Numbers in rectangular pattern-1 in python assignment expert

Striped Rectangle

Given an integer number

The first line of input is an integer

In the given example the striped rectangular pattern of

7 rows and 5 columns. Therefore, the output should be 

M=int[input[]]
N=int[input[]]


for i in range[M]:
    for j in range[N]:
        if [i%2==0]:
            print["+",end=" "]
        else:
            print["-",end=""]
    print[]

Learn more about our help with Assignments: Python

Given an integer number M, N as input. Write a program to print a striped rectangular pattern of M rows and N columns using [+ and -] character.

The first line of input is an integer M.

The second line of input is an integer N.

In the given example the striped rectangular pattern of

7 rows and 5 columns. Therefore, the output should be

+ + + + +

- - - - -

+ + + + +

- - - - -

+ + + + +

- - - - -

+ + + + +

numbers = [1, 2, 3, 4]
max_num = numbers[0]
for i in numbers:
    if i>max_num:
        max_num = i
print[max_num]

4

numbers = [1, 2, 3, 4]
max_num = max[numbers]        
print[max_num]

4

Python Programming 

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If you want to choose one from Python & R to handle data science and machine learning task. Both language are has own strength and importance, and both are great choice R and Python are both open-source programming languages with a large community. New libraries or tools are added continuously to their respective catalog. R is mainly used for statistical analysis while Python provides a more general approach to data science.

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AI vs ML vs DL

• What is Artificial Intelligence?

• What is Machine Learning?

• What is Deep Learning?

Hire Artificial intelligence Experts

The term was first introduced in 1956 in a conference where researchers wanted to digitized how human brain works

• AI is the science and engineering of making computers behave in ways that until recently, we thought required human intelligence, Andrew Moore

• AI is a moving target based on the capabilities that human possessed but machines do not, e.g., emotion – AI encompasses technology advances in different fields such as Machine Learning, Human Computer Interaction, etc

• Example of AI: DeepBlue, and to some extent: Google Home, Siri and Alexa

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Hire Machine Learning and Deep Learning Experts

Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience ~ Tom Mitchell

• A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E ~ Tom Mitchell

• The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful.

​

Deep Learning

• It is a class of machine learning algorithms inspired by the structure of a human brain.

• Deep learning algorithms use complex multi-layered neural networks, where the level of abstraction increases gradually by non-linear transformations of input data.

How can a machine learn?

Dataset

– The samples need to be representative

– The samples can include numbers, images, text, etc

Features

– Important piece of data that work as the key to the solution of the task

– Tell the machine/program what to pay attention to

Algorithm

– The same task can be solved using different algorithm

– The accuracy or speed of getting results can be different

If the dataset quality is high, the features were chosen right, an ML-powered system can be better than human for a given task

Machine Learning Problem

• Many real-world problems are complex. Inventing specialized algorithms to solve them perfectly every time is not practical

• Some example

            – How can we predict future traffic pattern at an intersection?

            – Is it cancer?

            – What is the market value of this house five years from now?

            – Which of these candidates are the perfect one for the job?

            – Which of these people can be my best friend/partner?

            – Will a certain people like this movie or not?

Machine Learning Algorithms In Which You Can Get Help

Generally divided into supervised and unsupervised learning, also reinforced learning, based on whether the they are trained with human supervision, and whether the training data is labeled or not

• Whether or not they can learn incrementally on the fly [online versus batch learning]

• Whether they work by simply comparing new data points to known data points, or instead detect patterns in the training data and build a predictive model, much like scientists do [instance-based versus model-based learning]

Supervised Learning

• The training data fed to the algorithm includes the desired solutions, called labels

• It models the relationship between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships learned from past data

• The goal is to develop a finely tuned predictor function h[x] [sometimes called the “hypothesis”] so that, given input data x about a certain domain, e.g., square footage of a house], it will predict interesting value h[x], e.g., the market price of the house.

• Two major categories are regression and classification

Some of the most important supervised learning algorithms include:

– k-Nearest Neighbors

– Linear Regression

– Logistic Regression

– Support Vector Machines [SVMs]

– Decision Trees and Random Forests

– Neural networks

​

Unsupervised Learning

• In unsupervised learning, the training data is unlabeled

• The unsupervised machine learning is typically tasked with finding relationships and correlation within data.

