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Ace an Interview: Top 4 MS Excel FAQs You Need To Know RN

Posted on October 5, 2018May 23, 2020 by Dexlab

Ace an Interview: Top 4 MS Excel FAQs You Need To Know RN

For more than three decades, MS Excel has been wooing us all. Thanks to its incredible features that help users analyze data, easily and quickly!

Spreadsheets are popular, more than ever before. Companies prefer conducting Excel interviews – it ensures job applicants are qualified and well-versed in using Excel – before job confirmations.

Now, without further ado, we’ll provide you the 4 most common Excel interview questions that recruiters ask for sure. Pore over them – a little bit of advance preparation doesn’t hurt, does it?! It’s better to be confident on interview day than being completely vague about the questions shot at you!

What are important data formats found in Excel, and how are they used?

Some of the most popular data formats in Excel are given below:

Numbers – They are one of the most widely seen data types in Excel. They are mostly formatted with a customized number of decimal places, and can be seen with or without commas separating thousands of digits.
Dates – They are displaced in Excel in numerous ways, including the most conventional US style MM/DD/YYYY format. Technically, they are stored as numbers and can be manipulated through a bunch of date-based functions.
Strings – Text is mainly stored in Excel in the form of strings. It’s format which includes standard characters, like numbers, letters and punctuation.

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Define a function in Excel.

Functions come secondary when you are a regular Excel user. For so many times, you have used SUM, VLOOKUP and AVERAGE that you mostly have even forgotten using a function while creating spreadsheets.

Now, a function is like a slew of components – which when mixed in the right way turns out something effective and more useful. The name of the function defines the purpose of the function, like what it does – for example, a SUM or an AVERAGE.

The arguments of the function refer to the components that go into it. And the output of the function is whatever useful comes out the other side.

Highlight the order of operations while evaluating formulas in Excel.

For this, we’ve the acronym PEMDAS – it’s widely taught in mathematics classes around the world, and that’s what Excel follows. The full form of PEMDAS is:

Parentheses
Exponents
Multiplication
Division
Addition
Subtraction

What distinguishes absolute cell references from relative cell references? And when should you use each of them?

Cell reference is touted as one of the most remarkable features of MS Excel. It lets users include values of external cells in formulas dynamically, instead of working on values manually. Talking of the differences, relative cell references change dynamically while they are copied and pasted in a sheet, whereas, absolute cell references remain fixed when they are copied and pasted to other places in a sheet. The latter can be used on anywhere, on columns, rows or both at the same time. It’s mostly denoted with the $ sign.

Hope these questionnaire set help you prepare for the upcoming MS Excel interview – for more intensive knowledge and Excel expertise, take up our Advanced Excel Course in Noida. Seasoned industry specialists, encompassing course curriculum and convenient prices are the USP of our training. For more information, drop by our homepage.

The blog has been sourced from — www.deskbright.com/excel/excel-interview-questions

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#AskTheExperts: Best FAQs on Business Analytics

Posted on October 4, 2018May 23, 2020 by Dexlab

#AskTheExperts: Best FAQs on Business Analytics

Do you want to make a living as a successful business analyst? Does the prospect of analyzing data and drawing meaningful conclusions interest you? Are you thinking of taking the next big step into the career of analytics?

The demand for business analysts is soaring. It’s even touted as the highest paid job in the field of management. The job profile of a BA includes understanding a business organization critically, tapping into the ongoing business problems and filing a proper documentation of all business requirements and securing future success for the organization.

Below, we’ve whittled down few important FAQs on Business Analytics:

To understand or delve deeper into business analysis, opt for an excellent business analytics course in Delhi. Training courses as this will help you grab the hottest job in town.

Define business analysis.

Business analysis is a series of functions, implemented to assess the business needs and requirements and craft the best solution to ring bells of success in an organization or enterprise. This sequence of actions is generally performed by a business analyst.

Mention the industry and professional standards that a BA has to adhere to.

The most popular industry standards that have been set up for Business Analysts are OOAD principles and Unified Modeling Language (UML). They are recognized across the globe, so drafting requirements from any part of the world won’t be difficult.

What are the components of UML?

UML is a concoction of diverse concepts from a lot many sources.

