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I am a certified python developer with 3 yrs of Experience covering c++ and added other languages like perl, java etc. Interested to work with new tec...Read More
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I am a python developer, having 6.5 years of experience in python
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I am a full stack developer having 4 years of experience in developing robust scalable applications. Having expertise in Javascript, Angular JS, React...Read More
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Optimization deals with selecting the simplest option among a number of possible choices that are feasible or do not violate constraints. Python is used to optimize parameters in a model to best fit data, increase profitability of a possible engineering style, or meet another form of objective which will be described mathematically with variables and equations.

 

pyOpt is a Python-based package for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. Python programming uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem.

 

All optimisation downside solvers inherit from the Optimizer abstract category. The category attributes include the solver name (name), an optimizer kind symbol (category), and dictionaries that contain the solver setup parameters (options) and message output settings (informs). The class provides ways to check and alter default solver parameters (getOption, setOption), as well as a method that runs the solver for a given optimisation problem (solve).

 

Optimization solver

A number of constrained optimization solvers are designed to solve the general nonlinear optimization problem.

  1. PSQP: This optimizer is a preconditioned sequential quadratic programming algorithm. This optimizer implements a sequential quadratic programming method with a BFGS variable metric update.
  2. SLSQP: This optimizer is a sequential least squares programming algorithm. SLSQP uses the Han–Powell quasi-Newton method with a BFGS update of the B-matrix and an L1-test function in the step-length algorithm. The optimizer uses a slightly modified version of Lawson and Hanson’s NNLS nonlinear least-squares solver.
  3. CONMIN: This optimizer implements the method of feasible directions. CONMIN solves the nonlinear programming problem by moving from one feasible point to an improved one by choosing at each iteration a feasible direction and step size that improves the objective function.
  4. COBYLA: It is an implementation of Powell’s nonlinear derivative–free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust–region algorithm that employs linear approximations to the objective and constraint functions.
  5. SOLVOPT: SOLVOPT is a modified version of Shor’s r–algorithm with space dilation to find a local minimum of nonlinear and non–smooth problems.
  6. KSOPT: This code reformulates the constrained problem into an unconstrained one using a composite Kreisselmeier–Steinhauser objective function to create an envelope of the objective function and set of constraints. The envelope function is then optimized using a sequential unconstrained minimization technique.
  7. NSGA2: This optimizer is a non-dominating sorting genetic algorithm that solves non-convex and non-smooth single and multiobjective optimization problems.
  8. ALGENCAN: It solves the general non-linear constrained optimization problem without resorting to the use of matrix manipulations. It uses instead an Augmented Lagrangian approach which is able to solve extremely large problems with moderate computer time.
  9. FILTERSD: It use of a Ritz values approach Linear Constraint Problem solver. Second derivatives and storage of an approximate reduced Hessian matrix is avoided using a limited memory spectral gradient approach based on Ritz values.

 

To solve an optimization problem with pyOpt an optimizer must be initialized. The initialization of one or more optimizers is independent of the initialization of any number of optimization problems. To initialize SLSQP, which is an open-source, sequential least squares programming algorithm that comes as part of the pyOpt package, use:

>>> slsqp = pyOpt.SLSQP()

This initializes an instance of SLSQP with the default options. The setOption method can be used to change any optimizer specific option, for example the internal output flag of SLSQP:

>>> slsqp.setOption('IPRINT', -1)

Now Schittkowski’s constrained problem can be solved using SLSQP and for example, pyOpt’s automatic finite difference for the gradients:

>>> [fstr, xstr, inform] = slsqp(opt_prob,sens_type='FD')

By default, the solution information of an optimizer is also stored in the specific optimization problem. To output solution to the screen one can use:

>>> print opt_prob.solution(0)

 

Example:

The problem is taken from the set of nonlinear programming examples by Hock and Schittkowski and it is defined as

=======================================================================

      min            − x1x2x3

     x1,x2,x3

 

subjected to     x1 + 2x2 + 2x3 − 72 ≤ 0

                        − x1 − 2x2 − 2x3 ≤ 0

 

                        0 ≤ x1 ≤ 42

                        0 ≤ x2 ≤ 42

                        0 ≤ x3 ≤ 42

 

The optimum of this problem is at (x1∗ , x2∗ , x3* ) = (24, 12, 12), with an objective function value of f ∗ = −3456, and constraint values g (x∗ ) = (0, −72).

