| #2 |  Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with Unix Power Tools
|
| #3 |  Veri Bilimi
|
| #4 |  Çıplak İstatistik
|
| #5 |  The Hundred-Page Machine Learning Book The Hundred-Page Machine Learning Book
|
| #6 |  Natural Language Processing With Python
|
| #7 |  Python Data Science Handbook
|
| #8 |  The Art of Statistics
|
| #9 |  Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
|
| #10 |  AI and Machine Learning for Coders
|
| #11 |  Introducing Mlops Introducing Mlops: How to Scale Machine Learning in the Enterprise
|
| #12 |  Practical Statistics for Data Scientists
|
| #13 |  Practical Natural Language Processing: A Comprehensive Guide to Building Real-world NLP systems
|
| #14 | Deep Learning for Coders with Fastai and Pytorch Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD
|
| #15 |  Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
|
| #16 |  Storytelling with Data: A Data Visualization Guide for Business Professionals
|
| #17 |  Pattern Recognition and Machine Learning
|
| #18 |  Artificial intelligence
|
| #19 |  Deep Learning for NLP and Speech Recognition
|
| #20 |  Practical Statistics for Data Scientists: 50 Essential Concepts
|
| #21 |  Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics
|
| #22 |  Fundamentals of Data Engineering Fundamentals of Data Engineering: Plan and Build Robust Data Systems
|
| #23 |  Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures
|
| #24 |  Machine Learning Simplified: A Gentle Introduction to Supervised Learning
|
| #25 |  Python for Data Analysis
|
| #26 |  Süper Zekâ: Yapay Zekâ Uygulamaları, Tehlikeler ve Stratejiler
|
| #27 |  Probabilistic Machine Learning: An Introduction
|
| #28 |  Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
|
| #29 |  Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement
|
| #30 |  Machine Learning System Design Interview
|
| #31 |  An Introduction to Statistical Learning
|
| #32 |  Introduction to Time Series Analysis and Forecasting Introduction to Time Series Analysis and Forecasting
|
| #33 |  Probability
|
| #34 |  Outlier Analysis
|
| #35 |  Recommender Systems: The Textbook
|
| #36 |  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition
|
| #37 |  Introduction to Information Retrieval
|
| #38 |  Machine Learning: A Probabilistic Perspective
|
| #39 |  Probabilistic Graphical Models: Principles and Techniques
|
| #40 |  Foundations of Statistical Natural Language Processing
|
| #41 |  Statistical Rethinking: A Bayesian Course with Examples in R and Stan
|
| #42 |  Introduction to Machine Learning
|
| #43 |  AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning
|
| #44 |  The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight
|
| #45 |  Machine Learning Interviews
|
| #46 |  Deep Learning
|
| #47 |  Deep Learning with Python
|
| #48 |  Lean Analytics
|
| #49 |  HBR's 10 Must Reads on AI, Analytics, and the New Machine Age HBR's 10 Must Reads on AI, Analytics, and the New Machine Age
|
| #50 |  Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
|
| #51 |  Hands-On Large Language Models
|