For Structure: Actor, Attribute, Class, Component, Interface, Object, Package
For Behavior: Activity, Event, Message, Method, Operation, State, use case
For Relationships: Aggregation, Association, Composition, Depends, Generalization (or Inheritance)
Other Concepts: Stereotype – It qualifies the symbol it is attached to

Highlight the quality procedures that a BA normally follows.

Loud and clear, there exists no specific bar for such things, but if you ask us, Six Sigma and ITIL (Information Technology Infrastructural Library UK) have specific quality standards, which are more than enough. However, here we’ve enumerated some common things to consider:

Ensure the quality of communication is excellent and seamless.
Explore and decipher requirements of system functionality and user demands.
Collect, manage and analyze data for better business outcomes and future success of organizations in question.

Explain the procedure of Requirement Analysis.

JAD session usually precedes a Requirement Session. Business analysts, top notch sponsors and hardcore technical folks attend these significant sessions. In the end of the discussions, Business Analysts rifles through each requirement and asks from a valuable feedback. Now, if the sponsors and technical folks conclude all the requirements are as per business requirement, they will give an official signoff on business requirement documents, along with IT managers and business managers.

How do you define UML?

UML is the abbreviated form of Unified Modeling Language – which is referred to as a generic language for mentioning, envisioning, building and documenting the objects of software systems, business models and other non-software structures. Together, it’s a compilation of superior engineering practices that screams of proven success and functionability of large and complex models.

DexLab Analytics offers top of the line business analyst training Delhi – the course itinerary is crafted according to industry demands and seasoned consultants impart in-demand skill training to the aspiring candidates. For more information, visit their official site now.

The blog has been sourced from — www.wisdomjobs.com/e-university/business-analyst-interview-questions.html

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Most Popular R Programming Interview Questions with Answers to Help You Get Started

Posted on October 3, 2018October 3, 2018 by Dexlab

Most Popular R Programming Interview Questions with Answers to Help You Get Started

Brainchild of Ross Ihaka and Robert Gentleman, R programming language was first developed in 1993 with an exclusive and extensive catalog of statistical and graphical techniques and processes, including machine learning, time series, linear regression, statistical inference and lot more.

In the following section, we’re about to talk about top interview questions on R programming –perfect for both freshers and experienced consultants, this interesting interview guide covers almost all the major concepts of R and its applications.

Dive Down!

What is R programming?

R programming is an ideal language used for data analysis, and to build incredible statistical software. It’s widely used for a wide range of machine learning applications.

How to write syntax for R commands?

When you start writing commands in R, start using # in the beginning of the line, so that the commands are written as #division.

How to project data analysis outcome through R language?

The best way to convey the results would be by combining the results of data, code and analysis on a document and present the data for further reproducible research. It would help the user recheck the result and take part in the following discussions. The reproducible research aids in performing experiments easily and solving crucial problems.

What are the data structures found in R programming?

Homogenous and Heterogeneous are two data structures found in R programming. For same kinds of objects, we suggest using homogenous data structures as for Array, Vectors and Matrix. And for different types of objects, it’s better to stick to heterogeneous data structures.

How should you import data in R language?

Importing of data in R is done with the help of R commander GUI – it’s used to type commands and is also known as Rcmdr.

Here are 3 ways to import data into R:

As soon as you select data set from the dialog box, enter the date set name as asked.
R command can also be used to enter data – Data-> New Data Set (It’s only applicable for small data sets).
The user can also import data directly from URL, through simple ASCII file, statistical package or from clipboards.

Highlight the advantages of R programming language.

The user doesn’t get entangled in license restrictions and norms for using R programming.
It’s an open source software and completely free of cost.
It has several graphical capabilities.
It is easily run on a majority of hardware and OS (including 32 and 64-bit processors).

Mention the limit for memory in R.

For a 32-bit system, the memory of R is limited to 3GB. And for a 64-bit system, the limit is extended to 8TB.

With this, hope you are ready to crack a tough job interview on R programming – however, for those, who want to dig deeper into the intricacies of this fascinating programming language, we have fabulous R programming courses in Gurgaon. With them discover the path towards a dream career!

