About this ebook
Are you ready to move beyond theory and start building intelligent systems that work in the real world? Applied Machine Learning: From Fundamentals to Deployment with Scikit-Learn and PyTorch is your complete, professional guide. This book is meticulously crafted for Python developers, data scientists, and aspiring ML engineers who want a deep, practical understanding of the entire machine learning lifecycle—from the first line of exploratory code to a model serving live predictions.
Forget dry, academic texts. This book teaches through hands-on projects and professional practices. You'll start by building a solid foundation with scikit-learn, mastering essential algorithms for classification, regression, and clustering. You'll learn not just how to call an API, but the underlying mathematics and intuition, enabling you to choose the right model, diagnose its failures, and tune it for peak performance.
The journey then progresses seamlessly into modern deep learning with PyTorch. We demystify this powerful framework from the ground up, starting with tensors and autograd, so you truly understand how neural networks learn. You'll build and train advanced architectures including Convolutional Neural Networks (CNNs) for computer vision, Recurrent Neural Networks (RNNs) and Transformers for natural language processing (NLP), and even explore cutting-edge areas like Graph Neural Networks (GNNs). A dedicated section on Large Language Models (LLMs) covers practical fine-tuning and prompt engineering using the Hugging Face Transformers library.
The final, crucial part of the book is what sets it apart: Deployment and MLOps. You'll learn how to take your trained models out of the notebook and turn them into reliable applications. We cover building a REST API with FastAPI, containerization with Docker, model serving with TorchServe, and the fundamentals of MLOps—including monitoring for data drift, CI/CD for ML, and building automated retraining pipelines. This is the knowledge that transforms a hobbyist into a professional.
What You Will Learn & Build:
Foundational Mastery: Implement core ML algorithms with scikit-learn and understand the math behind them.
Deep Learning Expertise: Build dynamic computation graphs with PyTorch, from simple neural networks to advanced CNN, RNN, LSTM, and Transformer models.
NLP & LLM Skills: Perform sentiment analysis, text classification, and learn practical fine-tuning of BERT and GPT-style models.
End-to-End Systems: Complete a capstone project that takes a model from a Jupyter notebook to a containerized web application.
Production-Ready Practices: Implement model validation, hyperparameter tuning with grid search, ensemble methods (Random Forests, Gradient Boosting/XGBoost), and write clean, maintainable code.
Deployment & MLOps: Package models with ONNX, serve them via API, monitor performance, and manage the full model lifecycle.
Who This Book Is For:
This book is for anyone with basic Python knowledge who wants to become a job-ready machine learning practitioner. It's perfect for:
Software developers transitioning into ML/AI roles.
Data analysts and scientists looking to add deep learning and deployment skills.
Students building a portfolio of practical ML projects.
Anyone tired of tutorial purgatory and ready to build professional, deployable systems.
Your Learning Path Includes:
Clear explanations paired with executable, well-commented code.
Best practices for debugging, optimizing, and accelerating training with CUDA.
Coverage of generative models like GANs and an introduction to diffusion models.
Specialized applications including time-series forecasting and recommender systems.
A full chapter on Graph Neural Networks (GNNs) using PyTorch Geometric.
Stop just running code. Start building and deploying intelligent systems. Click "Buy Now" to begin your professional machine learning journey.
