Master Machine Learning - From Zero to Your First Predictive Model
Machine learning is no longer a tool reserved for tech giants. Today, anyone with curiosity, data, and a laptop can build predictive models that drive smarter decisions in business, healthcare, finance, and beyond.
Mastering Machine Learning: Build Your First Predictive Model is your step-by-step guide to understanding machine learning concepts and applying them through practical projects. With hands-on tutorials, clear explanations, and real-world case studies, this book gives you the skills and confidence to create models that work.
Inside, you'll discover:
✔️ The foundations of AI, machine learning, and deep learning explained in plain language.
✔️ How to collect, prepare, and clean data for accurate models.
✔️ Exploratory Data Analysis (EDA) and visualization techniques with Python.
✔️ Building classification and regression models including logistic regression, decision trees, and gradient boosting.
✔️ Feature engineering, pipelines, and hyperparameter tuning.
✔️ Debugging models and avoiding common pitfalls.
✔️ Ensemble methods and boosting techniques like Random Forests and LightGBM.
✔️ Deploying your models with FastAPI, Docker, and MLOps basics.
✔️ Auditing models for fairness, ethics, and explainability.
✔️ Real-world case studies in manufacturing, healthcare, and logistics.
By the end of this book, you'll not only understand machine learning theory but also have a portfolio of working projects to showcase your skills.