The blog has been sourced from — www.janbasktraining.com/blog/r-interview-questions-answers

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Best Data Science Interview Questions to Get Hired Right Away

Posted on October 1, 2018December 13, 2019 by Dexlab

Best Data Science Interview Questions to Get Hired Right Away

Data scientists are big data ninjas. They tackle colossal amounts of messy data, and utilize their imposing skills in statistics, mathematics and programming to collect, manage and analyze data. Next, they combine all their analytic abilities – including, industry expertise, encompassing knowledge and skepticism to unravel integral business solutions of meaningful challenges.

But how do you think they become such competent data wranglers? Years of experience or substantial pool of knowledge, or both? In this blog, we have penned down the most important interview data questions on data science – it will only aid you crack tough job interviews but also will test your knowledge about this promising field of study.

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What do you mean by data science?

Data is a fine blend of statistics, technical expertise and business acumen. Together they are used to analyze datasets and predict the future trend.

Which is more appropriate for text analytics – R or Python?

Python includes a very versatile library, known as Pandas, which helps analysts use advanced level of data analysis tools and data structures. R doesn’t have such a feature. Therefore, Python is the one that’s highly suitable for text analytics.

Explain a Recommender System.

Today, a recommender system is extensively deployed across multiple fields – be it music recommendations, movie preferences, search queries, social tags, research and analysis – the recommender system works on a person’s past to build a model to predict future buying or movie-viewing or reading pattern in the individual.

What are the advantages of R?

A wide assortment of tools available for data analysis
Perform robust calculations on matrix and array
A well-developed yet simple programming language is R
It supports an encompassing set of machine learning applications
It poses as a middleman between numerous tools, software and datasets
Helps in developing ace reproducible analysis
Offers a powerful package ecosystem for versatile needs
Ideal for solving complex data-oriented challenges

What are the two big components of Big Data Hadoop framework?

HDFS – It is the abbreviated form of Hadoop Distributed File System. It’s the distributed database that functions over Hadoop. It stores and retrieves vast amounts of data in no time.

YARN – Stands for Yet Another Resource Negotiator. It aims to allocate resources dynamically and manage workloads.

How do you define logistic regression?

Logistic regression is nothing but a statistical technique that analyzes a dataset and forecasts significant binary outcomes. The outcome has to be in either zero or one or a yes or no.

How machine learning is used in real-life?

Following are the real-life scenarios where machine learning is used extensively:

Robotics
Finance
Healthcare
Social media
Ecommerce
Search engine
Information sharing
Medicine

What do you mean by Power Analysis?

Power analysis is best defined as the process of determining sample size required for determining an impact of a given size from a cause coupled with a certain level of assurance. It helps you understand the sample size estimate and in the process aids you in making good statistical judgments.

To get an in-depth understanding on data science, enroll for our intensive Data Science Certification – the course curriculum is industry-standard, backed by guaranteed placement assistance.

The blog has been sourced from — intellipaat.com/interview-question/data-science-interview-questions

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Sell Yourself Well: Most Common Artificial Intelligence Interview Questions

Posted on September 27, 2018September 27, 2018 by Dexlab

Sell Yourself Well: Most Common Artificial Intelligence Interview Questions

Artificial Intelligence is seeping through our daily lives. Day by day, the robust technology is building a profound impact in the most beguiling ways, increasing the demand for AI professionals, blessed with the in-demand skills and expertise. No matter what, the future of AI seems to be all bright and beautiful.

This is why we are here to help you crack major AI job interview questions and guide your career through this fascinating field of science and technology. Go through the following questionnaire and showcase your knowledge, skill and talent. This will highlight how well you know the various nuances of AI and its implications.

What is Artificial Intelligence?

AI is the budding field of computer science and IT – which stresses on creating intelligent machines that imitate human brain’s cognitive abilities. It’s the simulation of human intelligence processed by machines using computer systems. Some of the notable AI activities are:

Speech recognition
Learning and planning
Problem-solving

What are the fields where AI is used?

Since its inception, AI is used across fields of extreme diversity, and some of them are mentioned below:

For customer support, including chatbots, sentiment analysis bots and humanoid support robots
In the linguistic field of processing natural language
Across IT fields, like computer software, sales prediction and analysis

Highlight the advantages of Fuzzy Logic Systems.

