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Month: June 2019

AI is Now Being Used in Beer Brewing!

Posted on by Imarticus Learning

AI is now being used in beer brewing -from creating unique beer recipes to adapting recipes as per customer feedback. AI is doing it all…

With the advent of the digital revolution, Artificial Intelligence (AI) has gained immense impetus in recent years. Today, everyone is connected to everything because of the growing importance of the Internet of Things. Right from the time, you wake up until the time you close your day, technology plays a key role in taking you forward.

Alexa and Siri have now become household names and no doubt, why “Her” was a blockbuster in the cinemas. AI and Machine Learning are here to make your work easier, and your life smoother. It is also brilliant to know how even breweries today are using AI to enhance their beer production.

Brewed with AI
As discussed earlier, digitization and technology have significantly impacted our lives across spectrums, and there are several examples of various companies that have started employing AI in their processes to serve their customers better. Breweries are nowhere behind in this race of digitization, so let us discuss a few examples of how they are using AI in order to enhance the experience of the consumers.

Intelligent X
Intelligent X is one of the best examples of how a platform employed AI to enhance their beer. It came up with the world’s first beer, which is brewed with Artificial Intelligence Course and advances itself progressively based on customer feedback. They use AI algorithms and machine learning to augment the recipe and adjust it in accordance with the preferences of the customers. The brewery offers four types of beer for the customers to choose from:

  • Black AI
  • Golden AI
  • Pale AI
  • Amber AI

In order to brew the perfect beer that pleases all your senses, all you need to do is sign up with IntelligentX, train their algorithm according to what appeals to your palate, and you are good to go. In addition to this, you can follow the URL link on your beer can and give your feedback so that they can create a beer you would like. These beers come in classy and minimally designed black cans that reflect their origin and give a feeling that what you are experiencing is the beer from the future.

Champion Brewing
Another example of a very intelligent deployment of AI in brewing beer is that of Champion Brewing. They used machine learning in the process of developing the perfect IPA. They took the big step by initially getting information regarding the best and the worst IPA selling companies to get an insight into how to go about the entire project. Based on the same, did they determine the algorithm of brewing the best IPA with their AI?

RoboBEER
An Australian research team found out that the form of a freshly poured beer affects how people enjoy it. Building on to this, they created RoboBEER, which is a robot that can pour a beer with such precision that can produce consistent foam, pour after pour. These researchers also made a video of how the RoboBEER poured the beer tracked the beer color, consistency, bubble size, and all the other attributes. They then showed the same videos to everyone who participated in the research in order to get seek their feedback and thoughts with regard to the beer’s quality along with its clarity.
Conclusively, this shows how AI has become the nascent yet a very preferred trend, which is even being followed by the breweries around the world. It has added an unusual turn to the way the perfectly brewed well-crafted beer makes its way to your glass. With the help of this ever-evolving technology, we can anticipate our favorite drinks to be made precisely in accordance with our preference only with the help of your smartphone.

By deriving minutest of the insights right from the foam of the beer till the yeast used in the same, companies these days are striving to deliver their best with the help of immense research and execution from the ideation derived from their research amalgamating it with AI and Machine Learning. Looking at the various examples, we can surely say that we are living in the future in the present.

For more information you can also visit – Imarticus Learning contact us through the Live Chat Support or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi and Gurgaon.

Retrospectives Make Better Product Outcomes

Posted on by Imarticus Learning

Most product managers and coaches struggle with frustrating outcomes. The answer to such a situation lies in the retrospectives. The Retrospectives-tool is an essential of the PMs toolkit and relies on learning, transparency and exploring curiosity in a safe environment. The product retrospectives can transform product processes, improve products, and offers an opportunity for continued learning through the iterative product development life-cycle.

Why Retrospectives?

The retrospective generates actions based on consensus and produces new information unlike the regular meets reviewing past performance, data and actions.
It is a based-in-reality change and action process which can be undertaken to occur at regular intervals. For example the quarterly review of road-maps, the finish of a sprint iteration, after a release, sales /client meeting, product launch or the hypothesis-testing. The retrospective reviews what, how and why in terms of reviewing over a fixed timeframe the desired outcome or event and comprises the entire product development community of stakeholders, customers, product and development teams.
The accruing benefits:
The retrospective is beneficial when it is able to: 

  • Use and gather community collective-wisdom.
  • Be neutral and non-judgmental about the truth.
  • Find areas for improvement and appreciation.
  • Generate beneficial product insights.
  • Try, change and make commitments to improvement actions.