 

#======================================================================

# Standard Python modules

#======================================================================

import os, sys, time

import pdb

#======================================================================

# Extension modules

#======================================================================

#from pyOpt import *

from pyOpt import Optimization

from pyOpt import PSQP

from pyOpt import SLSQP

from pyOpt import CONMIN

from pyOpt import COBYLA

from pyOpt import SOLVOPT

from pyOpt import KSOPT

from pyOpt import NSGA2

from pyOpt import ALGENCAN

from pyOpt import FILTERSD

 

#======================================================================

def objfunc(x):

   

    f = -x[0]*x[1]*x[2]

    g = [0.0]*2

    g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0

    g[1] = -x[0] - 2.*x[1] - 2.*x[2]

   

    fail = 0

    return f,g, fail  

 

#======================================================================

# Instantiate Optimization Problem

opt_prob = Optimization('Hock and Schittkowski Constrained Problem',objfunc)

opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addObj('f')

opt_prob.addCon('g1','i')

opt_prob.addCon('g2','i')

print opt_prob

 

# Instantiate Optimizer (PSQP) & Solve Problem

psqp = PSQP()

psqp.setOption('IPRINT',0)

psqp(opt_prob,sens_type='FD')

print opt_prob.solution(0)

 

# Instantiate Optimizer (SLSQP) & Solve Problem

slsqp = SLSQP()

slsqp.setOption('IPRINT',-1)

slsqp(opt_prob,sens_type='FD')

print opt_prob.solution(1)

 

# Instantiate Optimizer (CONMIN) & Solve Problem

conmin = CONMIN()

conmin.setOption('IPRINT',0)

conmin(opt_prob,sens_type='CS')

print opt_prob.solution(2)

 

# Instantiate Optimizer (COBYLA) & Solve Problem

cobyla = COBYLA()

cobyla.setOption('IPRINT',0)

cobyla(opt_prob)

print opt_prob.solution(3)

 

# Instantiate Optimizer (SOLVOPT) & Solve Problem

solvopt = SOLVOPT()

solvopt.setOption('iprint',-1)

solvopt(opt_prob,sens_type='FD')

print opt_prob.solution(4)

 

# Instantiate Optimizer (KSOPT) & Solve Problem

ksopt = KSOPT()

ksopt.setOption('IPRINT',0)

ksopt(opt_prob,sens_type='FD')

print opt_prob.solution(5)

 

# Instantiate Optimizer (NSGA2) & Solve Problem

nsga2 = NSGA2()

nsga2.setOption('PrintOut',0)

nsga2(opt_prob)

print opt_prob.solution(6)

 

# Instantiate Optimizer (ALGENCAN) & Solve Problem

algencan = ALGENCAN()

algencan.setOption('iprint',0)

algencan(opt_prob)

print opt_prob.solution(7)

 

# Instantiate Optimizer (FILTERSD) & Solve Problem

filtersd = FILTERSD()

filtersd.setOption('iprint',0)

filtersd(opt_prob)

print opt_prob.solution(8)

 

Solving non-linear global optimization problems could be tedious task sometimes. If the problem is not that complex then general purpose solvers could work. However, as the complexity of problem increases, general purpose global optimizers start to take time. That is when need to create your problem specific fast and direct global optimizer’s need arises.

 

We have an specialized team with PHD holders and coders to design and develop customized global optimizers. If you need help with one, please feel free to send your queries to us.

 

We first understand the problem and data by visualizing it. After that we create a solution to your needs.

 

Please do read to understand what a solver is and how it works - If you want to create your own simple solver. This is not exactly how every solver works, however, this will give you a pretty solid idea of what is a solver and how it is supposed to work.

New companies and the entrepreneurial impulse are key elements of the U.S. economy, and perhaps part of the nation’s mythology in the American dream. The U.S. federal agency in 2002 counted around twenty three million companies and nearly twenty four million business “establishments” that generated, in total, nearly $23 billion in sales. That’s if they make it. In a 2006 article entitled, “The Seven Pitfalls of Business Failure,” economic expert Brian Head of the small Business Administration noted that 30% new businesses fail inside the first five years of operation. However with careful coming up with, establishing a new company is a successful and profitable undertaking.

Steps for registering a new company

Choose your Company Location

Your company location determines the taxes, zoning laws, and regulations your company will be subject to. You will need to form a strategic decision regarding which state, city, and neighborhood you select to begin your company in. Where you locate your company depends in part on the location of your target market, business partners, and your personal preferences. In addition, you should consider the costs, benefits, and restrictions of different government agencies.

Choose right Company/Business type

The decision to choosing the right business structure is very important because the type of business you decide on influences everything from daily operations, to taxes, to how much of your personal assets are at risk.