Following are the key advantages of Fuzzy Logic System:

Easy to understand
Simple constructible logics
Takes in inaccurate, ill-mannered and malformed input data
Flexibility to include and delete the rules as per convenience in the FLS

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What is FOPL?

FOPL is the short form of First-order Predicate Logic, which is a compilation of formal systems, where the statement is divided into two sections: a subject and a predicate. The predicate has the power to determine or modify a subject’s characteristics.

What do you mean by Greedy Best First Search Algorithm?

This is an incredible algorithm method, where the node nearest to the goal expands first. f(n) = h(n) is the default explanation of nodes, and this process is largely applied in the subsequent levels, where priority queue comes into question.

Do you know the artificial key in AI?

An artificial key in AI is built by assigning a number to an individual record, when a standalone key goes missing.

What is an alternate key in AI?

All the candidate keys except primary keys are called alternate keys.

Mention the components of Robotics.

These are the following components, which we would require to build a robot:

Actuators
Pneumatic Air Muscles
Sensors
Power Supply
Electric Motors
Muscle Wires
Ultrasonic and Piezo Motors

Hope these general job-interview questions have helped you grasp the underlying features of AI and its applications. For more research in this specific area of interest, we recommend artificial intelligence certification in Delhi NCR – DexLab Analytics is the go-to institute in this case.

The blog has been sourced from — www.janbasktraining.com/blog/artificial-intelligence-interview-questions

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Best Machine Learning Questions to Crack the Toughest Job Interview

Posted on September 26, 2018March 5, 2020 by Dexlab

Best Machine Learning Questions to Crack the Toughest Job Interview

The robust growth of artificial intelligence has ignited a buzz of activities along the scientific community. Why not? AI has no many dimensions – including Machine Learning. Machine Learning is a dynamic field of IT– where, one gets access to data and learn from that data, resulting into massive breakthroughs in the field of marketing, fraud detection, healthcare, data security, etc.

Day by day, companies are recognizing the potentials of Machine Learning. This is why investment in this notable field is spiking up as much as the demand for skilled professionals. Machine Learning jobs are found topping the list of emerging jobs displayed on LinkedIn – the median salary of a ML professional is $106,225, which pretty much suffices for a well-paying career option.

Importantly, we’ve picked out 5 best interview questions about Machine Learning that’ll optimize your chances of getting hired. Known to all, though ML skill is in high demand, grabbing a job in this booming field of technology is no mean feat. Employers seek particular knowledge and expertise in this field to get you hired. Our 5 best interview questions will help you expand your knowledge base on ML and hone your skills ahead of time.

You can also check out our Machine Learning training course – it comprises of industry-standard course material, real life use cases and encompassing curriculum.

What is Machine Learning?

While you define the exact meaning of the term, make sure you convey your good grip over the nuanced concepts of machine learning, and its real life applications. Put simply, you must show the interviewers how well versed you are in AI and machine learning skills.

What is the difference between deductive and inductive Machine Learning?

Deductive ML begins with a conclusion, and then proceeds towards making deductions about that conclusion. Inductive ML starts from examples and ends with drawing conclusions.

How to choose an algorithm for a particular classification problem?

The answer here is subject to the degree of accuracy and the size of the training set. For a tiny training set, low variance/high bias classifier will work, and vice versa.

Name some methods of reducing dimensionality

Integrate features with feature engineering, eliminating collinear features, or use algorithmic dimensionality reduction – these procedures can definitely reduce dimensionality.

What makes classification and regression differ?

For definite answers, classification is far better a tool. It predicts class or group membership. On the other hand, regression entails prediction of a response.

What does a Kernel SVM mean?

Kernel SVM is the short form of Kernel Support Vector Machine. Kernel methods are basically a specific class of algorithms used for patter analysis and amongst them the most popular one is the Kernel SVM.

What do you mean by a recommendation system?

Recommendation system is a common feature for those who have worked on Spotify or shopped at Amazon. It’s an information filtering system that forecasts what a user wants to hear or see, structured on the choice patterns given by the user.

No second thoughts, these interview questions will set you on the right track to crack an interview – but, if you want to gain a deeper understanding on Machine Learning or AI, obtain Machine Learning training Gurgaon from the experts at DexLab Analytics.