​          - Used mostly for pattern detection and descriptive modeling

Some of the most important unsupervised learning algorithms include:

– Clustering

– Visualization and dimensionality reduction

– Association rule learning

Python Machine Learning Assignment Help

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Realcode4you Expert Team Help to:

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How to make a team: GitHub support many features which makes it better. Here you can create own team that can write code from each end. Here code save after implementing from any team member end site. 

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Hire top rated and experienced machine learning and data science expert that can implement your code in your GitHub Repository with proper coding format with comments. You need to send your requirement details at:

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Hire top rated and experienced machine learning and data science expert that can help to writing research paer with complete code implementation. For more details you can send your query at: or contact us at: +91 82 67 81 38 69

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This is the process of altering data. We we select specific features hat can help to increase the models efficiency. There are some steps which are used to implement machien learning task; 

• Gathering data.

• Cleaning data.

• Feature engineering.

• Defining model.

• Training, testing model and predicting the output.

​

Some words that are written by experienced machine learning experts; Much of the success of machine learning is actually success in engineering features that a learner can understand.

​

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.

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Data Bricks Assignment Help

Now a days data is increases day by day  so that data scientist face the problem to run the large size data which need to implement using Spark. Data bricks is a data engineering tool that are used by companies to process and transform by large quantity of data. 

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Databricks includes a variety of datasets within the Workspace that you can use to learn Spark or test out algorithms. You’ll see these throughout the getting started guide. The datasets are available in the /databricks-datasets folder

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If you need any help or support related to data bricks project then no need to worry about it. Realcode4you data bricks expert help to run the code on this.

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Feel free to ask any query at any time. 

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Basic Structure to write Dissertation Report

• ABSTRACT

• INTRODUCTION

• LITERATURE REVIEW

• Project Topics Description

• METHODOLOGY

• Model Description and Evaluation

• Conclusion

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It helps to automate ML workflow. It enable the sequence data to be transformed and correlated together in a model to analyzed and achieve outputs. Realcode4you.com machine learning team help to work as per ML pipeline using many assembling techniques. A well-organised pipeline makes the implementation more flexible. Below the some important machine learning techniques that use to create complete pipeline:

• Data collection

• Data cleaning

• Feature extraction [labelling and dimensionality reduction]

• Model validation

• Visualisation

Benefits of Machine learning Pipeline

There are many benefits, some of these are given below:

• Flexibility

• Extensibility

• Scalability

Why it is important for ML Industries

it will help companies interpret uncovered patterns to make better decisions.

• Timely Analysis And Assessment

• Real-Time Predictions

• Transforming Industries

Research Paper Writing Help, Master thesis Writing and Master Dissertation Help

Realcode4you.com has the excellent team to write your research paper related to Master Program. Here we have write research paper in all report writing standard format like APA7, IEEE and simple word document file.

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Below the some sample of research papers that successfully completed by our experts in last year.

Sample Research Paper 1

Topic - Grammarinator-Open-Source-Fuzzer

I. ABSTRACT

Fuzzing, or random testing, is an increasingly popular testing technique. The strength of this approach lies in its ability to generate many helpful test cases without consuming costly human resources. In addition, randomness can often create anomalies that are not perceptible to human testers. In this report, we introduce Grammarinator, a general-purpose test generator capable of using existing parsing grammars as a template. Since the model can act as both an analyzer and a generator, this tool can provide the capabilities of generation based and mutation-based dimmers. The presented device is actively used to test various JavaScript engines and has found more than 100 unique issues.

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Sample Research Paper 2

Topic - Book Depository to get insights on the Trends

I. ABSTRACT

We start the discussion with the introduction of the books and importance of printing. We discuss its usage in our everyday life whether nowadays we go for an online pdf books and journal even more. This is just as great as it can be. Then we do the background check about the printing of things and transferring this information through printing on clays and then woods stuffs and then finally to woodblock printing happening in Europe at around 15th century. Then we tell our aims and objectives that we will be following as our guiding teacher for this research. It is followed by Methods that we use in our research including importing of datasets, analysing its basic properties. Then data cleaning is done along with preprocessing. Which is not just limited to dropping columns with huge percentage nan values but also making the datatypes uniform the columns. If there is a numpy array in form of a string, it is handled with the help of python libraries. Then we add a new feature called Year from the existing feature publication-date. It is used to plot and see how different categories or genres of each book vary as time/year passes. Finally the use of ML algorithm to cluster our vectorized form to form some clusters. Keywords—machine learning algorithms, forming clusters, data analysis, data cleaning, ML Algorithms, Data Science, Charts and Graphs with matlplotlib

