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Category: Machine Learning Courses

8 Amazing Things That Artificial Intelligence Can Do

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8 Amazing Things That Artificial Intelligence Can Do

AI plays a crucial role in our everyday lives. By now, we are aware of AI’s glaring significance in our very existence. Nevertheless, you would be surprised to know that AI has already imbibed some of the skills that we, humans, possess. Ahead, we’ve 8 incredible skills that AI has learnt over the years:

Read

Wondering how to summarize all those kilobytes of information? AI-powered SummarizeBot is the answer. Whether its books, news articles, weblinks, audio/image files or legal documents, ATS (automatic text summarization) reads everything and records the important information. Natural Language Processing (NLP), artificial intelligence, machine learning and blockchain technologies are in play here.  

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Write

Did you know that myriad news enterprises and seasoned journalists rely on AI to write? The New York Times, Reuters, Washington Post and more have turned to artificial intelligence to craft interesting reading pieces. Also, AI is expected to enhance the process of creative writing as well.  Even, it has generated a novel that was shortlisted for a prestigious award.

See

Machine vision is in the hype. It is implemented in different ways in today’s world, such as facilitating self-driven cars, facial recognition for payment portals, police work and more. The main concept of machine vision is to let the computers ‘visualize’ the world, analyze key data and make decisions thereafter.

Speak

We are fortunate enough to have Google Maps and Alexa to give us directions and respond to our queries but Google Duplex takes it to a whole new level, courtesy AI. With the help of this robust technology, Duplex can schedule appointments and finish tasks on phone in a very interactive language. It can also respond perfectly to human behaviors.

Hear and Understand

Detecting gunshots and alerting to-the-purpose agencies is one of the greatest things achieved by AI. It means AI can hear and understand sound. It is very well evident in how digital voice assistants respond to your queries regarding weather or a day’s agenda. Working professionals simply love the efficiency, accuracy and convenience of automated meeting minutes provided by AI.

Touch

With the help of cameras and sensors, a robot can identify and handpick ‘supermarket ripe’ blueberries and put them in your basket. The creator of the robot even asserts that it is designed to pick one blueberry every 10 seconds for 24 hours a day!

Deep Learning and AI using Python

Smell

A team of AI researchers are at present developing robust AI models that can detect illnesses – simply by smelling. The model is designed in such a way so that it can notice chemicals, known as aldehydes that cause human stress and diseases, including diabetes, cancer and brain injuries. AI bots can even identify other caustic chemicals or gas leaks. Of late, IBM is using AI to formulate new perfumes.

Perceive Emotions

Today, AI tools can observe human emotions and track them down as one watches videos. Artificial emotional intelligence can collect meaningful data from a person’s facial expressions or body language, analyze it to determine what emotion he/she is likely to express and then ascertain an action base on that detail.

For more such interesting updates, follow DexLab Analytics. Our Machine Learning Using Python course is a bestseller. To know more, click here <www.dexlabanalytics.com>

 

This post originally appeared on – www.forbes.com/sites/bernardmarr/2019/11/11/13-mind-blowing-things-artificial-intelligence-can-already-do-today/#2777e5ec6502

 

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Machine Learning Jobs in 2019: Freezing your own Job

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Machine Learning Jobs in 2019: Freezing your own Job

Machine Learning surely needs no introduction. Joining forces with Data Science and Big Data, Machine Learning is one of the principal technologies, which is carving the future for us. From self-propelled cars to voice assistants, to surgical robots, Artificial Intelligence is already amongst us.

Besides, with this cutting-edge technology, marketing is also witnessing a fresh bloom, irrespective of the field you are working on. Thus, it is obvious that the career opportunities have quickly and radically shifted in the way of the candidates who are well-versed with Machine Learning platforms and languages. If you are also looking forward to shooting your career up, the premium Machine Learning course in India is the place you should reach now!

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Learning Machine Learning is No More a Pain Now!

Whether you are a professional or a fresher planning your way to be successful as a Machine Learning professional, you must ensure that you are updated. Besides, you should also be careful that you have certain skills in your grip that you can work on!

However, if you are not aware of them still, here are the skills that you need to focus on to rest assured:

Programming Languages

As you speak English and/or your regional languages accepted to your society in order to communicate comprehensibly, you also need to be well-versed with the languages specific to Machine Learning.