The main benefits accrue when retrospectives are used for: 
·         Active Engagement.
·         Go beyond the process.
·         Use product data to make better product decisions.
Let us explore how retros help under each head.
Active engagement:
Retrospectives are important Scrum events for the teams in product development. However, the PD Managers avoid shoddily run meets on product quality instead of addressing the issues and making situations better. They are also useful in mutual learning and resolving key issues like strained team member relationships.
Transparency, a safe environment, and open communications are key in the retros. Product leadership starts with discussing in a neutral non-judgemental environment even undiscussables. The skilled facilitator can then help transition the team to the high-performance zone.

Go beyond the process:

Most times the Retrospectives are useful in development processes and go beyond product releases and sprint iterations. Retrospectives are event-based learning from events like the product launch, hypothesis test, product/customer research, roadmap-outcomes, and customer conferences. Your leverage depends on the events linked to your product, engaging the right people and post-event retros to learn from.

Use product data to make better product decisions:

Typically any retrospective involves the data gathering, culling of insights, and product-data focus for making good business decisions.

The Retrospective structure:

A structure has a series of activities like: 

  1. Readying the stage: Here one collects data required, starts the session with stakeholders, defines parameters for retro success, and creates retro safety.
  2. Using past data: Data here is used to recreate and tell the story using shared resources of quantitative and qualitative data.
  3. Draw present insights: This phase reflect on feelings and facts, interprets data accordingly, looks and understands the whole scenario while answering the top five retro-questions.
  4. Make future decisions: Here one decides the actions for implementation and decides what and when to change.
  5. Retro Closure: The whole process is reviewed for future use and improvements.

In retros, data is both quantitative (like coding, tech debts, quality, defects, etc) and qualitative (like happiness, reviews, reactions, etc). It also includes metrics of the HEART (like customer happiness, engagement, outcomes, adoption, task success, retention, etc). Factors like revenue, loss/win results, costing results over a time-period, metrics of marketing campaigns, test findings, hypothesis testing, and conversion rates are also part of it.

Retrospectives help to learn:

Retrospectives can enable learning when such learning is reinforced and is essential for self-direction, immediacy, and relevance. By Immediacy, one means you apply your learning immediately, by relevance one means it applies aptly to our situations,  and self-direction implies taking control of retrospection and make learning-based changes. Retrospectives hence should be mindful of everyone’s involvement in things that need change and the achievement of change itself.

Conclusion:

Retrospectives go beyond the obvious thinking. To practically use and reap better retrospective-based outcomes, the product leaders have to determine when to use, who can best facilitate, learn all about the timing, duration, etc, and possess safety from a psychological perspective.
In conclusion, ask yourself if using retrospectives and better productivity interests you. Do an Agile course at Imarticus Learning to further your career today.

Current Trends Likely to Shape the Future of Business Analytics

Posted on by Imarticus Learning

 

Business Analytics is the process of organizing a company’s data into a simpler and more understandable form, in order to allow the administration to take a better decision with respect to the growth of the company.

Many individuals, after taking the Business Analytics course in Thane, are employed in multinational companies. They are tasked with providing relevant information, backed with suitable data, to the company’s administration. This makes for better decision making, which in turn, increases the rate of revenue made by the company. Business analytics could also be seen as an iterative investigation of a company’s past records. Recommendations are made in accordance with the study to ensure the betterment of the company.

There are a number of trends today which are shaping the future of Business Analytics. Keeping up with these trends and analyzing them is also a major part of the future. This makes it the right time to take up the Business Analytics Course. In this article, we will specifically talk about these trends.

Machine Intelligence

Machine Intelligence refers to creating smart machines which could act and perform specific activities just like humans. These machines are designed to work similar to human activities and natural phenomena. The machines learn from specific data sets customized to their activity. These are organized by individuals with skills in business intelligence.

Artificial Intelligence is only as good as the data provided to it.

With technological advancement, we have come to the invention of a medium both to enhance human intelligence, and replace it with something smarter and more efficient. Artificial Intelligence (also called augmented intelligence) takes its decisions on its own in accordance with its programming and learning from the data provided to it. An essential aspect of AI is that it is only as smart as the data. Business analytics serves that purpose by providing the AI program with a sorted and efficient set of data.

Internet of Things reached $170.57 billion in 2017.

