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

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

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

DEEP LEARNING FOR DATA MINING UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

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

DATA SCIENCE ETHICS AND RESPONSIBLE AIEITHICAL CONSIDERATIONS IN DATA SCIENCE AND AI

PRACTICAL MACHINE LEARNING APPLICATIONS

Storytelling with Data: Let's Practice!

DEEP LEARNING: NEURAL NETWORK AND BEYOND

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

MACHINE LEARNING FOR IMAGE GENERATION AND STYLE TRANSFER

Deep Learning with PyTorch

REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS

Critical Data Literacies: Rethinking Data and Everyday Life

MACHINE LEARNING FOR HEALTHCARE PREDICTIVE ANALYTICS AND PERSONALIZED MEDICINE

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

THE ART OF INTELLIGENT MACHINES UNLEASHING THE POWER OF MACHINE LEARNING

Deliver Value in the Data Economy

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