Machine Learning with spark and python – Michael Bowles

Extracting actionable information from data is changing the fabric of modern business in ways that directly affect programmers. One way is the demand for new programming skills. Market analysts predict demand for people with advanced statistics and machine learning skills will exceed supply by 140,000 to 190,000 by 2018. That means good salaries and a wide choice of interesting projects for those who have the requisite skills. Another development that affects programmers is progress in developing core tools for statistics and machine learning. This relieves programmers of the need to program intricate algorithms for themselves each time they want to try a new one. Among general-purpose programming languages, Python developers have been in the forefront, building state-of-the-art machine learning tools, but there is a gap between having the tools and being able to use them efficiently.

Programmers can gain general knowledge about machine learning in a number of ways: online courses, a number of well-written books, and so on. Many of these give excellent surveys of machine learning algorithms and examples of their use, but because of the availability of so many different algorithms, it’s difficult to cover the details of their usage in a survey. This leaves a gap for the practitioner. The number of algorithms available requires making choices that a programmer new to machine learning might not be equipped to make until trying several, and it leaves the programmer to fill in the details of the usage of these algorithms in the context of overall problem formulation and solution.

Related posts:

Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Neural Networks and Deep Learning - Charu C.Aggarwal
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning in Python - LazyProgrammer
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Introduction to the Math of Neural Networks - Jeff Heaton
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Artificial Intelligence by example - Denis Rothman
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Neural Networks - A visual introduction for beginners - Michael Taylor
Python Machine Learning - Sebastian Raschka
Deep Learning with Hadoop - Dipayan Dev
Learn Keras for Deep Neural Networks - Jojo Moolayil
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Python Deep Learning Cookbook - Indra den Bakker
R Deep Learning Essentials - Dr. Joshua F.Wiley
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
An introduction to neural networks - Kevin Gurney & University of Sheffield
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster