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Facebook is planning to evaluate its quest for generalised AI

Facebook Artificial Intelligence Researchers

A major misconception about artificial intelligence is the fact that today’s robots possess a very generalized intelligence, however, we are fairly efficient in leveraging large datasets to accomplish otherwise complex tasks. Nevertheless we still fail and fall flat at the prospect of replicating the breadth of human intelligence.

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We will need our systems to be able to communicate and will be able to learn through language effectively even when they lack in context and discussing thing in undefined terms.

Furthermore, such systems should be capable of learning up new skills, fairly simply. As per Facebook this skill set is called “learning to learn”. Present machine learning models may be trained on data and be used for classifying defined objects. We can also make use of transfer learning to quickly adapt a model to achieve the same task on the new data, however our machines cannot completely teach themselves without heavy to moderate intervention from the developers.

It is in general agreed upon, that in order to generalize across several tasks, a program should be capable of compositional training. And that is of storing and recombination solutions to sub-problems across the different tasks, as per the team from Facebook.

As per Facebook they consider these capabilities to be of more of a prerequisite to being a generalized AI than the true Turing test. Alan Turing created the original Turing test in the 1950s. It is usually understood to be a means of assessing machine learning intelligence with respect to human intelligence.

However, with the maturation of the field of Ai the Turing test has lost a lot of its relevance. Facebook hopes to offer a nice alternative way to think about the necessary requirements of a modern generalized AI which should be less of a research distraction than the more rigid Turing Test.

The team at FAIR which include – Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic and Tomas Mikolov have also developed another open source platform for the testing and training of AI systems.

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How to Parse Data with Python

How to Parse Data with Python

Before we begin with our Python tutorial on how to parse data with Python, we would like you to download this machine learning data file, and then get set to learn how to parse data.

The data set we have provided in the above link, mimics exactly the way the data was when we visited the web pages at that point of time, but the interesting thing about this is we need not visit the page even. We actually have the full HTML source code, so it is just like parsing the website without the annoying bandwidth use.

Now, the first thing to do when we start is to correspond the date to our data, and then we will pull the actual data.

Here is how we start:

import pandas as pd
import os
import time
from datetime import datetime

path = "X:/Backups/intraQuarter"

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As given above, we are importing the Pandas for the Pandas module, OS, that is so we can interact with the directories, date and time for managing the date and time information.

Furthermore, we will finally define the path, which is the path to the intraQuarter folder than one will need to unzip the original zip file, which you just downloaded from the website.

def Key_Stats(gather="Total Debt/Equity (mrq)"):
    statspath = path+'/_KeyStats'
    stock_list = [x[0] for x in os.walk(statspath)]

We began our functions, with the specification that we are going to try to collect all the Debt/equity values.

The path to the stats directory is Statspath.

To list all the contents in the directory, you can use stock_list which is a fast one-liner for the loop that uses os.walk.

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Then the next step is to do this:

    for each_dir in stock_list[1:]:
        each_file = os.listdir(each_dir)
        if len(each_file) > 0:

Mentioned above is a cycling through of directory (which is every stock ticker). Then the next step is to list “each_file”, which is each file within that very stock’s directory. If in case the length of each_file which is in fact is a list of all of the files in the stock’s directory, is greater than 0 only then will we want to proceed. However, there are some stocks with no files or data:

            for file in each_file:

                date_stamp = datetime.strptime(file, '%Y%m%d%H%M%S.html')
                unix_time = time.mktime(date_stamp.timetuple())
                print(date_stamp, unix_time)


Finally, at the end, we must run a loop that pulls the date_stamp, from each file. All our files are actually stored under their ticket, with a file name for the exact date and time from which the information is being taken out.

It is from there that we will explain to date-time what the format for our date stamp is, and then we will convert it to a Unix time stamp.

To know more about data parsing or anything else in python, learn Machine Learning Using Python with the experts at DexLab Analytics.

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A robot too close to humans! Story of BINA 48

BINA 48 is the world’s most renowned and highly sought after humanoid robot in America. You can visit her there, by driving down a long winding dirt road just west of the Lincoln Gap in Bristol, Vt. Where sits two large yellow houses on a sprawling property that features ten solar panels and a dock over-looking the sunlit pond filled with trout, a homely porch decorated with rocking chairs.

