Machine Learning In The Cloud With Azure Machine Learning | Simpliv

The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies - both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines - we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not - based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies - the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior. 

Or Amazon's recommendation engine which recommends products based on buying patterns of millions of consumers.

In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling.

This progress in the field of machine learning is great news for the tech industry and humanity in general.

But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics.

Well, what if there was an easy to use a web service in the cloud - which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity?

The answer - is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.

The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.

In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.

Do you know what it takes to build sophisticated machine learning models in the cloud? 

How to expose these models in the form of web services? 

Do you know how you can share your machine learning models with non-technical knowledge workers and hand them the power of data analysis? 

These are some of the fundamental problems data scientists and engineers struggle with on a daily basis.

This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems.

If you’re serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you.

These data science skills are in great demand, but there’s no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects.

Startups and technology companies pay big bucks for experience and skills in these technologies They demand data science and cloud engineers make sense of their dormant data collected on their servers - and in turn, you can demand top dollar for your abilities.

You may be a data science veteran or an enthusiast - if you invest your time and bring an eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can demand as a data science engineer or a consultant. We are confident your investment will come back to you many-fold in no time.

So, if you're ready to make a change and learn how to build some cool machine learning models in the cloud, click the "Add to Cart" button below.

Look, if you're serious about becoming an expert data engineer and generating a greater income for you and your family, it’s time to take action.

Imagine getting that promotion which you’ve been promised for the last two presidential terms. Imagine getting chased by recruiters looking for skilled and experienced engineers by companies that are desperately seeking help. We call those good problems to have.

Imagine getting a massive bump in your income because of your newly-acquired, in-demand skills.

That’s what we want for you. If that’s what you want for yourself, click the “Add to Cart” button below and get started today with our “Machine Learning In The Cloud With Azure Machine Learning”.

Let’s do this together!

Who is the target audience?

• Data science enthusiasts
• Software and IT engineers
• Statisticians
• Cloud engineers
• Software architects
• Technical and non-technical tech founders