Learn to solve challenging data science problems by building powerful machine learning models using Python
About This Book
* Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
* This practical tutorial tackles real-world computing problems through a rigorous and effective approach
* Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale
Who This Book Is For
This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.
What You Will Learn
* Use predictive modeling and apply it to real-world problems
* Understand how to perform market segmentation using unsupervised learning
* Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test
* Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
* Increase predictive accuracy with deep learning and scalable data-handling techniques
* Work with modern state-of-the-art large-scale machine learning techniques
* Learn to use Python code to implement a range of machine learning algorithms and techniques
In Detail
Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.
In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.
The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
* Python Machine Learning Cookbook by Prateek Joshi
* Advanced Machine Learning with Python by John Hearty
* Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron
Style and approach
This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!
Author Biography:
Prateek Joshi is an Artificial Intelligence researcher and a published author. He has over eight years of experience in this field with a primary focus on content-based analysis and deep learning. He has written two books on Computer Vision and Machine Learning. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences.
People from all over the world visit his blog, and he has received more than a million page views from over 200 countries. He has been featured as a guest author in prominent tech magazines. He enjoys blogging about topics, such as Artificial Intelligence, Python programming, abstract mathematics, and cryptography. You can visit his blog at www.prateekvjoshi.com.
He has won many hackathons utilizing a wide variety of technologies. He is an avid coder who is passionate about building game-changing products. He graduated from University of Southern California, and he has worked at companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. You can learn more about him on his personal website at www.prateekj.com John Hearty is a consultant in digital industries with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics.
Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modeling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modeling in asymmetrical games, and the creation of player segmentation models for individualized game experiences.
Eventually, John struck out on his own as a consultant offering comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favorite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network.
After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds machine learning solutions in Python to fulfil a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation. He currently lives in the UK. Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has an MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence projects.
He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale. Luca Massaron is a data scientist and marketing research director who is specialized in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience in solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about everything regarding data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essentials. Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges that span from natural language processing (NLP) and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.
