Subtitles are available for Introduction, Case Study and Design Patterns Concepts. Design and architecture of any software/solution provides the base and makes it flexible, extensible for future requirements. A good designed software/solution makes it easy to understand and maintain. Design patterns are known as best practices to design software for problems which are repeated in nature. This course "Design Patterns Through Python" gives you understanding of all 23 patterns described in Gang Of Four book - "Design Patterns: Elements of Reusable Object-Oriented Software", by Gamma, Helm, Johnson and Vlissides.

Andy Betts is a chief marketer, consultant and digital hybrid with more than 25 years of experience in digital and marketing technology--working across London, Europe, New York, and San Francisco. He works as a global executive adviser and consultant for start-ups, agencies, and many Fortune 500 companies. He spends considerable time working in marketing technology communities on strategic marketing initiatives, influence and public relations projects.

Have you ever wanted to build a games with a graphical interface but didn't know how to? May be you even know how to create tools on a command line but have no idea how to convert it into a graphical interface that people can click on. In this course we will be learning Python GUI Programming Turtle other advanced python modules to build graphical user interfaces (GUI) and games from scratch. We will learn from basics of Python i.e. variables, slicing, string, some module, arithmetic and logical operations, looping, functions, object oriented programming. After that we will learn the basics stuff of Pygame and OpenGL and Blender basics stuff.

As a response to this sort of criticism, many have promoted parameter estimation as a better method over hypothesis testing. For example, you could estimate the difference in the treatment and placebo groups, along with the 95% confidence interval as a measure of the uncertainty in that estimate. However, confidence intervals are often misinterpreted as meaning there is a X% change the parameter falls in the interval. Instead, the correct interpretation is obtuse: if you did your experiment an infinite number of times and calculated the confidence interval for each of those experiments, then X% of the time the X% confidence interval would contain the true parameter. For any one experiment though, there is no probability calculated to tell you if your confidence interval actually contains the true parameter, the parameter is either in the interval or not.

These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time. The usage of open standards for data and knowledge models eliminates proprietary vendor lock-in.

This article targets whom have a basic understanding for TensorFlow Core API. Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model. The loss returned is 53.76. Existence of error, specially for large error, means that the parameters used must be updated.

Thanks to all the wonderful folks at DataScience.com and especially Pramit Choudhary for helping me discover the amazing world of model interpretation. The field of Machine Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. Rather than just running lab experiments to publish a research paper, the key objective of data science and machine learning in the 21st century has changed to tackling and solving real-world problems, automating complex tasks and making our life easier and better. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same.

Apple's Applied Machine Learning team has built systems for a number of large-scale data science applications. We work on many high-impact projects that serve various Apple lines of business. We use the latest in open source technology and as committers on some of these projects, we are pushing the envelope. Working with multiple lines of business we manage many streams of Apple-scale data. We bring it all together and unleash business value.

This project was part of one my recent job interview skill test for a "Machine learning engineer" position. I had to complete the project in 48 hours which includes writing a 10-page report in latex. The dataset has classes and highly imbalanced. The primary objective of this project was to handle data imbalance issue. In the following subsections, I describe three techniques I used to overcome the data imbalance problem.

Today I've published my latest book "Mastering Machine Learning Algorithms" (in a few days it will be available on all channels). Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms.