Overview

From Models to Mastery: Building Classical & Deep Learning Systems with PyTorch, Scikit-Learn, and Python Unlock the Full Power of Machine Learning - From Foundational Algorithms to Modern LLMs, Vision Transformers, and Production-Ready MLOps Are you ready to elevate your machine learning expertise and master modern AI development with the most trusted Python tools in the industry? From Models to Mastery is your comprehensive guide to designing, building, and deploying powerful machine learning and deep learning systems using PyTorch 2.x, Scikit-Learn 1.7+, and the latest open-source tools shaping the ML landscape in 2025 and beyond. Whether you're a data scientist, ML engineer, AI researcher, or software developer transitioning into ML, this book empowers you with a complete, end-to-end roadmap - from reproducible classical models to cutting-edge LLM fine-tuning, graph learning, vision systems, and scalable MLOps workflows. What You'll Learn Set up a modern ML environment using Poetry, Conda, CUDA, and GPU accelerators Perform clean, ethical data wrangling using Pandas, Polars, Great Expectations, and DVC Master feature engineering with Featuretools, Feast, Autoencoders, and Dimensionality Reduction Build robust models using Logistic Regression, SVMs, XGBoost, k-NN, and Naïve Bayes Dive deep into PyTorch with tensor operations, torch.compile, and PyTorch Lightning Create and evaluate CNNs, Transformers, GANs, Diffusion Models, and Graph Neural Networks Fine-tune LLMs using LoRA, QLoRA, PEFT, and deploy RAG pipelines with Hugging Face Transformers Optimize and track models using Optuna, MLflow, and Ray Tune Serve models via FastAPI, BentoML, Triton, and deploy to edge with TensorRT, ONNX, and TinyML Implement real-world monitoring with Evidently AI, Prometheus, Model Cards, and ISO/EU compliance Built for the 2025 AI Stack This book is completely modernized with: PyTorch 2.x + torch.compile Scikit-Learn 1.7+, Polars, PEFT, Flash Attention 3 Graph Transformers, ControlNet, RLHF/DPO Feast, Kubeflow, Ray, Argo, GitHub Actions, and more Who This Book Is For Aspiring ML practitioners seeking a rigorous, modern foundation Software engineers transitioning into AI roles Data scientists and AI engineers looking to scale their skillset with LLMs, MLOps, and production workflows Educators and researchers in need of a single, trusted reference across classical and deep learning Take Your ML Journey From Idea to Inference Whether you're solving tabular classification problems, fine-tuning transformers for NLP, building vision systems, or deploying fast inference pipelines to the cloud and edge-this book will help you bridge the gap between academic knowledge and real-world ML engineering mastery. If you're serious about mastering both classical and modern machine learning systems, this is the only book you'll need. Grab your copy now and transform how you build, deploy, and scale intelligent systems with Python.

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