The Internet of Things (IOT) is a market of devices which provide users with data on the basis of machine-to-machine communication. These devices or machines (like the Fitbit and smart watches) collect data from the user, interpret and analyze them by their parameters, and return their results accordingly. The IOT market is expected to rise to $561.04 billion by 2022. This will be a result of business analytics, through which a more organized data set is expected to be provided. Business analytics provides these wireless devices with properly understandable data which helps them provide simplified and relevant data in return.

Takeaway:

With the advent of technology, consumer expectation has shot up accordingly. Devices for instant entertainment, wireless communication, and other smart devices have experienced a rise in market demand. Moreover, the demand for sensors in these devices has also been increasing. Business analytics is that field of data simplification which is needed to convert this expectation into reality. If you are thinking of a career in Business Analytics, this is the best time to take Business Analytics course in Thane.

What Is The Best Tool For Financial Analysis

Posted on by Imarticus Learning

It is the job of the financial analyst to use data from the company’s financial statements and records to understand and analyze the strengths, weaknesses and financial position of the company. For example, debts being serviced, the revenue stream flow, capital investments and current position to invest, operational efficiency, future profits and more.

The Following Are Essential to Financial Analysts.

1. Financial statements:
The company income statement and balance sheets reflect the losses or profits over a time period. The assets, liabilities, capital position and such data are crucial to plan and ensure success.

2. Working Capital Statement:
Changes in the working capital can be tracked from the current liabilities and assets in comparison to the previous year. This is a crucial decision making factor in planning and evaluation.

3. Comparative statements:
Size statements of multiple companies at any point helps with understanding the current position vis-à-vis the industry.

4. Analysis of ratios:
This is a great way to arrive at the asset management, debt management, liquidity, market value, profitability, and financial performance of the various departments and business parts.

Why tools help:

Data is the backbone, and a lot depends on the type of decision-making involved, the inferences drawn from data in the financial records and how you analyze the information and use it for constructive feedback and financial analysis. The software used is a crucial tool and should provide customizable, clear and concise analysis.

Popular Software for Financial Analysts:

Of the many software, the Stock Screener by Finviz and Customized Financial Analysis by BizBench are popular with investors. Cloud tools for management, reporting software, etc. are widely used by accountants and bookkeepers, and Managers/ analysts favor SWOT analysis providing the software.

Software like Balanced Scorecard can help assess the current position, overall return, capital financing, operating income, analysis of specific firm processes and performance history.

EPM Financial Reporting by Prevision is also popular and rated high as a tool for management of enterprises, report books maintenance and for being able to analyze and combine real-time data from IT with information on accounting.

SAS, QlikView, and MATLAB are preferred for financial analysis that is customizable and includes tools for statistical analysis in its sophisticated software. It is an excellent tool for traders, analysts, and programmers.
Microsoft Excel and its VBA macro tool is the foundational tool for beginners and those who use small and less complex functions.

In conclusion, if you have a flair for financial analysis, then the financial analysis course in Hyderabad can get you the coveted financial analyst certification. The financial analyst course teaches you the best tools for assessing the company’s weaknesses, strengths, and financial strength. Doing such courses at Imarticus are particularly advantageous to you because of the global robust curriculum, hands-on practice on popular tools, an industry-relevant project involving real-time live data, and excellent mentorship provided which makes you industry-ready from day one.

How Big Is The Agile Methodologies Provider Industry

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Let us first understand what Agile methodology is all about. Under Agile methodologies, the software is developed in an escalating, incremental manner through rapid cycles. The process focuses on adaptability and customer satisfaction of the software product. Each cycle of software building is better and incremental than the previous one, and thus the cycle continues.
There are various reasons why Agile methodology has gained worldwide fame, appreciation, and popularity.
(a)One of them being that Agile is used to prevent large projects from failing, it had a prime focus on business profitability and delivering software as opposed to documentation.
(b)It is user-friendly and treats the user/consumer in a friendly way rather than a machine-centric manner.
There are certain strong strategies that Agile has adapted to have become this popular and widely acclaimed. They are as follows:
1. It shares the common value and goal of fixing large scale projects
2. It is user/customer/human centric rather than being machine-centric. This has positively impacted the productivity of Agile users and shown proven efficiency in business successes.
3. Inclusive/collaborative approach – The agile methodology has a very inclusive feature that supports the entire Agile team and promotes collaborative efforts of the team through its built-in flexibility.
4. Some Agile methodologies are very popular and user-friendly. These include Extreme Programming (XP), SCRUM, Features Driven Development (FDD) and Dynamic Systems Development Method (DSDM)
5. Agile methodology helps you perform quicker and can also be developed at a good pace.
There many Agile methodology courses and certifications that you can sign up for. You can start with basics, and if you have previous exposure to the software development and its aspects such as coding, testing, and other software skills, it helps you gain Agile knowledge much more efficiently. From mastering an adaptive approach to product development, the courses are streamlined for your needs/requirements.

