Mastering Machine Learning with Python in Six Steps

1. Front Matter

   Pages i-xvii

   PDF

2. Step 1: Getting Started in Python 3

   • Manohar Swamynathan

   Pages 1-64

3. Step 2: Introduction to Machine Learning

   • Manohar Swamynathan

   Pages 65-143

4. Step 3: Fundamentals of Machine Learning

   • Manohar Swamynathan

   Pages 145-262

5. Step 4: Model Diagnosis and Tuning

   • Manohar Swamynathan

   Pages 263-323

6. Step 5: Text Mining and Recommender Systems

   • Manohar Swamynathan

   Pages 325-381

7. Step 6: Deep and Reinforcement Learning

   • Manohar Swamynathan

   Pages 383-442

8. Conclusion

   • Manohar Swamynathan

   Pages 443-448

9. Back Matter

   Pages 449-457

   PDF

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.

You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. 

Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.

What You'll Learn

• Understand machine learning development and frameworks
• Assess model diagnosis and tuning in machine learning
• Examine text mining, natuarl language processing (NLP), and recommender systems
• Review reinforcement learning and CNN

Who This Book Is For

Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.

• Machine Learning
• Python
• Scikit-Learn
• Model Tuning
• Text Mining
• Neural Networks
• Deep Learning
• Recommendation System
• Reinforcement Learning

• Bangalore, India

  Manohar Swamynathan

Manohar Swamynathan is a data science practitioner and an avid programmer, with over 14+ years of experience in various data science related areas that include data warehousing, Business Intelligence (BI), analytical tool development, ad-hoc analysis, predictive modeling, data science product development, consulting, formulating strategy and executing analytics program. He's had a career covering life cycle of data across different domains such as US mortgage banking, retail/e-commerce, insurance, and industrial IoT. He has a bachelor's degree with a specialization in physics, mathematics, computers, and a master's degree in project management. He's currently living in Bengaluru, the silicon valley of India. 

Policies and ethics