Andrew Ng is a hero and a role model for everyone who is starting the machine learning journey. One of his earliest Machine Learning courses saw lakhs of students enrolling and getting a huge boost to their careers. He is now back with a course in Deep Learning specialisation supported by his company Deeplearning.ai. Andrew Ng, one of the foremost artificial intelligence experts, is working hard to train more AI experts on a larger scale who can work across a range of industries. Ng has been an early adopter of online learning with the creation of Coursera.

Python is an object-orientated language that closely resembles the English language which makes it a great language to learn for beginners as well as seasoned professionals. Examples sites that use Python are Instagram, YouTube, Reddit, NASA, IBM, Nokia, etc. Python is one of the most widely used programming languages in the AI field of Artificial Intelligence thanks to its simplicity. It can seamlessly be used with the data structures and other frequently used AI algorithms. This is because it is the ideal language to work with for general purpose tasks. Experienced coders tend to stay more organized and productive when working with Python, as well.

In the coming decade, this evolution will change the realities of work for everyone, which makes it a priority to gain further knowledge related to artificial intelligence. First and foremost, artificial intelligence is a support system that helps us focus on our strengths. As a university, we perceive our role as being an engine for this technological and societal revolution.»

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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.

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.

Over the last few years, Google and Coursera have regularly teamed up to launch a number of online courses for developers and IT pros. Among those was the Machine Learning Crash course, which provides developers with an introduction to machine learning. Now, building on that, the two companies are launching a machine learning specialization on Coursera. This new specialization, which consists of five courses, has an even more practical focus. The new specialization, called "Machine Learning with TensorFlow on Google Cloud Platform," has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for IT professionals and data-scientists. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This comprehensive 3-in-1 course is your one-stop solution to everything that matters in mastering machine learning algorithms and their implementation. Develop pipelines and process data through manipulation, extraction, and data-cleansing techniques. Learn clean coding techniques which are applicable to any scalable Machine Learning projects.

Python is a popular general purpose programming language used for both large and small-scale applications. Python's wide-spread adoption is due in part to its large standard library, easy readability and support of multiple paradigms including functional, procedural and object-oriented programming styles.This Course follows pragmatic approach to tackle end-to-end data science project cycle right from extracting data from different types of sources to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries. By the end of this course, you will have a solid foundation to handle any data science project and have the knowledge to apply various Python libraries to create your own data science solutions.