Hire the best
Machine Code Programmers

Top 27 Machine Code Programmers on 18 Sep 2019 on Toogit. Machine Code Programmers on Toogit are highly skilled and talented. Hiring Machine Code Programmers on Toogit is quite affordable as compared to a full-time employee and you can save upto 50% in business cost by hiring Machine Code Programmers on Toogit. Hiring Machine Code Programmers on Toogit is 100% safe as the money is released to the Freelancer only after you are 100% satisfied with the work.

Get Started

Explore Toogit’s top Machine Code Programmers

 
 
 
Mashal A.Mechatronic & Control Engineer, Pakistan
$7 /hr
3 Years Exp.
0 Followers
I am a graduate Engineer from a well known university, having 4 years of experience in teaching field. I am good in communication & have good Engl...Read More
Ashish I.Computer Vision and Deep Learning Expert, India
$19 /hr
7 Years Exp.
0 Followers
I am a Computer Vision and Deep Learning expert with 7 years experience. 1. Research experience in the field of Image Processing, Machine Learning...Read More
Chaitnaya K.Machine Learning Engineer (Intern), India
$24 /hr
0 Years Exp.
0 Followers
I am currently doing my IBM certification in Machine Learning Using Python
Sunil Database Admin and Machine Learning Expert, India
$20 /hr
17 Years Exp.
0 Followers
I have been a certified database admin with more than 17 years of experience. It includes various RDBMS like Oracle, Microsoft Sql Server, My Sql and...Read More
Jehoshaphat I. Abu Developer/Designer, Nigeria
$55 /hr
2 Years Exp.
0 Followers
I'm a developer and designer. As a developer, I help startups and seasoned businesses create attention-grabbing VR/AR/MR experiences to delig...Read More
Manish n nn nn nn nnnn, India
$2 /hr
0 Years Exp.
0 Followers
j j jjjj j j
Noureddine Engineerman, Morocco
$5 /hr
0 Years Exp.
0 Followers
I am full stack developper and linux administration also worked on some mini projects before as web app that app is about an ERP that help a company t...Read More
Mayank Data Scientist/ ML Engineer, India
$20 /hr
0 Years Exp.
0 Followers
Passionate to explore and establish myself in the field of ML. I have a strong hold on ML algorithms' understanding and have been implementing th...Read More
Vishal N.Data Scraper, India
$8 /hr
0 Years Exp.
0 Followers
I have previously worked in this field and want to learn more in this field as this field is growing more.I have worked on a project in which I was to...Read More
Sheenam K.Machine Learning Enthusiast. , India
$34 /hr
2 Years Exp.
0 Followers
I am a certified Python developer. I am a machine learner enthusiast. I know Python, Tensorflow, OpenCV, Python libraries which are used for numerical...Read More
Karthikeyan Signal processing engineer, India
$4 /hr
12 Years Exp.
0 Followers
I'm a signal processing engineer and consultant with 12 years of experience.
Mubashar Nazar A.Data Scientist, Pakistan
$15 /hr
2 Years Exp.
0 Followers
Data Scientist with hands-on experience in Python, Data Analytics, Data visualization, and Prediction models. I had an experience of almost 2 years.
Rishabh R.Data Scientist and Analyst, India
$7 /hr
1 Years Exp.
0 Followers
I am a certified Data Scientist and Analyst having 1 year of experience in data labeling work. I am also a certified Python developer and I also have...Read More
I had 4+ years of industrial experience in computer vision, deep learning and machine learning.
Bindu Software engineer, India
$17 /hr
5 Years Exp.
0 Followers
c, c++, javascript, deep learning, computer vision, machine learning, data annotation, data entry
Muhammad M.Data Entry Operator, Pakistan
$4 /hr
0 Years Exp.
0 Followers
I am a professional Data Entry Operator Working Experience 3 years
Nidhi Priya S.AI Engineer, India
$4 /hr
2 Years Exp.
0 Followers
I have experience of 2 years in AI as an AI engineer. I have experience in ocr, object detection ,RNN,LSTM,ANN,CNN.
Sajan Gohil Machine learning engineer, India
$4 /hr
1 Years Exp.
0 Followers
I am currently pursuing a bachelors degree. I am experienced in AI and machine learning field and a computer vision enthusiast. I have worked on multi...Read More
Rupesh K.Software Engineer, India
$14 /hr
0 Years Exp.
0 Followers
I'm a software engineer looking for some remote work. I am good at Data Analyzing and find patterns in them so that we can build a business model...Read More
Emwinghare K.Data Scientist , Nigeria
$10 /hr
1 Years Exp.
0 Followers
I am a Data scientist with 1 year experience at building ml solution for fintech. I work presently at Carbon, Africa's leading digital loan lendi...Read More
Tapas Kumar D.Software Developer, India
$30 /hr
4 Years Exp.
0 Followers
I am a software developer with 4+ years of experience.
Sharon O.Statistical programmer, Kenya
$10 /hr
2 Years Exp.
0 Followers
Experienced Statistical Programmer with a demonstrated history of working in the pharmaceuticals industry. Skilled in Microsoft Excel, Data Analysis,...Read More
Sachin S Freelance Developer & Consultant, India
$60 /hr
2 Years Exp.
0 Followers
A Software developer with 2.5 years of experience in Automobile, Health care domains. I use Machine learning and deep learning to solve industry probl...Read More
Sandeep Data Scientist, India
$10 /hr
4 Years Exp.
0 Followers
I am a Data Scientist with 4 Years of Experience
Glenn D.Sales Data and Intelligence Analyst, Philippines
$20 /hr
2 Years Exp.
0 Followers
I'm an experience Data Analyst and leverage Python for my projects and analysis. I've a formal training (bootcamp) on Data Science and studi...Read More
To view more profile join Toogit

