Description

The Machine Learning Blueprint is your comprehensive guide to understanding and implementing modern machine learning techniques using Python. Whether you’re a student, researcher, or an industry professional, this book equips you with the knowledge and skills to build smarter, more efficient models.

Step-by-step tutorials covering classification, regression, clustering, and ensemble models

● Learn to leverage powerful Python libraries such as NumPy, pandas, scikit-learn, and TensorFlow to develop, test, and optimize machine models.
● Explore practical case studies on predictive modeling in domains like healthcare and geoscience.
● Gain hands-on experience with real-world datasets, building and fine-tuning models with modern AutoML techniques.

Table of Contents
1. Foundations of Machine Learning
2. Understanding & Preparing Data
3. Creating & Collecting Real ML Datasets
4. Feature Engineering & Data Enrichment
5. Feature Selection & Dimensionality Reduction
6. Core Machine Learning Algorithms
7. Unsupervised Learning & Anomaly Detection
8. Ensemble Learning & Modern ML Techniques
9. Model Evaluation, Explainability & MLOps Basics
10. If Your Machine Learning Model Fails: Why It Happens and How to Respond