Machine Learning Courses Archives - Page 5 of 9 - DexLab Analytics | Big Data Hadoop SAS R Analytics Predictive Modeling & Excel VBA

Tag: Machine Learning Courses

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

Posted on by Dexlab

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.

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.

Get a Python certification today from DexLab Analytics – a premier data science with python training institute in Delhi NCR.

It was 1996, when the officials from Python Org, including Konrad Hinsen (He was then involved in the Numerical Python project, but currently working as a researcher in theoretical physics at the French Centre National de la Recherche Scientifique (CNRS). He is also the author of ScientificPython, a general-purpose library of scientific Python code) started developing MMTK. They initially had a brush off with mainstream simulation packages for biomolecules penned down by Fortran, but those packages were too clumsy to implement and especially modify and extend. In order to develop MMTK, modifiability was a crucial criterion undoubtedly and they gave it utmost attention.

groel_deformation-web

The language chosen

The selection of language took time. The combination of Python and C was an intuitive decision. The pundits of Python were convinced that only a concoction of a high-level interpreted language and a CPU-efficient compiled language could serve their purpose well, and nothing short of that.

For the high-level segment, Tcl was rejected because it won’t be able to tackle such complex data structures of MMTK. Perl was also turned down because it was made of unfriendly syntax and an ugly integrated OO mechanism. Contrary to this, Python ranked high in terms of library support, readability, OO support and integration with other compiled languages. On top of that, numerical Python was just released during that time and it turned out to be a go-to option.

Now, for the low-level segment, Fortran 77 was turned down owing to its ancient character, portability issues and low quality memory management. Next, C++ was considered, but finally it was also rejected because of portability issues between compilers in those days.

 

The architecture of library

The entire architecture of MMTK is Python-centric. For any user, it will exude the vibes of a pure Python library. Numerical Python, LAPACK, and the netCDF library functions are observed extensively throughout MMTK. Also, MMTK offers multi-threading support for MPI-based parallelization for distributed memory machines and shared memory parallel machines.

The most important constituent of MMTK is a bundle of classes that identify atoms and molecules and control a database of fragments and molecules. Take a note – biomolecules (mostly RNA, DNA and proteins) are administered by subclasses of the generic Molecule class.

Extendibility and modularity are two pillars on which Python MMTK model is based. Without going under any modification of MMTK code, several energy terms, data type specializations and algorithms can be added anytime. Because, the design element of MMTK is that of a library, and not some close program, making it easier to run applications.

Note Bene: MMTK at present includes 18,000 lines of Python code, 12,000 lines of hand-written C code, and several machine-generated C codes. Most of the codes were formulated by one person during eight years as part of a research activity. The user community provided two modules, few functions and many ideas.

For more information, peruse through Python Training Courses Noida, offered by DexLab Analytics Delhi. They are affordable, as well as program-centric.

 

This article is sourced from –  www.python.org/about/success/mmtk

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

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

Posted on by Dexlab

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.

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

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

Posted on by Dexlab

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.

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.

Looking for an accelerated Machine Learning course in India? DexLab Analytics is your go-to destination.

2

According to research, there exists a potent relationship between degree of automation and profit generation –the companies that have automated their business processes get to enjoy the perks of higher revenue growth six times more than those who didn’t. Also, they are twice more likely to supersede their pre-determined financial goals.

Now coming to our chief area of concern, how humans deliver a significant impact in coordinating automation with AI projects – their process of imagination, understanding, leadership quality, emotional intelligence and versatile management skills outweighs the very fundamentals of technology, hence it is said that for successful digital transformation, investment on human workforce is indispensable. To derive the best results, it is important to shell out money on crucial human elements that will lead to fuller automation and successful AI implementation.

Automation makes people more human. It liberates them from doing humdrum, repetitive work that pulls them back from doing something productive, or creative. Without AI, businesses can’t work or obtain competitive advantage in the future, making them defenseless. Nevertheless, you can’t expect AI to do a whole bunch of things for you, jobs that require creativity, empathy, critical thinking, leadership, artistic expression are meant for humans, and no automation will be able to fulfill those qualities. Humans are the meat and potatoes for AI, and we can’t agree more!

For better successful ventures, it is imperative to make humans and machines work together – it will only make us better in our job profiles. Also, this kind of relationships best augments the deep-rooted potentials of human beings, making humans more humane.

Research also says in the coming days, creative human skills will garner even more demand. Comprehensive training and skill development is highly advisable to remain ahead in the rat-race of advanced technology. Skills like, creativity, emotional intelligence, collaboration, critical thinking, communication and cognitive flexibility will become key skills to grab specific job titles. 

An advice to make: before entering the workforce, it is better seek broad educational experiences in the field of data science or computer science, or your preferred field of study, and then amp up your CV with a professional, program-centric Machine Learning training Delhi. In this way, you will be always updated and stay ahead of the curve.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

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

Posted on by Dexlab

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.