What You Will Gain From An Agile Certification Course?

1. You will gain the knowledge base, skills, tools and techniques involved in Agile methodologies and will be able to understand, apply and implement the principles of Agile.
2. You will also learn to smoothly coordinate Agile development processes including managing your respective teams, bringing about a social culture of experimentation, and running sprints.
3. In the final project completion stage, you should be able to apply, everything that you have learnt to a real-world hands-on practical project and demonstrate your skills and abilities.
4. Flexible schedules to learn the course in case you are already a working professional. Many education platforms offer online certification courses and you can time these according to your convenience.
The job opportunities you will encounter are many to pick from. You can become an Agile Scrum Master, Technical project manager, Technology manager, Scrum Master consultant, Project leader, Senior Agile transformer, and Agile coach/tutor/mentor/guide and give lessons and coach many more hundreds of people, Product Owner handling the business operations too, and/or software development manager, among other opportunities. The Agile Scrum certification promises you a great career with a heavy income. A career with a business analyst certification India is also possible using these skills and knowledge base.

Can You Be Agile Without Doing Scrum If So How

Posted on by Imarticus Learning

This is not a frequent question that many encounters in their Agile career, but, have you ever wondered, ‘Is Agile possible without Scrum?’
The answer to that is, yes, absolutely.
There are many instances of real-world projects and corporate projects that sometimes do not make use of any of your Scrum sensibilities. Knowing and being an Agile practitioner is more than sufficient. Let us understand further. Scrum as a framework is used to enable teams, enterprises, and organizations on their Agility pathway. It is not the only proven way to be Agile. But Agile works well without Scrum under certain circumstances only. Let us understand what these are –
1. When your project is small in size – Agile without Scrum works best only if the project size is small and the team members are also small in number. This is because the time used for Agile scaling will be shorter and will have not many disadvantages affecting the project directly.
2. Clear, simple and direct requirements – Under Agile without Scrum is applicable best to requirements from the project that are really simple, crystal clear and direct.
3. Periodical planning and requirements – The projects that you undertake must have and experience periodical planning and must be upgraded with periodical requirements, so as to not pressure the project processes. This can be used to a large extent with Agile and there is no requirement for Scrum, whatsoever.
4. Regular improvements – open communication with your team members which is quite easy to do since it is already a small team, can help you figure out already existing solutions and gain feedback regarding your work, weekly developments and progress, crosscheck these with the developer, and before you know it, you would have increased your efficiency through such regular improvements.
5. Small teams mean no hierarchies/better transparency – Small teams where everybody is an equal leads to transparent knowledge sharing, coping strategies and mutual help/support. Also since smaller teams lack a head, or rather is not hierarchy conducive, it becomes easier to be a self-organizing team which is driven by equal participation from the team members.
6. Quality output – All team members are equally held responsible for their contribution and quality output, which is responsible to keep the team going. This helps deliver success and efficient output in small scale but the quality remains very high. Each and every member ensures that their work meets a standard parameter to test quality.
7. Regular/periodical releases – It is most important to have working software in place instead of comprehensive documentation.
Sometimes you will have no release team and may have to work around this aspect of the project by yourselves. You will need to put your work into production periodically, ideally, weekly, and get the approval of the Product Manager to ensure you can move on smoothly to the next tasks at hand. You may also need to segregate tasks and assign them according to each team member’s unique capacities, to see quality output from your team’s final project work. There is an excellent way to learn Agile and go under Agile training. You can try Agile training in Mumbai. You can eventually upgrade to Agile development certification.

How have statistical machines influenced Machine Learning?

