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Tag: machine learning training courses

A Complete Guide For Deep Learning!

Posted on by Imarticus Learning

Deep Learning is known as neurally organized or as learning of various levels. It is one piece of an even more extensive type of group of the techniques used for machine learning in the aspect of learning and retrieving information, instead of undertaking the particular calculations. Also, learning could be directed, or semi-managed or even unsupervised.

Hence, careers in the field of Deep Learning renders organizations with different kinds of arrangements for systems in order to look after the issues of complex explanatory and also drives rapid developments in the counterfeit consciousness.

Complex undertakings such as- picture examination and discourse, can be performed with the help of models prepared by fostering calculations of deep learning amidst an immense amount of information.

These models of Deep learning are generally identified with the data preparing as well as with correspondence designs that are in a system of organic sensory, for example, the neural coding that attempts to characterize one connection between distinct data and the related reactions of neurons inside the brain. Therefore, a career in deep learning looks prospering.

What are the job positions that one can expect in the field of Deep Learning?

Mentioned below are the job positions that a person who specializes in deep learning can look out for:

  1. Research Analyst
  2. Data Scientist
  3. Neuroinformatics
  4. Image Recognition
  5. Research Scientist
  6. Deep Learning Instructor
  7. Full-stack deep learning web developer
  8. Process Engineer for Natural Language
  9. Software Engineer
  10. Data Analyst
  11. Data Engineer
  12. Bioinformatician
  13. Software Developer
  14. Research Fellow
  15. Applied Scientist
  16. A lead manager in Deep Learning

This shows that a career in Deep Learning has lots of options to make a future in.

Career Outlook

The information researcher hunts through enormous measures of unstructured as well as organized information in order to give fractions of knowledge; plus, it also helps to meet the particular business requirements or needs and objectives. Similar work needs to be done if you have pursued the Machine-Learning courses.

From where should you pursue the deep learning course?

Imarticus Learning is one of the best platforms to learn and help yourself make a future in the field of Deep Learning. Here, you will get to learn all the skills that are essential to becoming an expert in the field of Deep Learning. Because there are a number of skills and academic study required, Imarticus offers a ‘Machine Learning & Deep Learning Prodegree’, in association with the edtech partner, IBM.

It is the first-of-its-kind certification course of more than 145+ hours of training. This provides in-depth data science exposure, as well as, big data, machine, and deep learning as well. The meticulous curriculum-aligned as per the industry provides a comprehensive knowledge of Python as well as data science for a flourishing future and career in machine learning and big data as well. This program also stars seven projects of industry, various case studies as well as periodic interaction with industry leaders inside the ecosystem of machine learning.

Machine Learning and Information Security: Impacts and Trends

Posted on by Imarticus Learning

Machine Learning and Information Security: Impacts and Trends

Gone are the days when we needed to patiently sit and teach computers how to perform complex tasks that were backed by human intelligence. Today, the machine teaches itself– far from ‘magic’, it’s a tool that has revolutionized industries across the board today.

For context, machine learning is as significant a change for the world as the introduction of the Internet was. The future of machine learning encompasses more than just tech and related industries. Cybersecurity– more specifically, information security– has been heavily impacted by the introduction of machine learning in a mostly positive manner, but some grey areas exist.

What is Information Security?

InfoSec is the network of processes and systems designed for and deployed to safeguard confidential information, largely business-related, from destruction or modification in any way. InfoSec is not the same as Cybersecurity, albeit it is the part of it that is dedicated exclusively to data protection.

The types of Information Security span cloud security, cryptography, infrastructure protection and detection and management of vulnerabilities. Most Machine Learning training courses brief students about these facets, not least because they’re universal in their use across industries.

Machine Learning in InfoSec: Impacts and Trends

Automate repetitive tasks

Setting up ML algorithms to take care of everyday threats can help ensure a regular check on the underlying security. This also allows security analysts, supported by more complex algorithms, to focus their strength on bigger tactical fights an set up bulletproof systems. This frees up a lot of time on the team’s hands and cuts costs on holding onto employees for repetitive tasks alone.

Endpoint security control in mobile devices

With mobile passwords being the quickest and easiest springboard to accessing information worth selling, cybercriminals are increasingly preferring to target mobile devices. To counter this, machine learning techniques include ‘zero trusts’ no sign-on approaches that eliminate passwords and cloud-based authentication systems.

Predict and preempt strikes on systems

Predictive analytics is fast becoming a core facet of InfoSec systems today because continuous analysis and correlation mean better chances of recognizing patterns and threats ahead of the actual strike. Using AI and machine learning techniques to capture, analyze and classify data in real-time is a benefit that no other system has offered so far, least of all human systems. By identifying potential threats, businesses can prepare in advance by strengthening security, putting extra authentication processes in place and running audits.

Cloud-based security systems

In place of saving millions of customer data on chunky servers prone to breach, businesses are increasingly moving to cloud-based security systems. These systems allow all information to be kept in one place with hefty security barriers in place, with the help of machine learning, to prevent breaches. These systems keep the reins of authority in the hands of a few, making it easier to trace leaks, if any, and allow for timely intervention.

The future role of machine learning

In setting up a top-of-its-class, dynamic security landscape, machine learning plays several roles, both routine and complex. Machine learning training is the talk of the town today because companies want their employees to be more than capable of using machine learning data for the betterment of InfoSec at any organization.

10 Interesting Facts About Artificial Intelligence!

Posted on by Imarticus Learning

Artificial Intelligence has received a lot of focus and attention in the last couple of years. There has been a boom in the innovations that have artificial intelligence at its base. Obviously, the internet has played a crucial role in the development of artificial intelligence-enabled services.

