Category Archive: Machine Learning Training

5G Mobile Innovation: 3 Key Takeaways That Every Business Leader Should Know About

5G stands for fifth generation wireless connectivity based on the IEEE 802.11ac standard of broadband technology, though an official standard is yet to be fixed. It comes with a lot of promises – better bandwidth, faster speed and lower latency that affects (positively) customers and businesses, as well.

 
5G Mobile Innovation: 3 Key Takeaways That Every Business Leader Should Know About
 

Although it isn’t expected to roll out until 2020, a large number of companies have already started prepping up to adopt and incorporate 5G mobile connectivity into their business scopes and operations. As the biggest shift in technology is looming right ahead, business leaders around the world are leaving no stone unturned to fathom the rich impact of 5G on several next-gen techs, like self-driving cars and cloud computing.

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Here’s Why Indian Techies Need to Imbibe Rich AI and Data Science Skills to Ride High This 2018

Amid growing anxiety over machines replacing human intelligence, Indian IT sector is seeking out expert skills in new technologies, involving big data, artificial intelligence and machine learning. There exists a high demand for skills in such newer streams of technology, which now forms the backbone of businesses. It’s not like these jobs appeared out of thin air; after being labeled as “niche skills” for several years, they are now making their way into the mainstream industry. 

 
Here’s Why Indian Techies Need to Imbibe Rich AI and Data Science Skills to Ride High This 2018
 

This year, this trend is going to gain more momentum. It is expected to create 180000 to 200000 new jobs in 2018, mostly related to these new technologies – Alka Dhingra, the general manager of IT staffing at TeamLease Services, stated. It is equally applicable for both large service organizations as well as budding startups.

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Stories of Success: Molecular Modeling Toolkit (MMTK), Open Source Python Library

Welcome again!! We are back here to take up another thrilling topic and dissect it inside out to see what compelling contents are hidden within. And this time we will take up our newly launched Python Programming Training Module – Python, invented by Guido Van Rossum is a very simple, well-interpreted and goal-specific intensive programming language.

 

Stories of Success: Molecular Modeling Toolkit (MMTK), Open Source Python Library

 

Programmers love Python. Since there is zero compilation step, debugging Python programs is a mean feat. In this blog, we will chew over The Molecular Modeling Toolkit (MMTK) – it’s an open source Python library for molecular modeling and simulation. Composed of Python and C, MMTK eyes on bio-molecular systems with its conventional standard techniques and schemes, like Molecular Dynamics coupled with new techniques based on a platform of low-level operations.

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Chief Data Officer Is the Next “Commander” To Join the Digital Kingdom and Here’s Why

An over-empowering digital transformation is here and it is wreaking havoc in the C-Suite. CDOs have started taking a front line in managing and pushing new tech like AI and machine learning to alter business landscapes forever.

 

Chief Data Officer Is the Next “Commander” To Join the Digital Kingdom and Here’s Why

 

As a matter of fact, this promising job title has existed for years, even decades – mostly in the financial market. But now when data is being generated at record high speeds, the job role of the CDO is emerging out bigger and better. No more a single person or a general crew is enough to tackle such challenging data issues – to fulfill complicated data management tasks, management is now looking up to specialized data experts.

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Google is Back in China! It Decides to Open an AI Lab in the Far-East

Google is Back in China! It Decides to Open an AI Lab in the Far-East

 

Google is strengthening its artificial intelligence base, including China.

 

And it is so doing by establishing a new AI research center in Beijing. Google is digging deep into China, where it contravened the government in 2010 committing a spectacularly principled act of self-sabotage by refusing to self-censor search content and later found most of its services to be blocked. The company’s decision to return back to China is more about safeguarding its future, and acknowledging the supreme importance of technology’s most competitive field: AI.

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Humans and Automation Shares an Everlasting Bond for a Successful Tech Future

“God created the world in seven days, because he didn’t have to port anything from legacy systems” – the CEO of a blue chip IT company once quoted. A similar idea was even echoed by MIT’s former director of computer science and AI, Mr. Rodney Brooks who penned down an article “Seven Deadly sins of AI Predictions,” which largely focused on the rate of deployment and the influence of technology over it.

 
Humans and Automation Shares an Everlasting Bond for a Successful Tech Future
 

But for any technological revamp, humans are the key ingredient for successful implementation of AI – because they are the ones who have invented such striking tools of automation with their own wit and determination. AI has enhanced productivity, coupled with raising standards of living. Companies all across the globe are recognizing the benefits of AI, and contemplating investments in this budding field of science to trigger greater competitiveness.

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Bad Data is Really Bad for Machine Learning: Here’s Some Ways to Fix It

The quality of data is the talisman of decision-making. Irrespective of the goals, the key to better decision-making lies in the quality of data. As it’s said, bad data takes its toll on organization’s data endeavors – as a result, only 25% of businesses are able to optimize the use of data for revenue generation, despite a volley of resources being thrown at them.

