Talking about technologies of the future, Machine learning is one domain no one can skip. Machine learning over the years has opened numerous opportunities for companies while at the same time creating new job prospects for people working in quant trading domain. Machines learning comes in handy for someone who is interested in pursuing a quantitative research trader role and or wants to make his career flourish across wall street. To help you make your way to Wall Street, we have come up with top 4 books which can help you understanding python machine learning in depth.
1. Building Machine Learning Systems with Python
One of the best books to understand the broad concepts of machine learning, Building Machine Learning Systems with Python, allows users to follow topics like sci-kit-learn for regression easily. Written by Luis Pedro Coelho this book is recommended for those who have basic knowledge of programming and are looking to enter the machine learning domain. The book also dwells deep into theoretical concepts associated with machine learning, such as sentiment analysis etc. most of which are being utilized in top MNCs.
2. Machine Learning: An Algorithmic Perspective
A mathematical approach to machine learning concepts, this books by Stephen Marsland is a must-read for anyone who is familiar with the concepts of statistics and probability. For those who are alien to the statistical concepts, we recommend reading Elements of Statistical Learning before you start this book. The book covers topics like neural networks, Markov Chain Monte Carlo etc. from a mathematical point of view, and has codes which can directly be inserted into computers for a better understanding.
3. Programming Collective Intelligence: Building Smart Web 2.0 Applications
Best for understanding concepts like consumer analytics, programming collective intelligence, focus on letting readers understand how to apply concepts of machine learning through python. The book doesn’t dwell much into explaining users about the libraries and concepts of python, instead starts straight with the application. Almost every idea associated with machine learning such as clustering, optimization, decision trees etc. has been explained with associated code in detail. Written by Toby Segaran, the book is a must buy for anyone who wishes to understand the concepts of machine learning and its implementation in detail.
4. Machine Learning in Action
This book written by Peter Harrington is focussed on programmers who have knowledge of python as well as some background in mathematics. Divided broadly into two major sections namely supervised and unsupervised methods, this book covers significant topics of machine learning from a mathematical and programmer’s point of view. After reading the book, you will be able to reason as well as apply concepts of machine learning using the language with greater ease.
5. Programming Collective Intelligence
This book was written long before the concepts of machine learning, and artificial intelligence started getting into mainstream culture. However, the book is an excellent resource for understanding the ideas which are utilised in implementing machine learning solutions such as filtering techniques, support vector machines etc. One of the standard books read by data scientists, Programming Collective Intelligence, is best suited for people who are looking to build a career as a quantitative researcher.
So, these five books are what we consider the best resources for learning python machine learning. Let us know if you have some better recommendation or suggestion for us in the comment section below.