  • Sole proprietorship: A sole proprietorship is easy to form and gives you complete control of your business. You're automatically considered to be a sole proprietorship if you do business activities but don't register as any other kind of business. Sole proprietorship do not produce a separate business entity. This means your business assets and liabilities are not separate from your personal assets and liabilities. You can be held personally liable for the debts and obligations of the business. Sole proprietors can also be hard to raise money because you can't sell stock, and banks are hesitant to lend to sole proprietorship.
  • Partnership: Partnerships are the best structure for two or more people to possess a business together. There are two common forms of partnerships: limited partnerships (LP) and limited liability partnerships (LLP).
    • Limited partnerships have only one general partner with unlimited liability, and all other partners have limited liability. The partners with limited liability also tend to have limited control over the company, which is documented in a partnership agreement.
    • Limited liability partnerships are similar to limited partnerships, but give limited liability to every owner. An LLP protects every partner from debts against the partnership, they won't be responsible for the actions of other partners.
  • Limited liability Company (LLC): LLCs protect you from personal liability in most instances, your personal assets like your vehicle, house, and savings accounts won't be at risk in case your LLC faces bankruptcy or lawsuits. Profits and losses can get passed through to your personal income without facing corporate taxes. LLCs can be a good choice for medium- or higher-risk businesses, owners with significant personal assets they want to be protected, and owners who want to pay a lower tax rate than they would with a corporation.
  • Corporation: The incorporation process is more complicated than the other two options but will have many potential benefits for your on-line business. One of the most notable differences regarding this structure is that the corporation becomes a separate entity from you as the owner. This means you’re not in person financially liable for what happens to the corporation, and your personal assets are protected from the liabilities created by your shop. When your store grows and the liabilities you’re exposed to grow with it this is a huge benefit.

Choose your Company Name

You can find the right business name with creativity and market research. Once you’ve picked your name, you should protect it by registering it with the right agencies.

You’ll want to decide on a business name that reflects your brand identity and doesn’t clash with the types of goods and services you provide. Once you take decision on a name you prefer, you would like to protect it. There are four different ways to register your company name. Every way of registering your name serves a special purpose, and some may be legally required depending on your business structure and location.

  • Entity name protects you at state level
  • Trademark protects you at a federal level
  • Doing Business as (DBA) doesn’t give legal protection, but might be legally required
  • Domain name protects your business web site address

Each of those name registrations are legally independent. Most small businesses try to use an equivalent name for each kind of registration, but you’re not commonly required to.

Register your Company

Register your business to make it a distinct legal entity. How and where you need to register depends on your business structure and business location.

Your location and business structure determine how you’ll need to register your business. Verify those factors first, and registration becomes terribly easy. In some cases, you don’t need to register at all. If you conduct business as yourself using your legal name, you won’t need to register anywhere. But keep in mind, if you don’t register your business, you'll miss out on personal liability protection, legal benefits, and tax benefits.

Most businesses don't need to register with the federal government to become a legal entity, other than simply filing to get a federal tax ID. Small businesses sometimes register with the federal government for trademark protection or tax exempt status. If you would like to trademark your business, brand or product name, file with the United States Patent and Trademark office once you’ve formed your business. If you would like tax-exempt status for a noncommercial corporation, register your business as a tax-exempt entity with the IRS.

Get Employee Identity Number or State Tax ID

Getting an EIN number can help you identify your business entity. Most companies need this federal employee identification number to conduct business. An EIN is a nine-digit number that refers to only your business or non-profit entity. It is your business’s social security number. This number allows the IRS to identify your business and attach it to the correct business name for tax purposes. Just a unique business name is not enough.

Your employer identification number (EIN) is your federal tax ID. You need it to pay federal taxes, hire employees, open a bank account, and apply for business licenses and permits. You should do it right once you register your business. Your business desires a federal tax ID number if it does any of the following:

  • Pays employees
  • Operates as an organization of partnership
  • Files tax returns for employment, excise, or alcohol, tobacco, and firearms
  • Withholds taxes on income, other than wages, paid to a non-resident alien
  • Works with certain types of organizations

The need for a state tax ID number ties directly to whether your business must pay state taxes. Sometimes, you can use state tax ID numbers for other functions, like protection against identity theft for sole proprietors. Tax obligations differ at the state and local levels, so you'll need to check with your state's websites. To know whether you need a state tax ID, research and understand your state's laws regarding income taxes and employment taxes, the two most common forms of state taxes for small businesses.

Apply for Licenses

Most small businesses need a combination of licenses and permits from both federal and state agencies. The requirements and charges vary based on your business activities, location, and government rules.

The licenses and permits you need from the state, county, or city will depend on your business activities and business location. Your business license fees will vary. States tend to regulate a broader range of activities than the centralized. As an example, business activities that are commonly regulated domestically include auctions, construction, and dry cleaning, farming, plumbing, restaurants, retail, and vending machines.

Some licenses and permits expire after a set period of time. Keep close track of when you need to renew them it's usually easier to renew than it has to apply for a new one. You'll have to analysis your own state, county, and city regulations. Industry requirements usually vary by state.

