Part 1: Gradient Boosting and LightGBM Fundamentals

In this part, we will initiate our exploration of machine learning by grounding you in its fundamental concepts, ranging from basic terminologies to intricate algorithms like random forests. We will delve deep into ensemble learning, highlighting the power of decision trees when combined, and then shift our focus to the gradient-boosting framework, LightGBM. Through hands-on examples in Python and comparative analyses against techniques like XGBoost and deep neural networks, you’ll gain both a foundational understanding and practical competence in the realm of machine learning, especially with LightGBM.

This part will include the following chapters:

• Chapter 1, Introducing Machine Learning
• Chapter 2, Ensemble Learning – Bagging and Boosting
• Chapter 3, An Overview of LightGBM in Python
• Chapter 4, Comparing LightGBM, XGBoost, and Deep Learning

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