The blog has been sourced from —

https://www.simplilearn.com/machine-learning-interview-questions-and-answers-article

4 FAQs on Deep Learning

Posted on September 24, 2018September 26, 2018 by Dexlab

4 FAQs on Deep Learning

AI is revolutionizing the world. And Deep Learning is right in the heart of it. Yes, Deep Learning is a paradigm of machine learning, where algorithms are developed in a manner resembling the structure and function of human brain, and the entire phenomenon is called Artificial Neural Network.

From Facebook’s research to Netflix’s movie recommendations to DeepMind’s iconic algorithms, Deep Learning has indeed come a long way. Legendary innovations, awe-inspiring breakthroughs and latest technologies added to the flight. So, now that it became one of the hottest trends in the IT industry, you might be wondering what exactly in this nuanced AI concept… or how much does it includes studying mathematics and statistics… or how much deep is Deep Learning…

To answer all your questions and introduce you to the intricacies of such an in-demand IT skill, we’re here – this blog should help you in your quest for knowledge on Deep Learning.

Let’s get started!

What is Deep Learning and what makes it so popular?

Deep Learning is a significant part of AI that involves imitating the way a human brain functions, while learning some kind of knowledge. Put simply, this new branch of science has a lot to do with automating predictive analytics.

The superiority of human brain is unbeatable; this is why Deep Learning models is considered to be the most versatile and self-efficient man-made models ever been created. Using such an eccentric model, deriving crucial information from a humongous amount of data is what makes Deep Learning so special, and of course popular.

What are some of its real-life applications?

Facebook and Google are translating text into various different languages, all at the same time.
Siri, Cortana, Alexa are effortless working towards simplifying speech recognition techniques – their voice commands have ignited a whole new world of possibilities for a machine.
Deep Learning is increasingly influencing impactful computer vision applications, including OCR (Optical Character Recognition) and real time language translation.
Snapchat and Instagram use facial feature detection – which involves a larger chunk of Deep Learning technology.
In the healthcare domain, detection of malignant cells has been possible because of this latest technology.

What are the prerequisites to get started with Deep Learning?

Starting a career in this hottest field of science is not as difficult as it sounds to be. Deep Learning requires you to possess some knowledge on the following fields of study:

Mathematics
Statistics
Machine Learning
Basic skills for Coding

Which tools to possess to ace Deep Learning?

Hailing from the field of data science, I would always recommend Python certification course – because it is simple, robust, efficient, and has its own open source libraries and supports a large, active community of users. That being said, Python is a universal programming language; it can be used for development as well as implementation.

Besides Python, aspiring newbies are free to grasp top notch libraries, such as Keras. It simplifies the experimentation task and ensures access to the parameters that amplified the performance of similar models.

DexLab Analytics is a leading Python training institute in Delhi; if you are interested, you can browse through their course itinerary and make a well-informed decision.

The blog has been sourced from —

www.analyticsvidhya.com/blog/2018/05/deep-learning-faq

searchenterpriseai.techtarget.com/definition/deep-learning-deep-neural-network

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5 Big Challenges That Data Scientists Face Each Day

Posted on September 21, 2018May 23, 2020 by Dexlab

5 Big Challenges That Data Scientists Face Each Day

Data is lucrative; the world is revolving around how we churn out data. As a result, there’s been a high demand for data scientists. But of course, as rightfully said there’s no gain without pain – the promising field of data science is laden with many challenges, which needs to be overcome by expert consultants under needful guidance and with deft expertise.

Below, we’ve mentioned top 5 data science challenges, and how to handle them well…

Address the Specifics

Successful data scientists don’t try to do everything on their own. Instead, they individually focus on a single specific area. “I would encourage new professionals to understand that data science is a bit like medicine—it’s a vast and vague term that encapsulates wildly different practices under one roof,” said Tal Kedar, CTO at Optimove. “Data scientists [can have] very different engineering skill sets [and be] experienced with very different platforms and tools.”

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Be Guided By Your Intuition

Being a data scientist not only exposes you to the question of ‘how’, but also ‘why’. No longer do you just sift through data to make connections, instead you have to use your comprehensive knowledge to develop ‘mental model’, which can be accepted or rejected by your data.