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Sample Research Paper 3

Topic - Image Segmentation Using U-Net and Mask R-CNN

I. ABSTRACT

The U-Net model is based on Convolutional Neural Network[CNN] where CNN is capable of handling high dimensional raw data through convolutions. U-Net is capable of combining and concatenating multi-scale features to detect and segment objects. The Mask R-CNN model is based on regression networks to detect and segment objects. Mask RCNN is capable of handling high dimensional input data through convolutions. Mask R-CNN is capable of refining the detection result through semantic segmentation. A brief comparison and analysis on both models is presented.

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Sample Research Paper 4

Topic - Student Allocation System

I. ABSTRACT

In the last year of their studies toward an undergraduate degree, students in many different degree programs are obliged to finish projects as an integral component of the necessary curriculum that they have been following throughout their studies. Students will be able to generate a list of acceptable project possibilities by reading project descriptions, and from that list, they will either explicitly or implicitly choose a project based on their own preferences. This will allow students to demonstrate their critical thinking and problem-solving abilities. Students will be able to have a greater sense of ownership over the project selection process as a result of this. Their is the possibility of there being a limit placed on the number of students who are permitted to take part in a project or on the number of students that a professor is ready to oversee.

Hire Reinforcement Learning Expert

Reinforcement Learning is the technique of machine learning that train the the models to take the sequence of action. Realcode4you.com expert team has the deep knowledge in all Reinforcement Learning problems.

The main goal is long-term, such as game-playing, robotics, etc.

In this the agent continues doing these three things [take action, change state/remain in the same state, and get feedback], and by doing these actions, he learns and explores the environment.

Sample Project Description

For the fifth and final programming assignment, you will implement a reinforcement learner and apply it to the racetrack problem. The racetrack problem is a popular control problem, often used in reinforcement learning assignments. The goal is to control the movement of a race car along a pre-defined racetrack. You want your race car to get from the starting line to the finish line in a minimum amount of time. In this version of the problem, your agent will be the only racer on the track, so it is more like a time trial than a full competitive race. More formally, at each time step, the state of the agent can be encoded in four variables: xt and yt are the x and y coordinates corresponding to the location of the car at time step t [we treat the racetrack as being laid out on a Cartesian grid]. The variables vx = ˙xt and vy = ˙yt represent the x and y components of the car’s velocity at time t [note that x˙ denotes the first derivative of x with respect to time]. The control variables for the car are ax = ¨x and ay = ¨y, which represent the x and y components of an acceleration vector to be applied at the current time step [note that x¨ denotes the second derivative of x with respect to time]. The system is governed by an approximation of the standard laws of kinematics:

xt ≡ x position

yt ≡ y position

vxt = ˙xt = xt − xt−1 ≡ x speed

vyt = ˙yt = yt − yt−1 ≡ y speed

axt = ¨xt = ˙xt − x˙ t−1 ≡ x acceleration

ayt = ¨yy = ˙yt − y˙t−1 ≡ y acceleration

Get Help In Data Analysis Task

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Microsoft Power BI

Microsoft Power BI is a top business intelligence platform with support for dozens of data sources. It allows users to create and share reports, visualizations, and dashboards. Users can combine a group of dashboards and reports into a Power BI app for simple distribution. Power BI also allows users to build automated machine learning models and integrates with Azure Machine Learning.

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Tableau

Tableau is a data visualization and analytics platform that allows users to create reports and share them across desktop and mobile platforms, within a browser, or embedded in an application. It can run on the cloud or on-premises. 

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Jupyter Notebook

Jupyter Notebook is a free, open source web application that can be run in a browser or on desktop platforms after installation using the Anaconda platform or Python’s package manager, pip. It allows developers to create reports with data and visualizations from live code. The system supports more than 40 programming languages.

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RapidMiner

RapidMiner provides all the technology users need to integrate, clean, and transform data before they run predictive analytics and statistical models. Users can perform nearly all of this through a simple graphical interface. RapidMiner can also be extended using R and Python scripts, and numerous third-party plugins are available through the company's marketplace.

