"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web.

"Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

This repository contains all the code, notebooks and examples used in this book. We will also be adding bonus content here from time to time. So keep watching this space!

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.

We focus on leveraging the latest state-of-the-art data analysis, machine learning and deep learning frameworks including scikit-learn, pandas, statsmodels, spaCy, nltk, gensim, tensorflow, keras, skater and several others to process, wrangle, analyze, visualize and model on real-world datasets and problems! With a learn-by-doing approach, we try to abstract out complex theory and concepts (while presenting the essentials wherever necessary), which often tends to hold back practitioners from leveraging the true power of machine learning to solve their own problems.

Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.

• Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered.

• Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.

• Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!

• Part I: Understanding Machine Learning
  • Chapter 1: Machine Learning Basics
  • Chapter 2: The Python Machine Learning Ecosystem
• Part II: The Machine Learning Pipeline
  • Chapter 3: Processing, Wrangling and Visualizing Data
  • Chapter 4: Feature Engineering and Selection
  • Chapter 5: Building, Tuning and Deploying Models
• Part III: Real-World Case Studies
  • Chapter 6: Analyzing Bike Sharing Trends
  • Chapter 7: Analyzing Movie Reviews Sentiment
  • Chapter 8: Customer Segmentation and Effective Cross Selling
  • Chapter 9: Analyzing Wine Types and Quality
  • Chapter 10: Analyzing Music Trends and Recommendations
  • Chapter 11: Forecasting Stock and Commodity Prices
  • Chapter 12: Deep Learning for Computer Vision

• Execute end-to-end machine learning projects and systems
• Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
• Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
• Apply a wide range of machine learning models including regression, classification, and clustering.
• Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.

This book has been specially written for IT professionals, analysts, developers, data scientists, engineers, graduate students and anyone with an interest to analyze and derive insights from data!

TBA