Advances in Machine Learning and Data Analysis Bina 48


In the smaller of the two houses resides BINA 48, who is one of the most sought after humanoid who is based on a real personality – Bina Rothblatt.

Continue reading “A robot too close to humans! Story of BINA 48”

How to Assess Clustering Tendency: Unsupervised Machine Learning

How To Assess Clustering Tendency: Unsupervised Machine Learning

The meaning of clustering algorithms include partitioning methods (PAM, K-means, FANNY, CLARA etc) along with hierarchical clustering which are used to split the dataset into two groups or clusters of similar objects.

A natural question that comes, before applying any clustering method on the dataset is:

Does the dataset comprise of any inherent clusters?

A big problem associated to this, in case of unsupervised machine learning is that clustering methods often return clusters even though the data does not include any clusters. Put in other words, if one blindly applies a clustering analysis on a dataset, it will divide the data into several clusters because that is precisely what they are supposed to do. Continue reading “How to Assess Clustering Tendency: Unsupervised Machine Learning”

Here Are Four Predictions For AI This 2017!

Last year was the year, which saw artificial intelligence, went mainstream.


Here Are Four Predictions For AI This 2017!


By that, we do not mean just getting filtered raunchy photos on Twitter or getting the fake news suggestions on Facebook.

Here is what to look for in Artificial Intelligence for this New Year:

  • Driven by unprecedented financial support (along with a growing open source ecosystem), founders have been delivering artificial intelligence start-ups at a record high rate.
  • GE, Google, Intel, Microsoft, Facebook, Apple, Salesforce and Samsung, and several other name brands made rigorous AI investments last year.
  • There are now five million homes, which, are talking about their music and shopping choices with the help of Alexa from Amazon.
  • There is a whole new department of U.S. Department of Transportation Committee for self-driving cars. Even a few years ago, there were people talking about 2025 or so for the accessibility of self-driving cars (of level 5 autonomy), but this is a reality now, much before we could reach 2020. It is also amazing to think that self-driving cars may whittle down the 1.2 million annual deaths from automobiles.
  • Also in other interesting news, two AI unicorns just grew their horns, the Cylance in Silicon Valley and iCarbonX in China.
  • Also more than one-fifth of the MIT 50 smartest companies list, include AI as a core approach these days.

Continue reading “Here Are Four Predictions For AI This 2017!”

5 Major Problems in AI (Artificial Intelligence)

5 Major Problems in AI (Artificial Intelligence)

Before we get started with the topic, let us first get an idea about its background. Have you ever given a thought as to how many cats does it take to identify one cat?

In this article we will cover the five types of problems that people face with Artificial Intelligence (AI) i.e. we will address the all important question of – in which situation must one make use of AI (artificial intelligence)?

To have a better understanding of such concepts you can take up a Machine Learning course in Delhi.

Here is some background:

Just some time ago, we conducted a strategy workshop for a bunch of senior executives who are running a large multinational company. In that workshop, someone asked this question – “How many cats will it need to identify a cat?”

In this post, we will discuss the problems which can be uniquely resolved through Artificial Intelligence. While this may not be the exact taxonomy, but it still is pretty comprehensive. The main reason we have added extra emphasis on Enterprise AI issues, because we believe that this subject will have a deep impact on many mainstream applications, but despite that a lot of media attention focuses at the more esoteric avenues. Further, information about these concepts are available in our Machine Learning training course.

But before we delve into AI application types, we must discuss the main distinguishing characters between AI / Deep Learning / Machine Learning.

The term Artificial Intelligence by definition implies that machines can reason with the help of this feature. However, here is a better more complete list of AI characteristics:

  1. AI is capable of reasoning: they can solve complex problems through logical deductions on their own
  2. AI has knowledge: the capability to represent knowledge about the world or our understanding of it, that there are numerous events, entities, and varied situations that occur in the world and such elements have properties, which can be categorised.
  3. AI can plan: they have the ability to set and achieve targets. A specific state of the planet, which we desire along with a sequence of actions that can be undertaken which will help us, progress towards it.
  4. AI can communicate: they have the capability to comprehend well-written and spoken language.
  5. AI has its own perception: they have the ability to deduce things about their surrounding world through the visual images, sounds and other external sensory inputs just like us humans!

