Python: Step into the World of Machine Learning

2.5 (2 reviews total)
By Alexander T. Combs , Dan Van Boxel , Giancarlo Zaccone and 4 more
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About this video

ML is becoming increasingly pervasive in the modern data-driven world. This course takes a hands-on approach and demonstrates how you can perform various machine learning tasks on real-world data. The course starts by talking about various realms in machine learning followed by practical examples. It then moves on to discuss the more complex algorithms, such as Support Vector Machines, Extremely Random Forests, Hidden Markov Models, Sentiment Analysis, and Conditional Random Fields. You will learn how to make informed decisions about the types of algorithm that you need to use and how to implement these algorithms to get the best possible results.

After you are comfortable with machine learning, this course teaches you how to build real-world machine learning applications step by step. Further, we’ll explore deep learning with TensorFlow, which is currently the hottest topic in data science. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change the way you look at data. You will also learn how to train your machine to build new models that help make sense of deeper layers within your data.

By the end of this course, you should be able to solve real-world data analysis challenges using innovative and cutting-edge machine learning techniques.

Style and Approach

With easy-to-follow practical examples, this course will help you gain a grip on each and every aspect of machine learning. Covering all the powerful algorithms of machine learning, we’ll teach you how to build different interesting machine learning applications and finally cover deep learning with TensorFlow.

This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package. It includes content from the following Packt products:

Note: This interactive EPUB adheres to the latest specification, and requires that your reader supports video and interactive content. We recommend using Readium with the latest stable version of Google Chrome, or iBooks for OSX.

Publication date:
January 2017
7 hours

About the Authors

  • Alexander T. Combs

    Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.

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  • Dan Van Boxel

    Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for Dan Does Data, a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research articles and presented findings at the Transportation Research Board and other academic journals.

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  • Giancarlo Zaccone

    Giancarlo Zaccone has over fifteen years' experience of managing research projects in the scientific and industrial domains. He is a software and systems engineer at the European Space Agency (ESTEC), where he mainly deals with the cybersecurity of satellite navigation systems. Giancarlo holds a master's degree in physics and an advanced master's degree in scientific computing. Giancarlo has already authored the following titles, available from Packt: Python Parallel Programming Cookbook (First Edition), Getting Started with TensorFlow, Deep Learning with TensorFlow (First Edition), and Deep Learning with TensorFlow (Second Edition).

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  • Luis Pedro Coelho

    Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics - the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004, he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.

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  • Prateek Joshi

    Prateek Joshi is an artificial intelligence researcher, an author of several books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venturefunded Silicon Valley start-up building an intelligence platform for water facilities. He graduated from the University of Southern California with a Master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research.

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  • Sebastian Raschka

    Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

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  • Willi Richert

    Willi Richert has a PhD in machine learning/robotics, where he has used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft, he is involved in various machine learning areas, such as deep learning, active learning, or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later, he discovered Turbo Pascal, then Java, then C++ - only to finally arrive at his true love: Python.

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Latest Reviews

(2 reviews total)
nothing x
incomplete materials and books, says it will provide all the materials of the 6 books and videos but only few portions are given. Better to buy individual books or videos. A straight no no for curated learnings
Python: Step into the World of Machine Learning
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