In a nutshell, R programming certification and Machine Learning Using Python are undoubtedly the most significant ones when it comes to Machine Learning.

Data Modeling

If you believe that you can already boast of considerable knowledge of R & Python, then you shall extend your knowledge a bit more towards the advanced methods of analysis. Brief know how of the coding structures, Data Modeling and Data Visualization will help you steer your career forwards.

Deep Learning and AI using Python

Statistics and Probability

If you are seeking to make a career out of Machine Learning, it is important to note that you should have a good grip of statistics and probability. Now, with the thorough courses of Python for Data Analysis along with extensive knowledge of statistics and probability from Dexlab Analytics, it will be easier than ever.

Besides all these, you also need to grasp significant insights into the improved algorithms and clustering methods. 

 

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The Future of AI and Machine Learning: What the Experts Say?

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The Future of AI and Machine Learning: What the Experts Say?

It’s hard to ignore the growing prowess of AI and machine learning.

Previously, Gartner predicted that AI will become one of the key priorities for more than 30% C-Suite professionals by 2020. Indeed, it’s true; software vendors across the globe are following this new gold rush. For them, data is like new oil. In this blog, we explore the future of this budding technology and gain some new insights and ideas. Let’s see what the heavyweights from the digital industry have to say:

Hyper-targeting and Personalization

Ben Wald, Co-Founder & VP of Solutions Implementation at Very

Though machine learning is a subset of data analysis, it’s rapidly influencing the IoT industry and its respective devices. In the last couple of years, nearly 90% of data was generated through an array of smartphones, watches and cars. These mountains of data help in forming better customer relationships.

How? Using Machine Learning Using Python of course! With this power tool, the corporate houses are trying to understand their target audience and extract crucial information regarding how well they receive their products and related after-sales services. Fine-tuning personalization on a wider scale is the key. Hopefully, soon, we will be able to achieve this goal. We are still in the nascent stage.

Improved Search Engine Experiences

Dorit Zilbershot, Chief Product Officer at Attivio

Did you know that AI algorithms have a massive impact on search engine results?

In the next few years, search engines are expected to enhance user and admin experience: courtesy breakthroughs in neural networks and deep learning technologies. These revolutionary technologies, especially deep learning for computer vision with Python will make sure users enjoy a fabulous searching experience and will deliver highly relevant answers. Currently, we are working on delivering results that are based on user’s query and profile. The process requires a lot of manual configurations and a fundamental understanding of how search engines work. Later, the results will be customized based on individuals’ past preferences, interactions and words used. It will be fun to see how machine learning algorithms transform the dynamo of content publishing and search engines.

Quantum Computing

Matt Reaney, Founder & CEO of Big Cloud

Real and revolutionary, the concept of quantum computing is wreaking havoc in the domain of science and technology. It is the future of machine learning triggering an array of innovations. Integrating quantum computing with machine learning is expected to transform the field triggering accelerated learning, quicker processing and better capabilities. This means the intricate challenges that we can’t solve now could be done in a fraction of time then.

The potential of quantum computing is huge in the future and is likely affect millions of lives, notably in medicine and healthcare industry.

Currently, there are no commercially-built quantum algorithms or hardware available in the market. However, several research facilities and government agencies have been investing in this new field of science of late.

Data Science Machine Learning Certification

End Notes

At DexLab Analytics, we love to craft and curate insights from industry pundits, especially when it comes to something as significant as technological innovations that transform lives altogether. Follow us and stay updated!

 


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A Nifty Guide to Initiate AIOps in 2019

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A Nifty Guide to Initiate AIOps in 2019

AIOps (artificial intelligence for IT operations) is the buzz word of the 21st century.

In this digitally-charged world, AIOps platforms are the key. They fuse ML and big data functionalities to boost and partly replace primary IT operations’ programs, including event correlation and analysis, performance monitoring and IT service automation and management.

In simple terms, AIOps is the combined application of data science and machine learning to help mitigate IT operations-related challenges and find faster insights. It fixes high-severity outages in a jiffy. 

The main objective of revolutionary AIOps platforms is to ingest and analyze the aggravating volume, variety and velocity of data and deliver it in a useful manner.

Deep Learning and AI using Python

IT bigwigs are excited about the prospects of applying AI and ML to IT operations.

Gartner expects that big enterprises’ usage of AIOps and other monitoring tools and applications will rise from 5% in 2018 to 30% in 2023. The long-term impact of AIOps on IT operations is predicted to be transformative.

