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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.

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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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AI-Related Tech Jargons You Need To Learn Right Now

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AI-Related Tech Jargons You Need To Learn Right Now

As artificial intelligence gains momentum and becomes more intricate in nature, technological jargons may turn unfamiliar to you. Evolving technologies give birth to a smorgasbord of new terminologies. In this article, we have tried to compile a few of such important terms that are related to AI. Learn, assimilate and flaunt them in your next meeting.

Artificial Neuron Networks – Not just an algorithm, Artificial Neuron Networks is a framework containing different machine learning algorithms that work together and analyzes complex data inputs.

Backpropagation – It refers to a process in artificial neural networks used to discipline deep neural networks. It is widely used to calculate a gradient that is required in calculating weights found across the network.

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Bayesian Programming – Revolving around the Bayes’ Theorem, Bayesian Programming declares the probability of something happening in the future based on past conditions relating to the event.

Analogical Reasoning – Generally, the term analogical indicates non-digital data but when in terms of AI, Analogical Reasoning is the method of drawing conclusions studying the past outcomes. It’s quite similar to stock markets.

Data Mining – It refers to the process of identifying patterns from fairly large data sets with the help statistics, machine learning and database systems in combination.

Decision Tree Learning – Using a decision tree, you can move seamlessly from observing an item to drawing conclusions about the item’s target value. The decision tree is represented as a predictive model, the observation as the branches and the conclusion as the leaves.  

Behavior Informatics (BI) – It is of extreme importance as it helps obtain behavior intelligence and insights.

Case-based Reasoning (CBR) – Generally speaking, it defines the process of solving newer challenges based on solutions that worked for similar past issues.

Feature Extraction – In machine learning, image processing and pattern recognition plays a dominant role. Feature Extraction begins from a preliminary set of measured data and ends up building derived values that intend to be non-redundant and informative – leading to improved subsequent learning and even better human interpretations.

Forward Chaining – Also known as forward reasoning, Forward Chaining is one of two main methods of reasoning while leveraging an inference engine. It is a widely popular implementation strategy best suited for business and production rule systems. Backward Chaining is the exact opposite of Forwarding Chaining.

Genetic Algorithm (GA) – Inspired by the method of natural selection, Genetic Algorithm (GA) is mainly used to devise advanced solutions to optimization and search challenges. It works by depending on bio-inspired operators like crossover, mutation and selection.

Pattern Recognition – Largely dependent on machine learning and artificial intelligence, Pattern Recognition also involves applications, such as Knowledge Discovery in Databases (KDD) and Data Mining.

Reinforcement Learning (RL) – Next to Supervised Learning and Unsupervised Learning, Reinforcement Learning is another machine learning paradigms. It’s reckoned as a subset of ML that deals with how software experts should take actions in circumstances so as to maximize notions of cumulative reward.

Looking for artificial intelligence certification in Delhi NCR? DexLab Analytics is a premier big data training institute that offers in-demand skill training courses to interested candidates. For more information, drop by our official website.

The article first appeared on— www.analyticsindiamag.com/25-ai-terminologies-jargons-you-must-assimilate-to-sound-like-a-pro

 

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Big Data Analytics for Event Processing

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Courtesy cloud and Internet of Things, big data is gaining prominence and recognition worldwide. Large chunks of data are being stored in robust platforms such as Hadoop. As a result, much-hyped data frameworks are clouted with ML-powered technologies to discover interesting patterns from the given datasets.

Defining Event Processing

In simple terms, event processing is a typical practice of tracking and analyzing a steady stream of data about events to derive relevant insights about the events taking place real time in the real world. However, the process is not as easy as it sounds; transforming the insights and patterns quickly into meaningful actions while hatching operational market data in real time is no mean feat. The whole process is known as ‘fast data approach’ and it works by embedding patterns, which are panned out from previous data analysis into the future transactions that take place real time.