Posted on by Imarticus Learning

The past few years have witnessed tremendous growth of machine learning across various industries. From being a technology of the future, machine learning is now providing resources for billion-dollar businesses. One of the latest trend observed in this field is the application of statistical mechanics to process complex information. The areas where statistical mechanics is applied ranges from natural models of learning to cryptosystems and error correcting codes. This article discusses how has statistical mechanics influenced machine learning.
What is Statistical Mechanics?
Statistical mechanics is a prominent subject of the modern day’s physics. The fundamental study of any physical system with large numbers of degrees of freedom requires statistical mechanics. This approach makes use of probability theory, statistical methods and microscopic laws.
The statistical mechanics enables a better study of how macroscopic concepts such as temperature and pressure are related to the descriptions of the microscopic state which shifts around an average state. This helps us to connect the thermodynamic quantities such as heat capacity to the microscopic behavior. In classical thermodynamics, the only feasible option to do this is measure and tabulate all such quantities for each material.
Also, it can be used to study the systems that are in a non-equilibrium state. Statistical mechanics is often used for microscopically modeling the speed of irreversible processes. Chemical reactions or flows of particles and heat are examples of such processes.
So, How is it Influencing Machine Learning?
Anyone who has been following machine learning training would have heard about the backpropagation method used to train the neural networks. The main advantage of this method is the reduced loss functions and thereby improved accuracy. There is a relationship between the loss functions and many-dimensional space of the model’s coefficients. So, it is very beneficent to make the analogy to another many-dimensional minimization problem, potential energy minimization of the many-body physical system.
A statistical mechanical technique, called simulated annealing is used to find the energy minimum of a theoretical model for a condensed matter system. It involves simulating the motion of particles according to the physical laws with the temperature reducing from a higher to lower temperature gradually. With proper scheduling of the temperature reduction, we can settle the system into the lowest energy basin. In complex systems, it is often found that achieving global minimum every time is not possible. However, a more accurate value than that of the standard gradient descent method can be found.
Because of the similarities between the neural network loss functions and many-particle potential energy functions, simulated annealing has also been found to be applicable for training the artificial neural networks. Other many techniques used for minimizing artificial neural networks also use such analogies to physics. So basically,  statistical mechanics and its techniques are being applied to improve machine learning, especially the deep learning algorithms.
If you find machine learning interesting and worth making a career out of it, join a machine learning course to know more about this. Also, in this time of data revolution, a machine learning certification can be very useful for your career prospects.

Is Lisk the best Blockchain?

Posted on by Imarticus Learning

Lisk is a platform, out of many platform coins seeking to serve the broader applications of blockchain technology. Lisk is opposed to Bitcoin which is a digital currency. It is in its earlier stages of development and Lisk is being tested by multiple companies testing multiple methods in a race towards mass adoption.
The History of Lisk
Lisk was initially called Crypti and was created in September 2014. According to their Crunchbase profile, it was created as a fully stacked solution to deploy truly decentralized applications onto the blockchain. Founders Max Kordek and Oliver Beddows created the open source dapp platform to inspire more blockchain developers to participate in the cryptocurrency space. The team released their ICO in Q1 2016 and sold 100 million of their native tokens, LSK, in return for 14,000 BTC which was worth $5.8 million at that time. Since its inception and ICO, the team has made steady progress on the project – from the implementation of their road-map to the Q1 2018 rebranding.
Why is Lisk different?
Lisk is different than its competitors due to two major reasons – JavaScript and Dapp Sidechains.
JavaScript
According to a survey by the 2018 LinkedIn Emerging Jobs Report, the job market for blockchain has seen a 33 percent growth in the last year. Lisk is trying to help the blockchain job marketplace by letting dapp developers use JavaScript, which has continuously been the most popular language for programming for the past 6 to 8 years. A large number of websites use Javascript, which gives allows Lisk apps to easily mesh and connect with most of the internet. Lisk might turn out to be the topmost option when developers are looking for a platform to build apps with if the demand for developers continues to expand and JavaScript is still as popular.
fintech certification
Lisk Software Development Kit (SDK)
Lisk also offers the Lisk App SDK in order to make dapp development easy for blockchain developers. The Lisk Too SDK is a framework to deploy sidechains and develop blockchain applications. JavaScript is used to write everything – which means that one can develop platform-independent social networks, contract execution platforms, games, messengers, exchanges, prediction markets, online shops, loT applications, online shops and much more on one’s own blockchain, fully decentralized and without the trouble of complex consensus protocols pr P2P
This points to another part of Lisk that differentiates it from its competitors: Sidechains.
Dapp Sidechains
One of the central reasons why blockchain training is built is to increase the scalability of blockchain technology. This needs a platform to manage large amounts of activity and transactions happening constantly on their blockchain which has to be thoroughly planned, as seen with Ethereum. Lisk is applying the use of sidechains to allow apps to be built on their blockchain, without the risk of a congested network. This allows, theoretically for infinite scalability and increased security.
To conclude the Lisk team comes off as one of the most professional projects in the current space and it is also backed by prominent advisors. They entered the market with a unique solution to a major problem and have proven their ability to make partnerships in the industry. Lisk is definitely worth taking the time to research and given a chance, it might outlast the current market and see significant gains in the next market.

