Essential AI 101: Bridging the Knowledge Gap: Data Skills and the Importance of Data in AI – Episode 3

AI is only as smart as the data it learns from. 📊 In this episode of AI Innovations Unleashed, we dive into:
✅ Why data is the backbone of AI
✅ How to collect, clean, and preprocess data
✅ Must-have data skills: SQL, Python, and visualization

If you’re in AI, understanding data is a game-changer! 🚀

AI Innovations Unleashed

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AI Death Match Tournament – FINALS!!

byJR DeLaney

Vision vs Ultron. Who will walk away with the trophy?

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AI Death Match Tournament – FINALS!!

Vision vs Ultron. Who will walk away with the trophy? […]

April 2, 2025

JR DeLaney

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Reference List

1. Russell, S., & Norvig, P. (2020).Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
   • A comprehensive book covering AI fundamentals, including machine learning, data processing, and real-world AI applications.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016).Deep Learning. MIT Press.
   • An in-depth exploration of deep learning and how AI systems process structured and unstructured data.
3. Provost, F., & Fawcett, T. (2013).Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media.
   • A guide to data-driven decision-making, focusing on how AI uses structured and unstructured data.
4. McKinney, W. (2017).Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter (2nd ed.). O’Reilly Media.
   • A practical introduction to Python for data science, focusing on data cleaning, preprocessing, and visualization.
5. Kelleher, J. D. (2019).Deep Learning (MIT Press Essential Knowledge Series).
   • A beginner-friendly book explaining the principles of deep learning and data-driven AI models.
6. Van der Lans, R. (2006).Introduction to SQL: Mastering the Relational Database Language (4th ed.). Addison-Wesley.
   • A foundational book on SQL and how databases structure and store AI training data.
7. Domingos, P. (2015).The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.
   • A high-level overview of machine learning and how AI systems process vast amounts of data.
8. Manning, C., Raghavan, P., & Schütze, H. (2008).Introduction to Information Retrieval. Cambridge University Press.
   • Covers Natural Language Processing (NLP) and how AI interprets and structures text data.

Additional Resources

1. Google’s Machine Learning Crash Course – https://developers.google.com/machine-learning/crash-course
   • A free, interactive introduction to AI, machine learning, and data preprocessing.
2. IBM Data Science Professional Certificate (Coursera) – https://www.coursera.org/professional-certificates/ibm-data-science
   • A hands-on learning experience in Python, SQL, and AI-driven data analytics.
3. Stanford’s CS229: Machine Learning Course – https://cs229.stanford.edu/
   • Lecture notes and assignments from one of the top machine learning courses in the world.
4. Tableau Public – https://public.tableau.com/
   • A free data visualization tool to practice presenting AI insights.
5. The Elements of AI (University of Helsinki) – https://www.elementsofai.com/
   • A beginner-friendly course that explains how AI processes and interprets data.
6. Google Cloud BigQuery for SQL & Data Analysis – https://cloud.google.com/bigquery
   • A practical tool for handling large datasets with SQL and AI-powered analytics.
7. DataCamp’s Python & SQL Training – https://www.datacamp.com/
   • Offers beginner-to-advanced courses in data manipulation, SQL, and AI-driven analytics.
8. MIT OpenCourseWare: Introduction to Deep Learning – https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s191-introduction-to-deep-learning-january-iap-2020/
   • A free resource covering deep learning and AI model development.

Additional Readings

1. Halevy, A., Norvig, P., & Pereira, F. (2009). “The Unreasonable Effectiveness of Data.” IEEE Intelligent Systems, 24(2), 8–12.
   • Explains why large, well-structured datasets are more powerful than complex AI algorithms.
2. Gebru, T., et al. (2018). “Datasheets for Datasets.” arXiv preprint arXiv:1803.09010.
   • Discusses ethical AI data collection and how biases in training data impact AI outcomes.
3. Ng, A. (2018). “AI Transformation Playbook.” Landing AI.
   • A guide to how businesses can adopt AI, focusing on the role of data preparation and model training.
4. LeCun, Y., Bengio, Y., & Hinton, G. (2015). “Deep Learning.” Nature, 521(7553), 436–444.
   • Covers the foundations of deep learning and how AI models learn from massive datasets.
5. Cathy O’Neil (2016).Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.
   • A critical look at AI bias, data ethics, and the real-world consequences of bad data.
6. Dwork, C., & Mulligan, D. K. (2013). “It’s Not Privacy, and It’s Not Fair.” Stanford Law Review Online, 66, 35.
   • Explores the privacy and fairness implications of AI-driven decision-making.
7. Chollet, F. (2018).Deep Learning with Python. Manning Publications.
   • Covers how AI learns from structured and unstructured data with practical examples.
8. Pasquale, F. (2020).New Laws of Robotics: Defending Human Expertise in the Age of AI.
   • Discusses the ethical and societal impact of data-driven AI decision-making.