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R

R is an open source programming language and computing environment with a focus on statistics and graphical data visualization. R features numerous graphical tools and over 15,000 open source packages available, including many for loading, manipulating, modeling, and visualizing data.

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Python

Python is an open source, high-level programming language that's often used by technical analysts and data scientists. It now boasts more worldwide developers than Java and has more than 200,000 available packages.

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Excel

Microsoft Excel is the most common tool used for manipulating spreadsheets and building analyses. With decades of development behind it, Excel can support almost any standard analytics workflow and is extendable through its native programming language, Visual Basic.

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Oracle Analytics Cloud

Oracle Analytics Cloud is a suite of cloud business intelligence and analytics applications. It's focused on helping large enterprises transition their legacy systems to a modern cloud platform.

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Google Data Studio

Google Data Studio is a free dashboarding and data visualization tool that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Thanks to its integration with other Google services, Data Studio is great for those who need to analyze their Google data. 

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Topics In Which You Can Get Help

Socket Programming

Socket programming is a way of connecting two nodes on a network to communicate with each other. One socket[node] listens on a particular port at an IP, while the other socket reaches out to the other to form a connection.

Python Data Structure

Python has implicit support for Data Structures which enable you to store and access data. These structures are called List, Dictionary, Tuple and Set.

If you need any help related to python data structure then contact us so we can help you.

Python Data Science

Python one of the best language used by data scientist for various data science projects/application. Python provide great functionality to deal with mathematics, statistics and scientific function.  

Python machine Learning

Python community has developed many modules to help programmers implement machine learning. 

Machine learning is a type of artificial intelligence [AI] that provides computers with the ability to learn without being explicitly programmed.

Django

Django is a popular Python open-source web development framework used for rapid web development and clean, pragmatic design.

Built by experienced developers, it takes care of much of the hassle of web development

Flask

Flask is a web development framework developed in Python. It is easy to learn and use. It uses Jinja2 templates.  Flask is known as a micro-framework because it is lightweight and only provides components that are essential.

Hire expert to get instant help with an affordable price.

Tkinter

Tkinter is the Python binding to the Tk / Tcl GUI toolkit. With Tkinter, we can create GUI applications very fast and easily. Tk offers native look and feel on all platforms. Hire Tkinter Expert and get instant help with affordable price.

PyQT5

PyQt5 is cross-platform GUI toolkit, a set of python bindings for Qt v5. One can develop an interactive desktop application with so much ease because of the tools and simplicity provided by this library.

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Python Visualization

Data visualization is the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general. It is a way to summarize your findings and display it in a form that facilitates interpretation and can help in identifying patterns or trends.

Python OOPs Programming

Object-oriented programming is a programming paradigm that provides a means of structuring programs so that properties and behaviors are bundled into individual objects. Hire OOPs programming expert to get help.

Regular Expression

Regular Expressions, often shortened as regex, are a sequence of characters used to check whether a pattern exists in a given text [string] or not.

In Python, regular expressions are supported by the re module.

Class, Object & Loop

A Python class is like an outline for creating a new object. An object is anything that you wish to manipulate or change while working through the code. Every time a class object is instantiated, which is when we declare a variable, a new object is initiated from scratch.

Why Student Need Python Assignment 

Shortage of Time

A number of students already have other work at the same time or which has same due date then to manage all task is difficult. 

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Many of the students have enough knowledge and skills but still, they are struggling only because of their interest factor.

Challenges Faced by Students and Professionals

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2. Challenging in Code Explanations: Most of coding task related to case study, research paper implementation and report writing with coding. If student not have writing skills, then he also loss the marks. Our expert provides full code explanation and provide proper document in which everything mentioned in steps. Here you can also get full research paper implementation and case study related help.

3. High plagiarism risk: Being a prevalent subject, plagiarism is a massive risk in the students’ assignments. Hence, this stands as a challenge to remove all plagiarism and generate fully plagiarism-free assignments for the student.

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The ensemble methods in machine learning combine the insights obtained from multiple learning models to facilitate accurate and improved decision. In other way we can say, a group of models or classifier are called ensemble. 

How much for Assignment and homework help?

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Python Programming Sample 

Requirement Details

Sample 1:

Create a function that replaces all the vowels in a string with a specified character.