With developments in Deep Learning algorithms, AI is driven forward. The various deep learning algorithms can detect numerous patterns without having any prior definition of these features. And in a broader sense, Machine Learning means the application of any algorithm which can be applied against a set of data to discover a pattern within the same. Such algorithms have features like supervised, unsupervised, classification, segmentation, or regression. Moreover, while they are very popular, there are many reasons why Deep Learning algorithms may not make other Machine Learning algorithms.

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The 5 major types of problems with AI:

Now that we have some background knowledge, we can now discuss the five major types of problems with AI:

Domain expertise: troubles involving reasoning based on a complex body of knowledge

This consists of tasks that are based on learning several knowledge bodies like financial, legal, and more, and then formulating a process where the machine will be able to simulate as an expert in the given field.

Domain extension: problems surrounding extension of a complex body of knowledge

In this case, the machine learns a complex body of knowledge like information regarding the existing medication and much more, and then suggests new ideas to the domain itself, like for instance new drugs for curing diseases.

Complex planning: projects that require complicated planning

There are many logistics and scheduling projects, which can be done by current (non AI) algorithms. But as optimization keeps developing and gets more complex AI would slowly grow.

Proficient communicator: tasks that involve developing existing communications

AI and deep learning can offer benefits to many communication modes such as intelligent agents, automatic and much more.

Fresh perception: projects that involve a unique perception

Deep learning and AI can be capable of producing newer forms of perception which enables new services like autonomous automotives and more.

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Why Businesses Must Adapt to AI To Thrive in The Market?

Why Businesses Must Adapt to AI To Thrive in The Market?

It is a fact that Artificial Intelligence is no longer just a sci-fi hype anymore, but is in fact a major reality. The approached based on Artificial Intelligence like Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning are slowly emerging to be highly realistic technologies within the industry.

Today we have a very efficient NLP engine system, which is as powerful as ML and deep learning algorithms available. In a recent article, published on WIRED we read about the perpetual death of code (i.e. programs and programming) and how we will soon be training in systems, just as the way we train our pets!


Machine Learning is the same as learning from examples and experiences just like in real life. It is all about digesting huge volumes of data. We see great new developments within the industry such as IBM and Memorial Sloan Kettering are training Watson in things like Oncology by making use of massive amounts of patient medical records throughout the world. Watson learns from knowing how doctors are treating patients with cancer around the globe, just as how a medical student learns but only on a much larger scale.

Another great example of machine learning is from Japan. The farmers here are cultivating crispy fresh cucumbers with several prickles on them. The straight and thick cucumbers with a vibrant colour and lots of prickles are known to be of premium grade quality. Each cucumber has a different colour, quality, shape and freshness. They are sorted into nine different classes based on their size, shape, texture, colour, the amount of small scratches and whether or not they are crooked, along with the most important part of the amount of prickles on them. However, there is not well-defined instruction set for the classification of cucumbers in Japan.

AI Trends

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A farmer and agricultural scientist Makoto Koike has been studying this problem for several years now, and has been helping his farmer parents sort out cucumbers. But now with the use of Google’s TesorFlow based machine learning algorithm, he has been able to develop a system that learns from the precise way his parents have been sorting cucumbers in their farm. For achieving this, he had trained his system by using 7000 images of cucumbers that have been sorted by his mother, and at present the system classifies cucumbers with a much better rate of success and that too at a rapid speed.

Companies like Capgemeni have been making use of the technology of IBM’s Watson to improve efficiency and effectiveness in the resource supply chain.

Image Source:

Image Source:

It is predicted that the AI wave will definitely take the industry by storm and have a profound impact on almost all business and transform the present technology climate.

Moreover, we need to quickly turn our businesses into an AI-based approach along with implementation of Machine Learning, which will be supported by NLP and OCR (optical character recognition), speech recognition, and image recognition.