Fortunately, AI capabilities are making headway, and more real-time solutions are being formulated and made available each day.

Read on to know how to get started with AIOPs:

Be prepared

First and foremost, you have to familiarize yourself with all the ML and AI capabilities and vocabulary. It doesn’t matter if you are gearing up for an AIOps project or not. Capabilities and priorities change; so be ready to implement the platform anytime soon.

Select the first few test cases carefully

Small and steady wins the race. The same phrase applies to transformation initiatives. They start small, seize knowledge and iterate from there. Imbibe the same approach for AIOps success.

Enhance your proficiency

Decode the intricacies of AIOps amongst your colleagues by displaying simple techniques. Ascertain your skills and identify the loopholes, then devise a relevant plan to fill up those gaps in-between.

Feel free to experiment

Although a majority of AIOps platforms are complex and costly, there is a substantial number of open-source and relatively low-cost ML software available in the market that lets you evaluate the efficacy of AIOps and ML applications and their uses.

Look beyond IT

Don’t forget to leverage all kinds of data analytics resources available in your organization. Data management is the cornerstone of AIOps. Most of the teams are already skilled in it. Statistical analytics and business analysis are key components of contemporary business frameworks, and many techniques traverse public domains. 

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Standardize and modernize, as and when required

Prepare your work infrastructure to implement a robust AIOps adoption by embracing secure automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).

Interested in learning more about Machine Learning Using Python? Feel free to reach us at DexLab Analytics. We’re a premier learning platform specialized in offering in-demand skill training courses to the interested candidates.

 

The blog has been sourced from ― www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops

 

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Statistical Application in R & Python: Normal Probability Distribution

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Statistical Application in R & Python: Normal Probability Distribution

Gauss, the famous French Mathematician is responsible for developing one of the most significant distributions in all of statistics, i.e. – The Normal Distribution. Please refer to the blog on Central Limit Theorem: www.dexlabanalytics.com/blog/the-almighty-central-limit-theorem. It will help you fully grasp the significance of the Normal Distribution. However, if you want to revisit our series of blogs by following it from the start, you can reach STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY right now!

Essentially, the Normal Distribution provides “approximations” to most other distributions such as the Binomial, Poisson, Gamma, Exponential, etc. This is to say as sample sizes get statistically large enough, most distributions approximate into a normal shaped curve.

Every distribution has important features known as its “parameters”. Normal distribution has two parameters. These are Mean ( ) and Variance (σ²). The normal distribution has a bell-shaped curve, where the probability of likelihood peaks at its mean in the middle.

The Normal Distribution has vast practical applications in the field of Business, Finance, Medicine, and Physics and so on. Things like weights, heights, IQ scores follow the Normal Distribution.

Normal Distribution, Gaussian distribution, is a continuous probability distribution and is defined by the Probability Density Function (PDF).

Where,

Application:

Assume that the credit score fits a Normal Distribution.

Suppose Mr. Arjun’s last 10 month’s credit score are:

789, 635, 739, 687, 724, 810, 817, 735, 819, 820

What is the probability that the percentage of credit score will 825 or more in the 11th month?

Months

Credit Score

January

789

February

635

March

739

April

687

May

724

June

810

July

817

August

735

September

819

October

820

 

Calculating Normal Distribution in R:

If we go to calculate Normal Probability Distribution in R, we can predict that the probability of the 11th month credit score will be 825 or greater than that is 14.60%, whereas in another case, the probability of the 11th month credit score will be 825 or less than that is 85.40%.

Calculate Normal Distribution in Python:

Make a data frame of the data and calculate Mean and Standard Deviation for calculate Normal Distribution.

Now, we can easily calculate Normal Distribution in Python

So, in calculating the Normal Probability Distribution in Python, we can predict that the probability of the 11th month credit score will be 825 or greater than that is 14.60%, whereas in another case, the probability of the 11th month credit score will be 825 or less than that is 85.40%.

Conclusion:

Normal Distribution is used for calculating parameters. It is represented by the bell curve, where the total area of the curve is 1. Normal Distribution has its use in Finance, Business, Salaries, Blood Pressures, Measurement etc and many other fields.

Here, we have used Normal Distribution to predict Mr. Arjun’s 11th month credit score, and set the target (825). By Normal Distribution we can predict the percentage of possibility to achieve the target.