 
Bad Data is Really Bad for Machine Learning: Here’s Some Ways to Fix It
 

IBM has reckoned that bad data costs companies some $3.1 billion a year in the US alone, while as per Experian’s Data Quality survey, 83% of organizations alleged their revenue is affected by imprecise and incomplete customer or prospect data.

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How to Leverage AI Strategy in Business?

Everyday some company or the other are deploying AI into their systems – whether its Spotify’s machine learning program or Bank of America’s chatbot Erica – it seems AI has broken the shackles and left the machine room to enter the mainstream business.

 

How to Leverage AI Strategy in Business?

 

Today’s AI algorithms are framed on remarkably factual machine sight, speech and hearing, and they have easy access to global cache of information. Thanks to Deep Learning, meteoric growth in data and other cutting edge AI techniques, AI performance is staggeringly improving. With these developments, it may seem possible for CIOs, enterprise architects, application managers who are still in nascent stage in gaining expertise in AI to feel like they are lagging behind somewhere. Contrarily, they are doing well for themselves.

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R is Gaining Huge Prominence in Data Analytics: Explained Why

Why should you learn R?

Just because it is largely popular..

Is this reason enough for you?

Budding data analytics professionals look forward to learn R because they think by grasping R skills, they would be able to nab the core principles of data science: data visualization, machine learning and data manipulation.

Be careful, while selecting a language to learn. The language should be capacious enough to trigger all the above-mentioned areas and more. Being a data scientist, you would need tools to carry out all these tasks, along with having the resources to learn them in the desired language.

In short, fix your attention on process and technique and just not on the syntax – after all, you need to find out ways to discover insight in data, and for that you need to excel over these 3 core skills in data science and FYI – in R, it is easier to master these skills as compared to any other language.

Data Manipulation

As rightly put, more than 80% of work in data science is related to data manipulation. Data wrangling is very common; a regular data scientist spends a significant portion of his time working on data – he arranges data and puts them into a proper shape to boost future operational activities. 

In R, you will find some of the best data management tools – dplyr package in R makes data manipulation easier. Just ‘chain’ the standard dplyr together and see how drastically data manipulation turns out to be simple.

For R programming certification in Pune, drop by DexLab Analytics.

Data Visualization

One of the best data visualization tools, ggplot2 helps you get a better grip on syntax, while easing out the way you think about data visualization. Statistical visualizations are rooted in deep structure – they consist of a highly structured framework on which several data visualizations are created. Ggplot2 is also based on this system – learn ggplot2 and discover data visualization in a new way.

However, the moment you combine dplyr and ggplot2 together, through the chaining technology, deciphering new insights about your data becomes a piece of cake.

Machine Learning

For many, machine learning is the most important skill to develop but if you ask me, it takes time to ace it. Professionals, who are in this line of work takes years to fully understand the real workings of machine learning and implement it in the best way possible.

Stronger tools are needed time and often, especially when normal data exploration stops producing good results. R boasts of some of the most innovative tools and resources.

R is gaining popularity. It is becoming the lingua franca for data science, though there are several other high-end language programs, R is the one that is used most widely and extremely reliable. A large number of companies are putting their best bets on R – Digital natives like Google and Facebook both houses a large number of data scientists proficient in R. Revolution Analytics once stated, “R is also the tool of choice for data scientists at Microsoft, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing and Finance departments.” Besides the tech giants, a wide array of medium-scale companies like Uber, Ford, HSBC and Trulia have also started recognizing the growing importance of R.

Now, if you want to learn more programming languages, you are good to go. To be clear, there is no single programming language that would solve all your data related problems, hence it’s better to set your hands in other languages to solve respective problems.

Consider Machine Learning Using Python; next to R, Python is the encompassing multi-purpose programming language all the data scientists should learn. Loaded with incredible visualization tools, machine learning techniques, Python is the second most useful language to learn. Grab a Python certification Pune today from DexLab Analytics. It will surely help your career move!

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

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Incredible Tech Transformation: How Machine Learning is changing the Scope of Business

Machine Learning coupled with data analytics is modifying the norms of how business handles crucial data. Insights into ML and AI is already reaping benefits in transforming vast pools of data – curated by dexterous data pundits into meaningful, relevant analytic results that would have escaped clumsy human analysis, previously.

 
Incredible Tech Transformation: How Machine Learning is changing the Scope of Business
 

Today, the combat weapon of Machine Learning has started to influence the entire business world. While many organizations have grasped the bounties of this hi-tech tool of learning, few are left to fathom how it would affect the way they do business. The automation process is a completely data-driven task – ideal to change enterprises into vendors – by turning lessons learnt into advanced algorithm programs worthy of licensing to software and service providers for good money.

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