There are three trends in favour of the present technology service providers and their team of workers:

  1. The global expense on technology is increasing. So, technology enterprises will increase their size and market share by adapting to these new ways of working.
  2. The present availability of AI technology across the world is less than the amount that the world needs. So, the companies and individuals must pick up the pace to quickly expand their AI capabilities, and only then they will shine in the market. As for those interested in AI this is the best time to advance in their skills to become market leaders.
  3. The industry transformation has resulted in the marginalization of the CIO role in business and the expense into technology services by business buyers. This gives an edge to the business-oriented teams in play.

Image Source:

Image Source:

But reacting to this new demand for technology also needs AI and will bring newer challenges on board. The first being, change can only happen when the stakeholders of the company believe in the same. But sadly, many employees and managers do not believe in the capabilities of AI until they experience it on their own. It is for those who believe and develop their required skills and embrace the impending digital evolution that is destined to flourish.

However, secondly companies must address the problem of how to deal with the possible cannibalization of the existing revenues in order to adopt these new technologies. And finally, the lack of skill in the world of technology will make it even harder to build and expand AI capabilities.

Nevertheless, due to an industry boom, over the past 20 years a large percentage of the existing staff has skills that are almost obsolete and will not have new ones. Thus, this will bring an interesting future journey for the tech industry.


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Will AI Replace The Intelligentsia? Google’s AI writes mournful poetry

Will AI Replace The Intelligentsia? Google’s AI writes mournful poetry

It is no new news that Artificial Intelligence can now control self driving cars; they can beat the best humans at highly challenging board games like chess, and even fight cancer. But still one thing it cannot do perfectly is communicate.

So, to help solve this problem Google has been feeding its Artificial Intelligence with more than 11,000 unpublished books, which include more than 3000 steamy romantic titles. And in response the AI has penned down its own version of mournful poems.


The poems read something like this:

I went o the store to buy some groceries.

I store to buy some groceries.

I were to buy any groceries.

Horses are to buy any groceries.

Horses are to buy any animal.

Horses the favourite any animal.

Horses the favourite favourite animal.

Horses are my favourite animal.

And here is another one from Google’s AI:

he said.

“no,” he said.

“no,” i said.

“i know,” she said.

“thank you,” she said.

“come with me,” she said.

“talk to me,” she said.

“don’t worry about it,” she said.


The way this happened was, Google’s team fed their AI with unpublished works into a neural network and gave the system two sentences from the book; it was then up to this ingenious artificial intelligence to build its own poetry based on available information.

In example above, the team of researchers gave their AI two sentences one about buying some groceries and the other one about horses being a favourite animal (these are the first and the last lines of the above mentioned passages). The team then directed the artificial intelligence to morph between the two sentences.

In the research paper the team further went on to explain the AI system was able to “create coherent and diverse sentences through purely continuous sampling”.

With the use of an autoanecdoter, which is a type of AI network that makes use of data sets to reproduce a result, in this case that was writing sentences, using much fewer steps the team was able to produce these sentences.

The main principle behind this research is to create an Artificial Intelligence which will be proficient in communicating via “natural language sentences”.

This research holds the possibilities of developing a system that is capable of communicating in a more human-like manner. Such a breakthrough is essential in the creation of more useful and responsive chat bots and Artificial Intelligence powered personal assistants like that of Siri and Google Now.

In a similar project, the researchers at Google have been teaching an AI how to understand language by replicating and predicting the work of bygone authors and poets under their project Gutenberg.

This standalone team at Google fed the AI with an input sentence and then asked it to predict what should come next. And by analysing the text, the AI was capable of identifying what author was likely to have written the sentence and was able to emulate his style.

In another incident, on June, 2015 another team of talented researchers at Google were able to create a chatbot that even threatened its creators. The AI learned the art of conversation by analysis of a million movie scripts thereby allowing it to realize and muse on the meaning of life, the colour of blood, and even on deeper subjects like mortality; Ss, much so that the bot could even get angry on its human inquisitor. When the bot was asked with a puzzling philosophical question about what is the meaning of life, it replied by saying – “to live forever”.

In other such similar works, Facebook has also been teaching its artificial intelligence with the use of children’s books. As per the New Scientist which is a social network, it has been using novels such as The Jungle Book, Alice in Wonderland and Peter Pan.

If you are yearning for some more of AI’s written word, then here are the rest of Google AI’s poems.

You’re right.

“All right.

You’re right.

Okay, fine.

