Research Compendium on IoT and Machine Learning

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, Edition 2

DEEP LEARNING FOR DATA MINING UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis

MACHINE LEARNING: APPLICATION AND CHALLENGES

The Magic Of AI: Best Ways to Simplify Everyday Life

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

Practical Lakehouse Architecture: Designing and Implementing Modern Data Platforms at Scale

DEEP LEARNING: NEURAL NETWORK AND BEYOND

Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh

MACHINE LEARNING FOR IMAGE GENERATION AND STYLE TRANSFER

Storytelling with Data: Let's Practice!

REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, Edition 3

MACHINE LEARNING FOR HEALTHCARE PREDICTIVE ANALYTICS AND PERSONALIZED MEDICINE

Bayesian Analysis with Python: A practical guide to probabilistic modeling, Edition 3

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

Driving Data Quality with Data Contracts: A comprehensive guide to building reliable, trusted, and effective data platforms

PRACTICAL MACHINE LEARNING APPLICATIONS

Managing Data as a Product: Design and build data-product-centered socio-technical architectures