Example:

replace_vowels["the aardvark", "#"] ➞ "th# ##rdv#rk"

replace_vowels["minnie mouse", "?"] ➞ "m?nn?? m??s?"

replace_vowels["shakespeare", "*"] ➞ "sh*k*sp**r*"

​

Solution: ##Create a function that replaces all the vowels in a string with a specified character.
#inputs
str=input["enter a string : "]
char=input["enter the special characters to replace vowels in string"]
str_new=str
def replace[str_new,char]:
    c=0
    for z in str:
        if z=='A' or z=='a' or z=='E' or z=='e' or z=='I' or z=='i' or z=='O' or z=='o' or z=='U' or z=='u':
             str_new=str_new.replace[z,char]
             c=c+1
    if c>1:
        return[str_new]
    else:
        return['false']

ret=replace[str_new,char]
print["the original string : " , str]
if ret=='false':
    print["No vowels present in the given string "]
else:
    print["the new string : ",ret]

​

​

Sample 2:

Hamming distance is the number of characters that differ between two strings.

To illustrate:

String1: "abcbba"

String2: "abcbda"

Hamming Distance: 1 - "b" vs. "d" is the only difference.

Create a function that computes the hamming distance between two strings.

Examples

hamming_distance["abcde", "bcdef"] ➞ 5

hamming_distance["abcde", "abcde"] ➞ 0

hamming_distance["strong", "strung"] ➞ 1

​

Solution:

##a function that computes the hamming distance between two strings
#inputs
str1=input["enter string 1: "]
str2=input["enter string 2: "]

def hamming[str1,str2]:
    d=0
    for i in range[len[str1]]:
        if str1[i]!=str2[i]:
            d+=1
    return[d]
def check[str1,str2]:
      if len[str1]==len[str2]:
          print["the hamming distance for the given strings : ",hamming[str1,str2]]
      else:
          print["rerun and enter strings of same length"]
check[str1,str2]

​

​​

Sample 3:

Create a function that takes three arguments a, b, c and returns the sum of the numbers that are evenly divided by c from the range a, b inclusive.

Examples

evenly_divisible[1, 10, 20] ➞ 0

# No number between 1 and 10 can be evenly divided by 20.

evenly_divisible[1, 10, 2] ➞ 30

# 2 + 4 + 6 + 8 + 10 = 30

evenly_divisible[1, 10, 3] ➞ 18

# 3 + 6 + 9 = 18

​

Solution:

##Create a function that takes three arguments a, b, c and
## returns the sum of the numbers that are evenly divided by c from the range a, b inclusive.
#inputs
a=int[input["enter the lower limit"]]
b=int[input["enter the upper limit"]]
c=int[input["enter the divisor"]]
def sum_evendiv[a,b,c]:
    sum=0
    for i in range[a,b+1]:
        if i%c==0:
            sum+=i
    return[sum]

def check[a,b,c]:
    if c>b:
        print["run again and enter a value of 'c' less than 'b'"]
    else:
        print["the sum of the numbers evenly divided by c[{}] from the range a[{}],b[{}] inclusive".format[c,a,b],
              sum_evendiv[a,b,c]]
check[a,b,c]

​

Sample 4:

Write a program to compute the frequency of the words from the input. The output should output after sorting the key alphanumerically.

Suppose the following input is supplied to the program:

New to Python or choosing between Python 2 and Python 3? Read Python 2 or Python 3.

Then, the output should be:

2:2

3.:1

3?:1

New:1

Python:5

Read:1

and:1

between:1

choosing:1

or:2

to:1

​

Solution:

##a program to compute the frequency of the words from the input.
##The output should output after sorting the key alphanumerically
#input
##sorting them alphanumerically.
#inputs
str=input["enter a string :"]
word=str.split[]
print[word]
#sortingx
word_sorted=word
for i in range[len[word_sorted]]:
    for j in range[len[word_sorted]-1]:
        if word_sorted[j]>word_sorted[j+1]:
            word_sorted[j],word_sorted[j+1]=word_sorted[j+1],word_sorted[j]
print[word_sorted]
#removing duplicates
word_noduplicate=[]
for i in word_sorted:
    if i not in word_noduplicate:
            word_noduplicate.append[i]
#counting and output
def count[word_sorted,i]:
      return word_sorted.count[i]
for i in word_noduplicate:
      c=count[word_sorted,i]
      print[i,":",c]

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