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Employing Analytics and ML Models

In some instances, it is crucial to analyze data that is still in motion. For that, the predictions must be proactive and must be determined in real-time. Random forests, logistic regression, k-means clustering and linear regression are some of the most common machine learning techniques used for prediction needs. Below, we’ve enlisted the analytical purposes for which the organizations are levering the power of predictive analytics:

Developing the Model – The companies ask the data scientists to construct a comprehensive predictive model and in the process can use different types of ML algorithms along with different approaches to fulfill the purpose.

Validating the Model – It is important to validate a model to check if it is working in the desired manner. At times, coordinating with new data inputs can give a tough time to the data scientists. After validation, the model has to further meet the improvement standards to deploy real-time event processing.

Top 4 Frameworks for ML in Event Processing

Apache Spark

Ideal for batch and streaming data, Apache Spark is an open-source parallel processing framework. It is simple, easy to use and is ideal for machine learning as it supports cluster-computing framework.

Hadoop

If you are looking for an open-source batch processing framework then Hadoop is the best you can get. It not only supports distributed processing of large scale data sets across different clusters of computers with a single programming model but also boasts of an incredibly versatile library.

Apache Storm

Apache Storm is a cutting edge open source, big data processing framework that supports real-time as well as distributed stream processing. It makes it fairly easy to steadily process unbounded streams of data working on real-time.

IBM Infosphere Streams

IBM Infosphere Streams is a highly-functional platform that facilitates the development and execution of applications that channels information in data streams. It also boosts the process of data analysis and improves the overall speed of business decision-making and insight drawing.

If you are interested in reading more such blogs, you must follow us at DexLab Analytics. We are the most reputed big data training center in Delhi NCR. In case, if you have any query regarding big data or Machine Learning using Python, feel free to reach us anytime.

 

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AI Jobs: What the Future Holds?

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AI Jobs: What the Future Holds?

Technological revolutions have always been challenging, especially how they influence and impact working landscapes. They either bring on an unforeseen crisis or prove a boon; however, fortunately, the latter has always been the case, starting from the innovation of steam engines to Turing machine to computers and now machine learning and artificial intelligence.

The crux of the matter lies in persistence, perseverance and patience, needed to make these high-end technologies work in the desired way and transform the resources into meaningful insights tapping the unrealized opportunities. Talking of which, we are here to discuss the growth and expansion of AI-related job scopes in the workplace, which is expected to generate around 58 million new jobs in the next couple of years. Are you ready?

Data Analysts

Internet of Things, Machine Learning, Data Analytics and Image Analysis are the IT technologies of 2019. An exponential increase in the use of these technologies is to be expected. Humongous volumes of data are going to be leveraged in the next few years, but for that, superior handling and management skill is a pre-requisite. Only expert consultants adept at hoarding, interpreting and examining data in a meaningful manner can strategically fulfill business goals and enhance productivity.

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IT Trainers

With automation and machine learning becoming mainstream, there is going to be a significant rise in the number of IT Trainer jobs. Businesses have to appoint these professionals for the purpose of two-way training, including human intelligence as well as machines. On one side, they will have to train AI devices to grasp a better understanding of human minds, while, on the other hand, the objective will be training employees so as to utilize the power of AI effectively subject to their job responsibilities and subject profiles. Likewise, there is going to be a gleaming need for machine learning developers and AI researchers who are equipped to instill human-like intelligence and intuition into the machines – making them more efficient, more powerful.

Man-Machine Coordinators

Agreed or not, the interaction between automated bots and human brainpower will lead to immense chaos – if not managed properly. Organizations have great hope in this man-machine partnership, and to ensure they work in sync with each other, business will seek experts, who can devise incredible roadmaps to tap newbie opportunities. The objective of this job profile is to design and manage an interaction system through which machines and humans can mutually collaborate and communicate their abilities and intentions.