Machine learning essentially an artificial intelligence technique, has been stirring new developments by creating new algorithms that mimic or support human behavior or decision-making capabilities, which are already in use, like Apple’s Siri, or the email servers which eliminate junk or spam emails. You can also see the use of machine learning in e-commerce websites that use it to personalize the search or use of the web experience of their customers.

It is interesting to comprehend the capabilities of machines. Very soon machines will have the capability to perform advanced cognitive functions, processing language, human emotions, the machines will be proficient in learning, planning, or performing a task as intelligent systems.

There is also a definite possibility that the tasks performed will be or can be more accurate than humans, thus artificial intelligence can boost productivity and accuracy, and impact economic growth. Imagine the impact it can have on medical procedures, the continued support it could lend to the disabled, increasing their life expectancy.

Artificial intelligence is a technology that can improve the world for the better, however, it also comes along with some challenges such as machine accountability, security, displacement of human workers, etc.


But right now before the possible alarming impact of artificial intelligence, we could in the today, the now, enjoy learning about some interesting facts.

 

Interesting Facts About Artificial Intelligence

  1. It is interesting to note that research on artificial intelligence is not only a few years ago, but the inception of AI also goes back to the 1950s. Alan Turning is coined as the father of AI, back in the day he invested a test based on natural language conversation with a machine.
  2. Did you know that a lot of video games that engage humans over time are based on a technique of artificial intelligence and is called Expert System? This technique is knowledge-based and can imitate areas of intelligent behavior, with a goal to mimic the human ability of senses, perception, and reasoning.
  3. Autonomous vehicles are no longer a thing of the far future. The knight rider might actually become a reality in as close as the next 2-3years or less. These cars are based on artificial intelligence to recognize the driving conditions and adapt the behavior. These cars are in the test phase, already developed and almost ready to hit the road.
  4. There is a race that is warming up between social media corporations over perfecting the use of artificial intelligence to enhance the customer experience. Facebook and Twitter are two companies essentially applying AI to match relevant content to the people. Leading this race is Google, coming across as one of the most preferred and reliable search engines.
  5. IBM has created a supercomputer based on AI, called Watson. One of the major challenges of creating Watson was the programming that needed to be done so that it could understand questions in most of the common languages and the ability to attend to those questions in real-time. The development is such that currently Watson is not only applied in various industries but was recently successful in teaching people how to cook.
  6. Sony created a robotic dog called Aibo, one of its first toys that could be bought and played with. It could express emotions and could also recognize its owner. This was the first of its kind, however, today you will find more expensive and evolved versions of the same.
  7. At the rate at which Artificial Intelligence is being adopted in various areas of our lives, it is predicted that it will replace 16% of our jobs over the next decade.
  1. Artificial Intelligence Training CoursesIt is a fact that with increased intelligence and ability to perform tasks with accuracy, over the next few years it is predicted that close to three million workers will be reporting to or will be supervised by “Robot-bosses”.

    With Machine learning and language recognition, it is no surprise that 85% of telephonic customer service jobs will be performed by computers and will not need human interaction.  By the dawn of 2020, it will be possible for all customer digital assistant to recognize people by face and voice.

Organizations and private sectors have recognized the opportunity that AI investments can have on the future of their businesses. Hence have set up major investments in the development of the same.

Finally, one must remember the anticipated impact of AI is on calculated assumptions and predictions.
However, one thing is clear, that AI in the future will impact the internet, its citizens, and economies.

Read More 
The Promises of Artificial Intelligence: Introduction

7 Key Skills Required For Machine Learning Jobs!

Posted on by Imarticus Learning

Overall, 2017 saw an upward trend in talent acquisition across Machine Learning. This will further increase in 2018.
With technology such as Machine learning, AI, and predictive analytics reshaping the business landscape, software products, aggregators, Fintech, and E-commerce will drive the demand for technology professionals in India.

Machine Learning is usually associated with Artificial Intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.

Now, are you trying to understand some of the skills necessary to get a Machine Learning job? A great candidate should have a deep understanding of a broad set of algorithms and applied math, problem-solving and analytical skills, probability and statistics, and programming languages.

Here is a list of key skill sets in detail:

Programming Languages like Python/C++/R/Java

If you want a job in Machine Learning, you will probably have to learn all these languages at some point. C++ can help in speeding code up. R works great in statistics and plots, and Hadoop is Java-based, so you probably need to implement mappers and reducers in Java.

Probability and Statistics

Theories help in learning about algorithms. Great samples are Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. You need to have a firm understanding of Probability and Stats to understand these models. Use statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.

Data Modeling & Evaluation

A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.)

Machine Learning Algorithms

Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will need to understand subjects such as gradient descent, convex optimization, quadratic programming, partial differential equations, and alike.

Distributed Computing

Most of the time, machine learning jobs entail working with large data sets these days. You cannot process this data using a single machine, you need to distribute it across an entire cluster. Projects such as Apache Hadoop and cloud services like Amazon’s EC2 makes it easier and cost-effective.

Advanced Signal Processing Techniques

Feature extraction is one of the most important parts of machine-learning. Different types of problems need various solutions, you may be able to utilize really cool advanced signal processing algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets.

Other skills:

  1. Update yourself:

    You must stay up to date with any up and coming changes. It also means being aware of the news regarding the development of the tools (changelog, conferences, etc.), theory, and algorithms (research papers, blogs, conference videos, etc.).

  2. Read a lot:

    Read papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data.

The next question you would have is, “What can I do to develop these skills?” Unless you already have a strong quantitative background, the road to becoming a Machine Learning Specialist will be a bit challenging – but not impossible.

However, if it’s something you’re sincerely interested in and have a passion for Machine Learning and lifelong learning, don’t let your background discourage you from pursuing Machine Learning as a career.

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