Cross-Department Expertise is Appreciable

“The best data scientists are not just statisticians or machine learning experts; they are also an authority in the field or business where they are applying those skills,” said Kedar. It’s no hard fact, data scientists are arguably the best bridge between technical and non-technical teams. Quite naturally, whichever career they chose next, their skills will be treated as an asset to the next company in question.

Seamless Flow of Communication

Communication amongst the data teams is crucial – data scientists need to explain technical concepts to audiences from other departments, including executives and stakeholders, who might not belong from technical backgrounds. “It can be exciting to share all of the technical complexities that got you to your conclusions,” said Andrew Seitz, senior data analyst at Snowflake. “But what your stakeholders need are the key findings and action items. Save the details for the appendix (or Q&A).”

Raw Data Play

The biggest challenge for data scientists is to find ways of using the data – how the process of data extraction, data cleaning, data analysis and data modeling are carried out. Data scientists need to possess broad domain expertise in all programming languages, such as Python, R and SQL.

The work life of a data scientist revolves around creating clean data sets loaded with useful information on which machine learning algorithms can be applied. This kind of job is mostly treated as an art instead of science, because a majority of hard work and effort goes unnoticed when observing the final product, just like an artist’s craft.

The scope and capability of data science is encompassing, so are the challenges. But, of course, most of the challenges can be mitigated with considerable preparation and communication. How? With an intensive Python data science course – from the expert consultants of DexLab Analytics.

The blog has been sourced from — www.forbes.com/sites/laurencebradford/2018/09/06/8-real-challenges-data-scientists-face/#8adbc206d999

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What are important data formats found in Excel, and how are they used?

Some of the most popular data formats in Excel are given below:

Define a function in Excel.

Highlight the order of operations while evaluating formulas in Excel.

For this, we’ve the acronym PEMDAS – it’s widely taught in mathematics classes around the world, and that’s what Excel follows. The full form of PEMDAS is:

What distinguishes absolute cell references from relative cell references? And when should you use each of them?

Interested in a career in Data Analyst?

Define business analysis.

Mention the industry and professional standards that a BA has to adhere to.

What are the components of UML?

Highlight the quality procedures that a BA normally follows.

Explain the procedure of Requirement Analysis.

How do you define UML?

Interested in a career in Data Analyst?

What is R programming?

How to write syntax for R commands?

How to project data analysis outcome through R language?

What are the data structures found in R programming?

How should you import data in R language?

Highlight the advantages of R programming language.

Mention the limit for memory in R.

Interested in a career in Data Analyst?

What do you mean by data science?

Which is more appropriate for text analytics – R or Python?

Explain a Recommender System.

What are the advantages of R?

What are the two big components of Big Data Hadoop framework?

How do you define logistic regression?

How machine learning is used in real-life?

What do you mean by Power Analysis?

Interested in a career in Data Analyst?

What is Python?

What are the key features of Python?

What role does PYTHONPATH environment variable plays?

Name the supported data types in Python.

Do you know how to display contents of text file in a reverse order?

Pick out the invalid statement from below.

How do you manage memory in Python?

What do you mean by Python dictionary?

The blog has been sourced from:

Interested in a career in Data Analyst?

What is Artificial Intelligence?

What are the fields where AI is used?

Highlight the advantages of Fuzzy Logic Systems.

What is FOPL?

What do you mean by Greedy Best First Search Algorithm?

Do you know the artificial key in AI?

What is an alternate key in AI?

Mention the components of Robotics.

Interested in a career in Data Analyst?

What is Machine Learning?

What is the difference between deductive and inductive Machine Learning?

How to choose an algorithm for a particular classification problem?

Name some methods of reducing dimensionality

What makes classification and regression differ?

What does a Kernel SVM mean?

What do you mean by a recommendation system?

Classroom or Online Certification Courses to get you started

What is Deep Learning and what makes it so popular?

What are some of its real-life applications?

What are the prerequisites to get started with Deep Learning?

Which tools to possess to ace Deep Learning?

Interested in a career in Data Analyst?

Address the Specifics

Be Guided By Your Intuition

Cross-Department Expertise is Appreciable

Seamless Flow of Communication

Raw Data Play

Interested in a career in Data Analyst?

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