Data Science Machine Learning Certification

Security Analysts

Security is crucial. The moment the world switched from offline to online, a whole lot of new set of crimes and frauds came into notice. To protect and safeguard confidential information and high-profile business identities, companies are appointing skilled professionals who are well-trained in tracking, protecting and recovering AI systems and devices from malicious cyber intrusions and attacks. Thus, skill and expertise in information security, networking and guaranteeing privacy is well-appreciated.

No wonder, a good number of jobs are going to dissolve with AI, but also, an ocean of new job opportunities will flow in with time. You just have to hone your skills and for that, we have artificial intelligence certification in Delhi NCR. In situations like this, these kinds of in-demand skill-training courses are your best bet.

 

The blog has been sourced from ―  www.financialexpress.com/industry/technology/artificial-intelligence-are-you-ready-for-ocean-of-new-jobs-as-many-old-ones-will-vanish/1483437

 


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Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

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Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

Programming skills are indispensable for data science professionals. The main job of machine learning engineers and data scientists is drawing insights from data, and their expertise in programming languages enable them to do this crucial task properly. Research has shown that professionals of the data science field typically work with three languages simultaneously. So, which ones are the most popular? Are some languages more likely to be used together?

Recent studies explain that certain programming languages are used jointly besides other programming languages that are used independently. With the survey data collected from Kaggle’s 2018 Machine Learning and Data Science study, usage patterns of over 18,000 data science experts working with 16 programming languages were analyzed. The research revealed that these languages can actually be categorized into smaller sets, resulting in 5 main groupings. The nature of the groupings is indicative of specific roles or applications that individual groups support, like analytics, front-end work and general-purpose tasks.

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Principal Component Analysis for Dimension Reduction

In this article, we will explain how Bob E. Hayes, PhD holder, scientist, blogger and data science writer has used principal component analysis, a type of data reduction method, to categorize 16 different programming languages. Herein, the relationship among various languages is inspected before putting them in particular groups. Basically, principal component analysis looks into statistical associations like covariance within a large collection of variables, and then justifies these correlations with the help of a few variables, called components.

Principal component matrix presents the results of this analysis. The matrix is an nXm table, where:

n= total no. of original variables, which in this case are the number of programming languages

m= number of main components

The strength of relationship between each language and underlying components is represented by the elements of the matrix. Overall, the principal component analysis of programming language usage gives us two important insights:

  • How many underlying components (groupings of programming languages) describe the preliminary set of languages
  • The languages that go best with each programming language grouping

Result of Principal Component Analysis:

The nature of this analysis is exploratory, meaning no pre-defined structure was imposed on the data. The result was primarily driven by the type of relationship shared by the 16 languages. The aim was to explain the relationships with as less components as possible. In addition, few rules of thumb were used to establish the number of components. One was to find the number of eigen values with value greater than 1 – that number determines the number of components. Another method is to identify the breaking point in the scree plot, which is a plot of the 16 eigen values.

businessoverbroadway.com

 

5-factor solution was chosen to describe the relationships. This is owing to two reasons – firstly, 5 eigen values were greater than one and secondly, the scree plot showed a breaking point around 6th eigen value.

Following are two key interpretations from the principal component matrix:

  • Values greater than equal to .45 have been made bold
  • The headings of different components are named on the basis of tools that loaded highly on that component. For example, component 4 has been labeled as Python, Bash, Scala because these languages loaded highest on this component, implying respondents are likely to use Bash and Scala if they work with Python. Other 4 components were labeled in a similar manner.

Groupings of Programming Languages

The given data set is appropriately described by 5 tool grouping. Below are given 5 groupings, including the particular languages that fall within the group, meaning they are likely to be used together.

  1. Java, Javascript/Typescript, C#/.NET, PHP
  2. R, SQL, Visual Basic/VBA, SAS/STATA
  3. C/C++, MATLAB
  4. Python, Bash, Scala
  5. Julia, Go, Ruby

One programming language didn’t properly load into any of the components: SQL. However, SQL is used moderately with three programming languages, namely Java (component 1), R (component 2) and Python (component 4).

