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eLearning Bundle - Implementing Machine Learning Online Course

eLearning Bundle - Implementing Machine Learning

by Certstaffix® Training

Length: 5 Course(s)      Price: $600/person (US Dollars)      Bulk Pricing: 6+ Contact Us      Access Length: 6 Months      Category: Machine Learning


This eLearning bundle consists of these 4 courses: Machine Learning for Absolute Beginners, Machine Learning In The Cloud With Azure Machine Learning, Machine Learning for OpenCV – Advanced Methods and Deep Learning, Machine Learning with TensorFlow, Building Predictive Models with Machine Learning and Python.
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  Online Machine Learning Classes: Learn at Your Own Pace

We will enroll you into a demo (Windows 10) eLearning session. Login information will be emailed to you.

Prefer a self-paced learning solution to fit your own schedule? Certstaffix® Training offers eLearning courses bundled together:

  • Learn at your own pace - Start and stop as it is convenient for you. Pick up where you left off.
  • Lecture utilizing video and recorded screen shots
  • 6 month subscription length
  • Any software needed for training is provided in an online lab environment.

  Detailed Training Topics

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IMPLEMENTING MACHINE LEARNING

Machine Learning For Absolute Beginners - 6 hours and 43 minutes

If you've ever wanted the Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading its reach and making our devices smarter. Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them. So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with machine learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects. The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that's not all. At the end of each unit, the course includes quizzes to help you evaluate your learning on the subject.

Machine Learning In The Cloud With Azure Machine Learning - 2 hours and 59 minutes

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. 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 it scaled dynamically based on our data volume and velocity? The answer is the new cloud service from Microsoft called Azure Machine Learning.

Machine Learning for OpenCV – Advanced Methods and Deep Learning - 2 hours and 25 minutes

Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains.This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems.

The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems.

Machine Learning with TensorFlow - 1 hour and 14 minutes

TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.This video addresses common commercial machine learning problems using Google’s TensorFlow library. It will not only help you discover what TensorFlow is and how to use it, but will also show you the unbelievable things that can be done in machine learning with the help of examples/real-world use cases. We start off with the basic installation of Tensorflow, moving on to covering the unique features of the library such as Data Flow Graphs, training, and visualization of performance with TensorBoard—all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section.

Building Predictive Models with Machine Learning and Python - 2 hours and 47 minutes

Machine Learning is no longer the inaccessible domain it used to be. There are over 100,000 Python libraries you can download in one line of code! This course will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist's toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems. Once you master the content of the course, you can level-up your knowledge of the Python Data Analytics and Machine Learning stack by exploring these recommended libraries. This course will guide you through the tools in the Python ecosystem that Data Scientists use to get results in a matter of hours - and with practice - in a matter of minutes. The best way to learn is through examples, and this course will guide you through all the steps needed to train and test your models by tackling several classifications and regression challenges. By the end of the course, you will be able to take the Python Machine Learning toolkit we cover and apply it to your own projects to deploy models in just a few lines of code.
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