Special Bank Account for your Company

Open a business account once you are ready to start accepting or spending money as your business. A business bank account helps you keep legally compliant and protected. It also provides benefits to your customers and employees. Most business bank accounts provide perks that don't come with a standard personal bank account.

  • Protection: Business banking offers limited personal liability protection by keeping your business funds separate from your personal funds. Merchant services also offer purchase protection for your customers and ensures that their personal information is secure.
  • Professionalism: Customers will be able to pay you with credit cards and make checks out to your business instead of directly to you.
  • Preparedness: Business banking usually comes with the option for a line of credit for the company. This can be used in the event of an emergency, or if your business needs new equipment.
  • Purchasing power: Credit card accounts can help your business make large startup purchases and help establish a credit history for your business.

Get Business Insurance

Business insurance coverage protects businesses from losses due to events that may occur during the normal course of business. There are many types of insurance for businesses including coverage for property damage, legal liability and employee-related risks. Companies evaluate their insurance needs based on potential risks, which can vary depending on the type of environment in which the company operates.

Business insurance protects you from the unexpected costs of running a business. Accidents, natural disasters, and lawsuits could run you out of business if you’re not protected with the right insurance.

Speak to insurance agents to find out what kinds of coverage makes sense for your business, and compare terms and prices to find the best deal for you. Here are six common kinds of business insurance types are:

  • General liability insurance
  • Product liability insurance
  • Professional liability insurance
  • Commercial property insurance
  • Home-based business insurance
  • Business owner’s policy

Applying for a data scientist job can be an intimidating task as there can be many things to take care in an interview process — right from justifying the practical knowledge to showcasing the coding skills. While we have earlier discussed articles on how to crack data science interview and what are the things to keep in mind while appearing for an interview for data science-related roles. This article deals with some of the things that you might be doing wrong if ever you are rejected in a data science interview.

 

Here are five things you may have been doing wrong:

 

Not focusing on the job description: The definition of data science jobs is not always the same and may mean different roles and responsibilities for different companies. Some of the commonly required skills may be a PhD in statistics, Excel skills, machine learning generalist, Hadoop skills, Spark skills, among others. The job description largely varies for every company and it is important to thoroughly dig it and carefully look for specific skills, tools and languages. It is important to display the skills that the potential recruiter is looking for so that they can shortlist you easily.

 

No specific distinction of technical skills: The technical skills in data science and analytics industry is quite wide and not mentioning your strengths correctly might jeopardise your chances of cracking the interview. For instance, it might not be apt to just say machine learning skills as it might include a whole spectrum of things ranging from linear regression to neural networks. And these sub-areas might further require knowledge of specific tools and software such as Python, Keras, R or Pandas. It is always advisable to give specific skills that you master than describing generic skills as might confuse recruiters of the exact skills that you pose.

 

Incorrect information and rephrasing work experience: To suit the data science job roles, many a times candidates rephrase their previous work experiences such as in the IT or software domains to present it as data science job roles, which might disguise your abilities initially but expose the depth and understanding of the skills later. You might have included job description aligning in a way that suits data science job roles but you might not have a deeper experience in it, which may get noticeable by recruiters during a one-to-one interaction. Mentioning of incorrect or misleading facts may also lead to recruiters rejecting you. For instance, the resume may state achieved an accuracy of say 90% on the test run, but what are the baseline and state-of-the-art score for this dataset to claim these numbers?

 

No mention about the projects that you have worked on from the scratch: Many times the only projects that a candidate mention in a resume are the ones they have done on Kaggle. While Kaggle is a platform for a lot of researchers to explore avenues in data science, it also serves as a source of practice for people who aren’t a pro in data science field and are trying to make a transition, mentions a recruiter in one of the forums. There are different kinds of the audience at Kaggle such as those who are playing around with the dataset or getting to know how problem-solving in data science works like, without having actual experience in solving or creating a new data science problems. So, listing just Kaggle project might be good but not definitive of how good your data science skills are. Even if it a Kaggle project, it is better if it is done from scratch. Other than that, it is important to mention the projects that you have worked on. It gives recruiters a chance to understand the problems you faced and the way you approached the problem, thereby giving them a glance at your problem-solving abilities.

 

The resume is full of buzzwords and no concrete proof of your skills: While the resume may suit the job description, but there are chances that you are rejected if there are too many buzzwords in the resume and no concrete way to prove that you actually pose those skills. You may mention in the resume that you have had experience with Hadoop, Excel or certain areas, but if you have showcased it real-time on platforms such as GitHub, it convinces the potential employers of the skills you have. They can look through various projects you have been a part of and see how you have dealt with real data. Hiring managers like to see the time that a candidate has spent from start to finish. Having a portfolio gives recruiters just that. There may be fancy sounding terms in the resume, but if you don’t have a proof to showcase it, you might be rejected for a potential data science job role.

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