How can AI be integrated into blockchain?

Posted on by Imarticus Learning

Blockchain technology has created waves in the world of IT and fintech. The technology has a number of uses and can be implemented into various fields. The introduction of Artificial Intelligence Training (AI) makes blockchain even more interesting, opening many more opportunities. Blockchain offers solutions for the exchange of value integrated data without the need for any intermediaries. AI, on the other hand, functions on algorithms to create data without any human involvement.
Integrating AI into blockchain may help a number of businesses and stakeholders. Read on to know more about probable situations where AI integrated blockchain can be useful.
Creating More Responsive Business Data Models
Data systems are currently not open, and sharing is a great issue without compromising privacy and security. Fraudulent data is also another issue which makes it difficult for people to share data. Ai based analytics and data mining models can be used for getting data from a number of key players. The use of the data, in turn, would be defined in the blockchain records, or ledger. This will help data owners maintain the credibility, as the whole record of the data will be recorded.
AI systems can then explore the different data sets and study the patterns and behaviors of the different stakeholders. This will help to bring out insights which may have been missed till now. This will help systems respond better to what the stakeholder wants, and guess what is best for a potentially difficult scenario.
Creating useful models to serve consumers
AI can effectively mine through a huge dataset and create newer scenarios and discover patterns based on data behavior. Blockchain helps to effectively remove bugs and fraudulent data sets. New classifiers and patterns created by AI can be verified on a decentralized blockchain infrastructure, and verify their authenticity. This can be used in any consumer-facing business, such as retail transactions. Data acquired from the customers through blockchain infrastructure can be used to create marketing automation through AI.
Engagement channels such as social media and specific ad campaigns can also be used to get important data-led information and fed into intelligent business systems. This will eventually help the business cycle, and eventually improve product sales. Consumers will get access to their desired products easily. This will eventually help the business in positive publicity and improve returns on investments (ROI).
Digital Intellectual Property Rights
AI enabled data has recently become extremely popular. The versatility of the different data models is a great case study. However, due to infringement of copyrights and privacy, these data sets are not easily accessible. Data models can be used to show different architectures that cannot be identified by the original creators.
This can be solved through the integration of blockchain into the data sets. It will help creators share the data without losing the exclusive rights and patents to the data. Cryptographic digital signatures can be integrated into a global registry to maintain the data. Analysis of the data can be used to understand important trends and behaviors and get powerful insights which can be monetized into different streams. All of this can happen without compromising the original data or the integrity of the creators of the data.

Top Python Libraries For Data Science

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Top 10 Python Libraries For Data Science

With the advent of digitization, the business space has been critically revolutionized and with the introduction of data analytics, it has become easier to tap prospects and convert them by understanding their psychology by the insights derived from the same. In today’s scenario, Python language has proven to be the big boon for developers in order to create websites, applications as well as computer games. Also, with its 137000 libraries, it has helped greatly in the world of data analysis where the business platforms ardently require relevant information derived from big data that can prove conducive for critical decision making.

Let us discuss some important names of Python Libraries that can greatly benefit the data analytics space.

Theono

Theono is similar to Tensorflow that helps data scientists in performing multi-dimensional arrays relevant to computing operations. With Theono you can optimize, express and array enabled mathematical operations. It is popular amongst data scientists because of its C code generator that helps in faster evaluation.

NumPy

NumPy is undoubtedly one of the first choices amongst data scientists who are well informed about the technologies and work with data-oriented stuff. It comes with a registered BSD license and it is useful for performing scientific computations. It can also be used as a multi-dimensional container that can treat generic data. If you are at a nascent stage of data science, then it is key for you to have a good comprehension of NumPy in order to process real-world data sets. NumPy is the foundational scientific-computational library in Data Science. Its precompiled numerical and mathematical routines combined with its ability to optimize data-structures make it ideal for computations with complex matrices and data arrays.