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.

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

How to Leverage AI Strategy in Business?

Posted on by Dexlab

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.

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.

2

How?

No second thoughts, a majority of data architects are still learning AI technology so as to develop their adoption strategy. AI is an ever-evolving technology – constant new developments and breakthroughs are emerging out every day, hence crafting a particular strategy might be difficult at times. Luckily, tech oracles like Whit Andrews, VP distinguished analyst, Gartner, are able to pin down distinct trends that determines the direction of AI in the business, while leveraging its capabilities to the fullest.

Browse through our intensive Data Science with Python Courses – they are a real treat to satiate the analytics hunger!

Check out these three trends that Andrews focuses on to develop formidable AI strategy for your business setup:

Data Science and Machine Learning: In What State They Are To Be Found? – @Dexlabanalytics.

AI will mushroom normal, contextual user-machine interfaces

Google Home and Amazon Echo have penetrated the homes of thousands, taking the consumer space by a storm. Human-computer interaction is now shifting its base from tactile touchscreens and keyboards to voice – the voice recognition is not only limited to distinct commands but deciphers normal human speech.

Natural language processing (NLP) is the reason behind such intrinsic advancements – and we can’t thank more! NLP and natural language generation have improved operations. The workers employed in parts of Eastern Europe can now talk to their system in their own language and grasp the things that need to be done to complete their designated work, making the whole system work seamlessly.

Incredible Tech Transformation: How Machine Learning is changing the Scope of Business – @Dexlabanalytics.

IoT is the future of AI and Fluid Application Integration

IoT devices gather data from the real world, exchange the data, and perform tasks sent through the internet. In general, they are simple in make but when combined with AI, they can rock the world. How would it be if you find an AI-powered IoT that receive orders, grab products and pack them in containers to be shipped across! Impressive, right?

Besides, AI works upon boosting existing organization applications. AI is like a magical stone that improves customer engagement and support, and Bank of America’s chatbot Erica is a perfect example of that.

The Math Behind Machine Learning: How it Works – @Dexlabanalytics.

A complex computing ecosystem will surface out with AI at the center

While companies diversify their systems, computing ecosystem strives to be the beacon of hope – it includes an intricate mix of customers, staffs, IoT devices, applications and data, coupled with AI in the nucleus. This will ensure:

  • Better interaction between people and devices
  • Proper communication between applications
  • And everything in between

No wonder, such ecosystems presents organizations more integrated automation, deeper insight, and better customer experience. Moreover, Gartner has predicted that more virtual agents will get involved in a majority of business interactions between organizations and individuals by 2020 – so the rise of machines is here, and we are extremely excited about it!

Help develop a well-devised AI strategy – with DexLab Analytics. Our consultants will feed you meaningful information on everything related to AI and machine learning. Our machine learning training course is impressive, and if you want to excel in machine learning training, drop by DexLab Analytics. We have a lot of things in store for you!

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

R is Gaining Huge Prominence in Data Analytics: Explained Why

Posted on by Dexlab

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 Delhi, drop by DexLab Analytics.

2

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 Gurgaon today from DexLab Analytics. It will surely help your career move!

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

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

Posted on by Dexlab

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.

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.

2

Nevertheless, a lot of all that depends on how machine learning is going to evolve in the coming five to ten years and what implications it would bring into the hiring or recruitment strategies in the long run. And the best area to start off this discussion is unsupervised machine learning, where intricate frameworks are allotted large datasets and asked to draw patterns without human help to figure out what the software needs. With minimum human interference, the scalability of this mode of ML is the highest.

How to Assess Clustering Tendency: Unsupervised Machine Learning – @Dexlabanalytics.

Supervised or Unsupervised? Which is better?

Supervised ML needs human help to develop large sets of training data and corroborate the results of the training. Speech Recognition is the perfect example of such ML. But it is challenging to procure and classify vast data for supervised training. As a result, unsupervised ML is the key to the future – it reduces such interaction to a large extent. The minimum involvement of human beings suffices to be a boon – but take a note, a data scientist is required to select the data that is to be evaluated.

Unsupervised learning also needs a human touch to assign values to data structures and clusters. Hence, we cannot say for sure they are totally human-error free. Instead, we should focus more to ace up the performance of humans in tackling data for own interests.

In this context, “I think, right now, that people are jumping to automation when they should be focused on augmenting their existing decision process,” says David Dittman, director of business intelligence and analytics services at Procter & Gamble. “Five years from now, we’ll have the proper data assets and then you’ll want more automation and less augmentation. But not yet. Today, there is a lack of usable data for machine learning. It’s not granular enough, not broad enough.”