It is further understood that the groupings are determined by the functionality of different languages in the group. General-purpose programming languages, Python, Scala and Bash, got grouped under a single component, whereas languages used for analytical studies, like R and the other languages under comp. 2, got grouped together. Web applications and front-end work are supported by Java and other tools under component 1.

Conclusion:

Data science enthusiasts can succeed better in their projects and boost their chances of landing specific jobs by choosing correct languages that are suited for the job role they want. Being skilled in a single programming language doesn’t cut it in today’s competitive industry. Seasoned data professionals use a set of languages for their projects. Hence, the result of the principal component analysis implies that it’s wise for data pros to skill up in a few related programming languages rather than a single language, and focus on a specific part of data science.

For more help with your data science learning, get in touch with DexLab Analytics, a leading data analyst training institute in Delhi. Also check our Machine learning courses in Delhi to be trained in the essential and latest skills in the field.

 
Reference: http://customerthink.com/usage-driven-groupings-of-data-science-and-machine-learning-programming-languages
 

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More than Statistics, Machine Learning Needs Semantics: Explained

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More than Statistics, Machine Learning Needs Semantics: Explained

Of late, machines have achieved somewhat human-like intelligence and accuracy. The deep learning revolution has ushered us into a new era of machine learning tools and systems that perfectly identifies the patterns and predicts future outcomes better than human domain experts. Yet, there exists a critical distinction between man and machines. The difference lies in the way we reason – we, humans like to reason through advanced semantic abstractions, while machines blindly depend on statistics.

The learning process of human beings is intense and in-depth. We prefer to connect the patterns we identify to high order semantic abstractions and our adequate knowledge base helps us evaluate the reason behind such patterns and determine the ones that are most likely to represent our actionable insights.

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On the other hand, machines blindly look for powerful signals in a pool of data. Lacking any background knowledge or real-life experiences, deep learning algorithms fail to distinguish between relevant and specious indicators. In fact, they purely encode the challenges according to statistics, instead of applying semantics.

This is why diverse data training is high on significance. It makes sure the machines witness an array of counterexamples so that the specious patterns get automatically cancelled out. Also, segmenting images into objects and practicing recognition at the object level is the order of the day. But of course, current deep learning systems are too easy to fool and exceedingly brittle, despite being powerful and highly efficient. They are always on a lookout for correlations in data instead of finding meaning.

Are you interested in deep learning? Delhi is home to a good number of decent deep learning training institutes. Just find a suitable and start learning!

How to Fix?

The best way is to design powerful machine learning systems that can tersely describe the patterns they examine so that a human domain expert can later review them and cast their approval for each pattern. This kind of approach would enhance the efficiency of pattern recognition of the machines. The substantial knowledge of humans coupled with the power of machines is a game changer.

Conversely, one of the key reasons that made machine learning so fetching as compared to human intelligence is its quaint ability to identify a range of weird patterns that would look spurious to human beings but which are actually genuine signals worth considering. This holds true especially in theory-driven domains, such as population-scale human behavior where observational data is very less or mostly unavailable. In situations like this, having humans analyze the patterns put together by machines would be of no use.

End Notes

As closing thoughts, we would like to share that machine learning initiated a renaissance in which deep learning technologies have tapped into unconventional tasks like computer vision and leveraged superhuman precision in an increasing number of fields. And surely we are happy about this.

However, on a wider scale, we have to accept the brittleness of the technology in question. The main problem of today’s machine learning algorithms is that they merely learn the statistical patterns within data without putting brains into them. Once, deep learning solutions start stressing on semantics rather than statistics and incorporate external background knowledge to boost decision making – we can finally chop off the failures of the present generation AI.

Artificial Intelligence is the new kid on the block. Get enrolled in an artificial intelligence course in Delhi and kickstart a career of dreams! For help, reach us at DexLab Analytics.

 

The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2019/01/15/why-machine-learning-needs-semantics-not-just-statistics/#789ffe277b5c

 

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