Calculating Binomial Distribution might be tricky for many but with Dexlab Analytics it won’t be hassle anymore. So, get hold of our STATISTICAL APPLICATION IN R AND PYTHON: CALCULATING BINOMIAL DISTRIBUTION blog, to get around all your problems.

 

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Machine Learning Significantly Aids in Improving the Business Performance: Learn the Hows

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Machine Learning Significantly Aids in Improving the Business Performance: Learn the Hows

According to Forbes, Machine Learning is quickly growing up to be the biggest technology for the progress of businesses of the future. Furthermore, it will be able to add another $2.6 trillion in value, to the sales and marketing industry by 2020. Even in the field of manufacturing and logistics, it is estimated to add up to $2 trillion.

We are already seeing the extensive support that the AI-driven technology is lending to varied businesses which have joined hands with Machine Learning. This collaboration is bringing forth shocking results for the businesses, improving customer relationships, fueling sales and increasing the overall efficiency of the industry.

The total investments in Machine Learning are estimated to scale up reaching the $77 billion mark. So, if you want to enrol yourself for quality Machine Learning courses then, avail of the best Machine Learning course in India.

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To Brief About Machine Learning

Machine Learning is a brand new and extremely progressive discipline at the core of which lies mathematics, statics and artificial intelligence (AI).

The basic difference between Artificial Intelligence and Machine Learning is that the former deals with the engineers writing programs for the AI to carry out specific tasks. Whereas, Machine Learning demands the engineers to write algorithms that can teach computers to write programs for themselves.

Machine Learning stresses primarily on developing the intelligence of a program and its capability of learning from past experiences. Thus, they learn from every previous interaction and each of the experiences from the past and finally, churns out the fitting solution, no matter what the circumstance is.

Therefore, a large number of businesses are incorporating Machine Learning, leading to the growth of their businesses and making their business future proof.

Deep Learning and AI using Python

To list down some of the ways how Machine Learning boosts the business performance are:

  • This new technology aids in developing software to understand the natural human language.
  • Machine Learning further improves the efficiency of logistics and transportation networks.
  • It also aids in building preventive maintenance, thereby lessening the equipment breakdowns and increasing profits.
  • Machine Learning can also be extremely useful in collecting consumer data to analyse customer profiles. This, in turn, will maximise sales and improve brand loyalty.

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Application of Mode using R and Python

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Application of Mode using R and Python

Mode, for a given set of observations, is that value of the variable, where the variable occurs with the maximum or the highest frequency.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will elucidate the Mode and its application using Python and R.

Mode is the most typical or prevalent value, and at times, represents the true characteristics of the distribution as a measure of central tendency.

Application:

The numbers of the telephone calls received in 245 successive one minute intervals at an exchange are shown in the following frequency distribution table:

 

No of Calls
Frequency
0
14
1
21
2
25
3
43
4
51
5
40
6
51
7
51
8
39
9
12
Total
245

 

 [Note: Here we assume total=245 when we calculate Mean from the same data]

Evaluate the Mode from the data.

Evaluate the Mode from the data

Calculate Mode in R:

Calculate mode in R from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Calculate Median in Python:

First, make a data frame for the data.

Now, calculate the mode from the data frame.

Calculate mode in Python from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Mode is used in business, because it is most likely to occur. Meteorological forecasts are, in fact, based on mode calculations.

The modal wage of a group of the workers is the wages which the largest numbers of workers receive, and as such, this wage may be considered as the representative wage of the group.

In this particular data set we use the mode function to know the occurrence of the highest number of phone calls.

It will thus, help the Telephone Exchange to analyze their data flawlessly.

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Note – As you have already gone through this post, now, if you are interested to know about the Harmonic Mean, you can check our post on the APPLICATION OF HARMONIC MEAN USING R AND PYTHON.

Dexlab Analytics is a formidable institute for Deep learning for computer vision with Python. Here, you would also find more information about courses in Python, Deep Learning, Machine Learning, and Neural Networks which will come with proper certification at the end.

We are there in the Social Media where you can follow us both in Facebook and Instagram.

 

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Know the Trending Machine Learning Toolkits: For More Intelligent Mobile Apps

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Know the Trending Machine Learning Toolkits: For More Intelligent Mobile Apps

With the progressive age, innovative and effective technologies like Artificial Intelligence and Machine Learning is dominating the scene of the present. Therefore, developers are rooting for machine learning models to be up to date with the present era. You can also avail of Neural Network Machine learning Python to keep pace with the modern advancements.