The Math Behind Machine Learning: How it Works – @Dexlabanalytics.

How to become a vendor from a consumer

A portion of what drives an incessant demand for data scientists is the pressing need for data to turn ML more productive. Mike Gualtieri, Forrester Research’s vice president and principal analyst for advanced analytics and machine learning thinks that some organizations, exactly five years from now might turn into vendors -“Boeing may decide to be that provider of domain-specific machine learning and sell [those modules] to suppliers who could then become customers,” he says. Like him, Dittman also sees the thriving combination of Data and ML code as being a highly sellable product, more so a potent new source of revenue for organizations – “Companies are going to start monetizing their data,” he explains. “The data industry is going to explode. Data is absolutely exploding, but there is a lack of a data strategy. Getting the right data that you need for your business case, that tends to be the challenge.”

Irrespective of what the future holds, technology is grooming to become an extravagant revolving door of striking innovation, and the only way to nab this technology is by making ourselves technology-friendly. For excellent business analytics course in Delhi, DexLab Analytics provides the perfect platform to deliver student-friendly education on data analytics at affordable prices. Dig into our data analyst course by clicking on our homepage.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Classifying Bank Customer Data Using R? Use K-means Clustering

Posted on by Dexlab

Before delving deeper into the analysis of bank data using R, let’s have a quick brush-up of R skills.

 

Classifying Bank Customer Data Using R? Use K-means Clustering

 

As you know, R is a well-structured functional suite of software for data estimation, manipulation and graphical representation.

Continue reading “Classifying Bank Customer Data Using R? Use K-means Clustering”

5 Hottest Online Applications Inspired by Artificial Intelligence

Posted on by Dexlab

5 Hottest Online Applications Inspired by Artificial Intelligence

Artificial Intelligence projects, applications and platforms are being churned out from every corner of the world. A majority of them now possess the ability to break loose lab life and hit mainstream trends, making an appearance in myriad online tools, open source APIs and mass gadgets.

Though the machines are yet to take over our lives, they are filtrating their way into our lives, influencing day-to-day activities, be it work or entertainment. From personal assistants like Alexa and Siri, to self-driving vehicles powered by predictive modeling and more intense and fundamental machine learning technologies, a wide set of applications of AI are in use of late.

Feed yourself with Machine Learning Using Python technology, only from DexLab Analytics.

We perused through a handful number of AI apps so that we can enlist the ones that are more practical and thus really deserving! Let’s leverage piles of data with these effective applications:

Siri

As per Creative Strategies report, 70% of iPhone users have used Siri at least for once or sometimes, but everyone has tried it at least. We are here to tell you don’t hire a personal assistant, instead implement Siri.

siri

This voice-powered virtual assistant makes business operations smoother and hassle-free, while making your workday more productive. The software is activated by voice, and it is at present available in 20 languages.

Alexa

Developed and powered by Amazon for Amazon Echo intelligent speaker, Alexa, a robust voice service was launched in 2014. It can help you in ordering supplies, translating and controlling office’s vacuum.

amazon-echo

However, connecting your Echo to IFTTT may allow you to coordinate with services that aren’t supported originally by the Echo, while allowing you to integrate multiple actions into a single command to the Echo.

Google Now

This is one of the most popular artificial intelligence applications. Google Now functions by keeping a tab on your calendar, mail, web searches and lot more, along with sending relevant alerts and news on your device as and when detected. It can also carry out tasks, and answer queries, based on voice commands.

google-now

The best part of this application is that you don’t have to log in to use it. Just set up alerts that will be sent to the device, and that’s all. At present, it is available in English and is considered a tailing rival of Siri.

Cortana

If you know the exact way to maneuver it, Cortana would be the most effective AI personal assistant. It can perform all sorts of things, right from dictating and sending emails, tracking flights to searching something on the internet or checking weather forecasts. The more time you spent on it, its functionality gets better and better.

cortana

Even, the company is so impressed by its services that it has integrated the service into Power BI, its most intuitive BI tool.

Braina

Brain Artificial, aka Braina is self-regulating software, which enables easy hands-free operation in your computer to perform basic tasks by listening to voice based commands in English language.

braina-1

Braina enjoys a certain edger over its run of the mill competitors as it can precisely work with a variety of accents, which is not so common. The pro version is equipped with a bonus of deep learning – it is programmable as well as observes user behavior over time.

Hope, AI applications serves the humanity well!

Check out some more interesting stuff on Machine Learning at DexLab Analytics. We offer world-class machine learning courses in Delhi for all your data aspirations. Come, explore!

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Call us to know more

Gurgaon

+91 8676079880

Kolkata

+91 9903662244

Quick Links

Our Courses

Important dates

Copyright © 2024 DexLab Solutions Corporation | Privacy Policy | Legal Disclaimer | Terms and Conditions