“Okay, fine.

Yes, right here.

No, not right now.

“No, not right now.

“Talk to me right now.

Please talk to me right now.

I’ll talk to you right now.

“I’ll talk to you right now.

“You need to talk to me now. —


Amazing, isn’t it?

So, what is it?

It hurts, isn’t it?

Why would you do that?

“You can do it.

“I can do it.

I can’t do it.

“I can do it.

“Don’t do it.

“I can do it.

I couldn’t do it. —


There is no one else in the world.

There is no one else in sight.

They were the only ones who mattered.

They were the only ones left.

He had to be with me.

She had to be with him.

I had to do this.

I wanted to kill him.

I started to cry.

I turned to him. —


I don’t like it, he said.

I waited for what had happened.

It was almost thirty years ago.

It was over thirty years ago.

That was six years ago.

He had died two years ago.

Ten, thirty years ago. — “it’s all right here.

“Everything is all right here.

“It’s all right here.

It’s all right here.

We are all right here.

Come here in five minutes.

“But you need to talk to me now.


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Can Creative AI Predict The Future?

Can Creative AI Predict The Future?

Artificial Intelligence is reaching new heights, as the researchers at Massachusetts Institute of Technology (MIT) have come up with a program that can estimate the future. The machines can predict the possible events that may occur in a given scenario. The scientists have programmed the machines in such a manner that they can transform a still image into a video. However, the experiment is in its initial stage and researchers wish that it would just get better with time.

Predicting the future

According to the researchers at MIT, this computer can view an image and figure out what may happen next. To be able to do so, the data scientists have fed the computers with humungous amounts of images and videos. All the videos and images were similar in terms of category. For example, videos of sea waves and beaches of previous years were input into the machines. So, the next time, when the computer is shown an image of a sea beach, it automatically generated a video from the still, which replicated how waves are hitting the shore and people are playing in the water. Similar experiments were conducted using images of newborn babies, golf players, and train stations. And in each case, the computer produced videos resembling the expression of these babies, movement of the golf clubs and trains approaching towards the platforms, respectively.

Predicting the future

But how does this machine do it?

As soon as enormous amounts of data are fed into the machine, it starts learning just as humans can. In this experiment by MIT, computers became familiar with the happenings at a sea beach. Therefore, the next time it is shown the picture of a sea beach, the machine analysed the image and eventually, showed what happens there. However, the scientists say that these videos have certain limitations.

According to Carl Vondrick, a Ph.D. student at the MIT, “AI can be trained to produce output just like human beings. They can recall an event and more importantly, AI can predict the possible outcomes of the event based on past records.” Thus, the deep learning programs are able to spot the similarity in several events and make predictions according to the past results, which may not be accurate in many times. From another perspective, these AI generated videos are too short, as their duration does not exceed 1 second. Moreover, the videos seem like some animated movements created during the 90’s.

Despite such limitations, scientists are hopeful about the future of AI because this experiment was just the beginning and the results were better than what was estimated. Vondrick expressed his views on how AI can help us stop any negative incident from happening. He said, “A machine can study the movements of an old man, which may enable it to forecast whether the person has a chance of falling. In that case, adequate measures can be taken in order to prevent the accident.”

Progress of the AI

Progress of the AI

Apart from MIT, there are several companies including the search engine giant Google that are working on AI. At the Google Cultural Institute (GCI) in Paris, computers are programmed to create new images and art forms. The GCI has developed an application that helps users to search artworks from the dataset of several museums across the world. What is fascinating is that algorithms solely administer the entire app. It can search the dataset of almost 7 million images and artworks and provide search results that match the search criteria. The most important feature of the program is that the application can figure out the difference between the emotions embedded in different pictures.  It can differentiate a peaceful picture from the rest by analysing its content. In addition, this program, also known as the ‘Deep Dream Project’ can create artworks on its own, which adds to the creativity of AI. Google is also working on the ‘Magenta Project’, which has recently created a piano melody on its own. The duration of the melody is 90-seconds and it is the first tangible music sample ever produced by AI.

Therefore, we can find that AI is enabling the computers to make judgements based on their intuition and at the same time, they are developing a sense of creativity. Days are not far when human beings will depend on AI to make their next move.

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