To say it, even mobile applications have come a long way from what they were earlier. With the cutting edge technologies of face recognition, speech recognition, recognition of different gestures and movements, mobile apps are really smart now. Furthermore, with the popularity of AI and machine learning, the mobile industry is looking forward to introducing them into the mobiles.

So, here you can catch a glimpse of the top 5 machine learning toolkits for a mobile developer to be aware of.

Apache PredictionIO

Apache PredictionIO is an effective machine learning server. It is open source in nature and acts as a source stack for the developers and data scientists. Through this tool, a developer can easily build and deploy an engine as a web service on production. It can then be easily utilised by the users, where they can run their own machine learning models seamlessly.

Caffe

The Convolutional Architecture for Fast Feature Embedding or Caffe, is an open-source framework developed by the AI Research of Berkeley. Caffe is growing up to be both powerful and popular as a computer vision framework that the developers can use to run machine vision tasks, image classification and more.

CoreML

CoreML is a machine learning framework from the house of Apple Inc. Through this app, you can implement machine learning models on your iOS. CoreML supports the vision to analyse images, natural language for processing natural language, speech for converting audio to text and even sound analysis for the identification of sounds in audio.

Eclipse Deeplearning4j

Eclipse Deeplearning4j is a formidable deep-learning library and is, in fact, the first commercial-grade, open-source one for Java and Scala. You can also integrate Eclipse with Hadoop and Apache Spark if you want to bring AI into the business environment.

Besides, it also acts as a DIY tool where, the programmers of Java, Scala and Clojure can configure the deep neural networks without any hassles. 

Data Science Machine Learning Certification

Google ML Kit

This is a machine learning software development kit for mobile app developers. Through this app, you can develop countless interactive features that you can run on Android and iOS. Here you will also get some readily available APIs for face recognition, to scan barcodes, labelling images and landmarks. With this app, you just need to feed in the data and see the app at its optimum performance.

These are some peerless Machine Learning toolkits to be incorporated into the mobiles. You can also avail of the Machine Learning course in Delhi if you are interested. 

 


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Application of Harmonic Mean using R and Python

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Application of Harmonic Mean using R and Python

Harmonic mean, for a set of observations is the number of observations divided by the sum of the reciprocals of the values and it cannot be defined if some of the values are zero.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will discover Harmonic mean and its application using Python and R.

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Application:

A milk company sold milk at the rates of 10,16.5,5,13.07,15.23,14.56,12.5,12,30,32, 15.5, 16 rupees per liter in twelve different months (January-December), If an equal amount of money is spent on milk by a family in the ten months. Calculate the average price in rupees per month.

Table for the problem:

Month

Rates (Rupees/Liter)

January

10

February

16.5

March

5

April

13.07

May

15.23

June

14.56

July

12.5

August

12

September

30

October

32

November

15.5

December

16

Calculate Harmonic Mean in R:-

So, the average rate of the milk in rupees/liter is 12.95349 = 13 Rs/liter (Approx)

We get this answer from the Harmonic Mean, calculated in R.

Calculate Harmonic Mean in Python:-

First, make a data frame of the available data in Python.

Now, calculate the Harmonic mean from the following data frame.

So, the average rate of the milk in rupees/liter is 12.953491609077956 = 13 Rs/Liter (Approx)

We get this answer from Harmonic mean, calculated in Python.

Summing it Up:

In this data, we have a few large values which are putting an effect on the average value, if we calculate the average in Arithmetic mean, but in Harmonic mean, we get a perfect average from the data, and also for calculating the average rate.

Use of Harmonic mean is very limited. Harmonic mean gives the largest value to the smallest item and smallest value to the largest item.

Where there are a few extremely large or small values, Harmonic mean is preferable to Arithmetic mean as an average.

The Harmonic mean is mainly useful in averages involving time, rate & price.

Deep Learning and AI using Python

Note – If you want to learn the calculation of Geometric Mean, you can check our post on CALCULATING GEOMETRIC MEAN USING R AND PYTHON.

Dexlab Analytics is a peerless institute for Python Certification Training in Delhi. Therefore, for tailor-made courses in Python, Deep Learning, Machine Learning, Neural Networks, reach us ASAP!

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