Keras

One of the most powerful libraries on the list that allows high-level neural networks APIs for integration is Keras. It was primarily created to help with the growing challenges in complex research, thus helping to compute faster. Keras is one of the best options if you use deep learning libraries in your work. It creates a user-friendly environment to reduce efforts in cognitive load with facile API’s giving the results we want. Keras written in Python is used with building interfaces for Neural Networks. The Keras API is for humans and emphasizes user experience. It is supported at the backend by CNTK, TensorFlow or Theano. It is useful for advanced and research apps because it can use individual stand-alone components like optimizers, neural layers, initialization sequences, cost functions, regularization and activation sequences for newer expressions and combinations.

SciPy

A number of people get confused between SciPy stack and library. SciPy is widely preferred by data scientists, researchers, and developers as it provides statistics, integration, optimization and linear algebra packages for computation. SciPy is a linked library which aids NumPy and makes it applicable to functions like Fourier series and transformation, regression and minimization. SciPy follows the installation of NumPy.

NLKT

NLKT is basically national language tool kit. And as its name suggests, it is very useful for accomplishing national language tasks. With its help, you can perform operations like text tagging, stemming, classifications, regression, tokenization, corpus tree creation, name entities recognition, semantic reasoning, and various other complex AI tasks.

Tensorflow

Tensorflow is an open source library designed by Google that helps in computing data low graphs with empowered machine learning algorithms. It was created to cater to the high demand for training neural networks work. It is known for its high performance and flexible architecture deployment for all GPUs, CPUs, and TPUs. Tensor has a flexible architecture written in C and has features for binding while being deployed on GPUs, CPUs used for deep learning in neural networks. Being a second generation language its enhanced speed, performance and flexibility are excellent.

Bokeh

Bokeh is a visualization library for designing that helps in designing interactive plots. It is developed on Matplotib and supports interactive designs in the web browser.

Plotly

Plotly is one of the most popular and talked about web-based frameworks for data scientists. If you want to employ Plotly in your web-based model is to be employed properly with setting up API keys.

 

SciKit-Learn

SciKit learn is typically used for simple data related and mining work. Licensed under BSD, it is an open source. It is mostly used for classification, regression and clustering manage spam, image recognition, and a lot more. The Scikit-learn module in Python integrates ML algorithms for both unsupervised and supervised medium-scale problems. Its API consistency, performance, documentation, and emphasis are on bringing ML to non-specialists in a ready simple high-level language. It is easy to adapt in production, commercial and academic enterprises because of its interface to the ML algorithms library.

Pandas:

The open-source library of Pandas has the ability to reshape structures in data and label tabular and series data for alignment automatically. It can find and fix missing data, work and save multiple formats of data, and provides labelling of heterogeneous data indexing. It is compatible with NumPy and can be used in various streams like statistics, engineering, social sciences, and finance.

Theano:

Theano is used to define arrays in Data Science which allows optimization, definition, and evaluation of mathematical expressions and differentiation of symbols using GPUs. It is initially difficult to learn and differs from Python libraries running on Fortran and C. Theano can also run on GPUs thereby increasing speed and performance using parallel processing.

PyBrain

PyBrain is one of the best in class ML libraries and it stands for Python Based Reinforcement Learning, Artificial Intelligence. If you are an entry-level data scientist, it will provide you with flexible modules and algorithms for advanced research. PyBrain is stacked with neural network algorithms that can deal with large dimensionality and continuous states. Its flexible algorithms are popular in research and since the algorithms are in the kernel they can be adapted using deep learning neural networks to any real-life tasks using reinforcement learning.

Shogun:

Shogun like the other Python libraries has the best features of semi-supervised, multi-task and large-scale learning, visualization and test frameworks; multi-class classification, one-time classification, regression, pre-processing, structured output learning, and built-in model selection strategies. It can be deployed on most OSs, is written in C and uses multiple kernel learning, testing and even supports binding to other ML libraries.

 

Comprehensively, if you are a budding data analyst or an established data scientist, you can use the above-mentioned tools as per your requirement depending on the kind of work you’re doing. This is why it is very important to understand the various libraries available that can make your work much easier for you to accomplish your task much effectively and faster. Python has been traversing the data universe for a long time with its ever-evolving tools and it is key to know them if you want to make a mark in the data analytics field. For more details, in brief, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.