11. LLM and Generative AI
12. Advanced Generative AI and LLM
13. Machine Learning Deployment in Production
14. Projects
Wybrane elementy graficzne tej sekcji powstały przy wsparciu AI
Szczegóły książki
- ISBN Ebooka:
- 978-93-785-4820-8, 9789378548208
- Data wydania ebooka :
-
2026-09-07
Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna
z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie.
Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
- Język publikacji:
- angielski
- Rozmiar pliku ePub:
- 13.6MB
- Rozmiar pliku Mobi:
- 13.6MB
- Zgłoś erratę
- Kategorie:
Dane » Data science
Sztuczna inteligencja » Budowanie aplikacji AI
Dane » Data science » Machine Learning
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