AI for Data Scientists Harnessing AI Techniques for Advanced Data Analysis and Insights

AI for Data Scientists Harnessing AI Techniques for Advanced Data Analysis and Insights

Mastering Machine Learning and AI for Smarter Data-Driven Decisions

About the Book

AI is revolutionizing data science, enabling professionals to uncover deeper insights, automate complex processes, and enhance predictive modeling. AI for Data Scientists explores cutting-edge AI techniques, from machine learning algorithms to neural networks, providing practical applications to transform raw data into valuable intelligence.

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About the Author

ALBERT TETTEH ADJEI
ALBERT TETTEH ADJEI

Albert Tetteh Adjei is a passionate software engineer from Ghana with a deep interest in emerging technologies and digital transformation. A graduate of Aptech Computer Education, he specializes in software development and has a keen focus on next-generation wireless technologies, artificial intelligence, and cybersecurity. With a strong background in tech, Albert is dedicated to simplifying complex topics for readers, helping them navigate the ever-evolving digital landscape.

Table of Contents

Table of Contents Chapter 1: Introduction to AI and Data Science ................................ 11 Chapter 2: The Fundamentals of Machine Learning ......................... 16 Chapter 3: Data Preparation and Cleaning for AI Models ................. 22 Chapter 4: Feature Selection and Dimensionality Reduction ........... 28 Chapter 5: Introduction to Deep Learning ......................................... 35 Chapter 6: Neural Networks: Building Blocks of AI ............................ 41 Chapter 7: Convolutional Neural Networks for Image Analysis ........ 47 Chapter 8: Recurrent Neural Networks for Time Series Data ........... 58 Chapter 9: Natural Language Processing with AI ............................... 62 Chapter 10: Reinforcement Learning for Data-Driven Decision Making .................................................................................................. 70 Chapter 11: Transfer Learning for Efficient AI Model Development 74 Chapter 12: AI for Predictive Analytics and Forecasting ................... 82 Chapter 13: Explainable AI: Understanding AI Model Interpretability .............................................................................................................. 91 Chapter 14: Hyperparameter Tuning for Optimal Model Performance .............................................................................................................. 95 Chapter 15: AI-Driven Anomaly Detection ....................................... 101 Chapter 16: Advanced Clustering Techniques with AI..................... 106 Chapter 17: AI for Data Visualization and Insights Extraction ......... 112 Chapter 18: Ensemble Learning: Combining AI Models for Better Results ................................................................................................ 120 10 Chapter 19: AI in Big Data Analytics ................................................. 127 Chapter 20: AI-Enhanced Feature Engineering ................................ 134 Chapter 21: Ethics and Bias in AI for Data Science .......................... 142 Chapter 22: Building AI Models for Real-Time Data Analysis .......... 146 Chapter 23: Cloud-Based AI Solutions for Scalable Data Analysis .. 151 Chapter 24: Generative Adversarial Networks for Data Augmentation ............................................................................................................ 159 Chapter 25: AI for Personalization and Recommendation Systems 163 Chapter 26: Security and Privacy in AI for Data Science ................. 168 Chapter 27: AI in Automated Data Pipeline Development.............. 172 Chapter 28: Quantum Computing and AI in Data Science .............. 177 Chapter 29: Future Trends in AI for Data Science ........................... 183 Chapter 30: Building a Career in AI and Data Science ..................... 190

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