Preface

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace .It is one of the fastest growing trends in modern computing and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a Learning Path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems

What this learning path covers

Module 1, Python Machine Learning, discusses the essential machine algorithms for classification and provides practical examples using scikit-learn. It teaches you to prepare variables of different types and also speaks about polynomial regression and tree-based approaches. This module focuses on open source Python library that allows us to utilize multiple cores of modern GPUs.

Module 2, Designing Machine Learning Systems with Python, acquaints you with large library of packages for machine learning tasks. It introduces broad topics such as big data, data properties, data sources, and data processing .You will further explore models that form the foundation of many advanced nonlinear techniques. This module will help you in understanding model selection and parameter tuning techniques that could help in various case studies.

Module 3, Advanced Machine Learning with Python, helps you to build your skill with deep architectures by using stacked denoising autoencoders. This module is a blend of semi-supervised learning techniques, RBM and DBN algorithms .Further this focuses on tools and techniques which will help in making consistent working process.

What you need for this learning path

Module 1, Python Machine Learning will require an installation of Python 3.4.3 or newer on Mac OS X, Linux or Microsoft Windows. Use of Python essential libraries like SciPy, NumPy, scikit-Learn, matplotlib, and pandas. is essential.

Before you start, Please refer:

• The direct link to the Iris dataset would be: https://raw.githubusercontent.com/rasbt/python-machine-learning-book/master/code/datasets/iris/iris.data
• We've added some additional notes to the code notebooks mentioning the offline datasets in case there are server errors. https://www.dropbox.com/sh/tq2qdh0oqfgsktq/AADIt7esnbiWLOQODn5q_7Dta?dl=0
• Module 2, Designing Machine Learning Systems with Python, will need an inclination to learn machine learning and the Python V3 software, which you can download from https://www.python.org/downloads/.
• Module 3, Advanced Machine Learning with Python, leverages openly available data and code, including open source Python libraries and frameworks.

Who this learning path is for

This title is for Data scientist and researchers who are already into the field of Data Science and want to see Machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.

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