Statistical modeling and machine learning are the two most important aspects and prerequisites for becoming a quant trader or analyst. If you are willing to make a career out of quantitative finance, make sure that you have gathered ample knowledge over these two domains. Especially if becoming a quantitative trading researcher is there in your mind, these techniques and tools would be a mandate for you to learn. There are many books available in the market related to the techniques of statistical modeling and python machine learning, but some of the best Python books are suggested here from where you can acquire in-depth knowledge of the entire matter.
1. Programming Collective Intelligence: Building Smart Web 2.0 Applications
This book is written by Toby Segaran and imparts substantial knowledge about the pros and cons of python machine learning that makes it the best python book. This book attempts to discuss the consumer analytics application in detail and lays a solid foundation for the python machine learning for every reader. To understand the machine learning algorithms and their application, this book will be a smart pick for any learner. Recommendation, Searching/Ranking, Clustering, Optimization, Support Vector Machines, Decision Trees, Genetic Programming and Feature Detection are some of the topics that this book discusses elaborately making the process of learning more comprehensive.
2. Building Machine Learning Systems with Python
Willi Richert and Luis Pedro Coelho are the two names behind this book that is referred to as one of the best python books. Scikit-learn is considered to be the machine learning library for Python and the same has been discussed with elaborate examples in this book that makes it a must for every learner. Regression and classification tasks have been done in a holistic way to lend the learners a complete understanding. For the topics like modeling, this book also emphasizes on libraries like gensim. Hot topics of quantitative trading like text-based classification and sentiment analysis are also a part of this extensive book.
3. Learning scikit-learn: Machine Learning in Python
Developed by Raúl Garreta and Guillermo Moncecchi, this book is quite a short one in terms of its volume. However, the knowledge that it imparts is no how lesser in amount. If you have already gathered a considerable amount of knowledge regarding the Python coding with some exposure to NumPy, and Pandas, this book would be quite a useful one in making you a pro in machine learning techniques. This book covers a lot more than just the application of scikit-learn documentation and helps the learners to go beyond the basics to get a holistic understanding that makes it one of the best Python books in the market.
4. Machine Learning in Action
Supervised regression, supervised classification, and unsupervised methods are some of the highlights of this book written by Peter Harrington. With the lucid language and the easy to understand propositions, this book has earned considerable repute in the market as one of the best Python books. The mathematical exposure of this book is more than the ones already discussed. For Python programmers with their background in applied mathematics, this book could be an appealing one. it also attempts to discuss the upcoming concept of “big data” that makes it contemporary and relevant.
5. Machine Learning: An Algorithmic Perspective
Authored by Stephen Marsland, this book is one of the best
