Python is a big deal. More and more beginner programmers are choosing it as their first language to learn, which means its future is more than just bright - it's dazzling. It makes coding faster, easier and fun. When combined with the object oriented programming approach these qualities are further enhanced, which means Python is virtually unstoppable. If you want to future-proof your programming skills, this is exactly what you need to learn.

About this course: This introductory course is developed for high level business people (and those on their way) who want a broad understanding of new Information Technologies and understand their potential for business functions (e.g. This is not a course for people looking for guidance on how to become a deep technical expert or implement these technologies. From Blockchain over Artificial Intelligence to Virtual Reality technologies: This course will empower business leaders to embrace the concepts and bring the state of the art information technologies into their organizations to improve client and customer engagement and ultimately the bottom line of their businesses. Instead of digital disruption, the new technologies and management methods will become the foundation of a Digital Transformation journey for better customer relationship management and client satisfaction. The content is structured in a way that promotes discussions on challenges that business management and marketing functions face due to the rise of new technologies such blockchain, cryptocurrencies, internet of things (IoT), virtual, mixed and augmented reality (VR/AR), artificial intelligence (AI) and big data.

A large portion of the field of statistics is concerned with methods that assume a Gaussian distribution: the familiar bell curve. If your data has a Gaussian distribution, the parametric methods are powerful and well understood. This gives some incentive to use them if possible. Even if your data does not have a Gaussian distribution. It is possible that your data does not look Gaussian or fails a normality test, but can be transformed to make it fit a Gaussian distribution.

GreyCampus is a leading provider of online self-learning courses for working professionals. This course is on Python, which is one of the easiest, most effective and most widely-used programming languages of today. Its efficient high-level data structures, simple yet effective approach to object-oriented programming, dynamic typing and elegant syntax, make Python an ideal language for both experts and novices for quick application development. The code is similar to English and doesn't need much technical knowledge to be read & understood. In this online self-learning course, you'll be taken through the very basics of Python assuming zero prior understanding of programming languages. This course mostly consists of hands-on examples for practical knowledge on Python and provides basic knowledge about the fundamentals of Python and its applications.

Lyft, a leading ride-sharing organization valued at over US$11 billion, depends on its mobile apps and backend infrastructure to run its business. Undetected problems, such as riders not being matched with rides in a timely manner, can cost the company revenue, customers, and market share. Artificial intelligence (AI) can be used to identify problems in real time without the need for manual inspection of multiple dashboards. Join our webinar to hear how Lyft and other data-driven organizations benefit from uncovering hidden insights in real time with AI solutions from Amazon Web Services (AWS) and Anodot. Learn how to prevent events that can impact your revenue and brand integrity with a solution that detects anomalies quickly, allowing you to address issues in a timely manner to help ensure a consistently high-quality experience for your customers.

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It's not often that you get to use a language as powerful and as versatile as Python. Python is a great language for writing web applications, cross-platform desktop applications, Artificial Intelligence software, shell scripts, perform scientific computation, and even create home automation software. To master these skills, you'll need a solid understanding of the Python language. In this course, Programming with Python, you'll start by learning the fundamentals of the language before venturing out to learn more advanced concepts like working with functions, modules, strings, numbers, dates and times, data structures, control statements, and much more. When you are finished with this course, you'll have a solid foundation to go out and build your own applications using Python.

About this course: Welcome to the art and science of machine learning. In this course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. Once familiar with the knobs and levers, otherwise known as hyperparameters, you will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

About this course: This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framework you will learn how to ask complex questions of your data. This will lay the groundwork for the remainder of the course where you'll dive deep and learn about schema design, relational data migrations, and machine learning with MongoDB. By the end of this course you'll understand how to best use MongoDB and its Aggregation Framework in your own data science workflow.