Get Started
 

How it works

Post a job

Post a Job

List your project requirement with us. Anything you want to get developed or want to add to your business. Toogit connects you to Top freelancers around the world.

Hire

Hire

Invite and interview your preferred talent to get work done. Toogit Instant Connect helps you if you need your project started immediately.

Work

Work

Define Tasks, use Toogit's powerful project management tool, stay updated with real time activity logs

Payment

Pay

Review work, track working hours. Pay freelancers only if you are 100% satisfied with the work done.

Reviews From Our Users

Articles Related To Machine Code


Python is one of the fastest growing programming languages. It has undergone more than 28 years of the successful span. Python itself reveals its success story and a promising future ahead. Python programming language is presently being used by a number of high traffic websites including Google, Yahoo Groups, Yahoo Maps, Shopzilla, Web Therapy, Facebook, NASA, Nokia, IBM, SGI Inc, Quora, Dropbox, Instagram and Youtube. Similarly, Python also discovers a countless use for creating gaming, financial, scientific and instructive applications.

 

Python is a fast, flexible, and powerful programing language that's freely available and used in many application domains. Python is known for its clear syntax, concise code, fast process, and cross-platform compatibility.

 

Python is considered to be in the first place in the list of all AI and machine learning development languages due to the simplicity. The syntaxes belonging to python are terribly easy and can be easily learn. Therefore, several AI algorithms will be easily implemented in it. Python takes short development time as compared to different languages like Java, C++ or Ruby. Python supports object oriented, functional as well as procedure oriented styles of programming. There are lots of libraries in python that make our tasks easier.

 

Some technologies relying on python:

Python has become the core language as far as the success of following technologies is concerned. Let’s dive into the technologies which use python as a core element for research, production and further developments.

 

  1. Networking: Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.
  2. Big Data: The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.
  3. Artificial Intelligence (AI): There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes. It is only the Artificial Intelligence that has made it possible to develop speech recognition system, interpreting data like images, videos etc.

 

Why Choose Python for Artificial Intelligence and Machine Learning?

Whether a startup or associate MNC, Python provides a large list of benefits to all. The usage of Python is specified it cannot be restricted to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complicated processes like artificial intelligence (AI), Machine Learning (ML), natural language process, data science etc. The question is why Python is gaining such momentum in AI? And therefore the answer lies below:

 

Flexibility: Flexibility is one of the core advantages of Python. With the option to choose between OOPs approach and scripting, Python is suitable for every purpose. It works as a perfect backend and it also suitable for linking different data structures together.

 

Platform agnostic: Python provides developer with the flexibility to provide an API from the existing programming language. Python is also platform independent, with just minor changes in the source codes, you can get your project or application up and running on different operating systems.

 

Support: Python is a completely open source with a great community. There is a host of resources available which can get any developer up to speed in no time. Not to forget, there is a huge community of active coders willing to help programmers in every stage of developing cycle.

 

Prebuilt Libraries: Python has a lot of libraries for every need of your AI project. Few names include Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning.

 

Less Code: Python provides ease of testing - one of the best among competitors. Python helps in easy writing and execution of codes. Python can implement the same logic with as much as 1/5th code as compared to other OOPs languages.

 

Applications of Python:

There are so many applications of Python in the real world. But over time we’ve seen that there are three main applications for Python

Web Development: Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

Data Science (including Machine Learning): Machine Learning with Python has made it possible to recognize images, videos, speech recognition and much more.

Data Analysis/Visualization: Python is also better for data manipulation and repeated tasks. Python helps in the analysis of a large amount of data through its high-performance libraries and tools. One of the most popular Python libraries for the data visualization is Matplotlib.

 

NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient manner. By utilizing NLP and its parts, one can organize the massive chunks of text information, perform various automated tasks and solve a wide range of issues like – automatic summarization, machine translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation etc.

 

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to lexical resources like WordNet, along with a collection of text processing libraries for classification, tokenization, stemming, and tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

 

NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

 

Downloading and installing NLTK

  1. Install NLTK: run pip install nltk
  2. Test installation: run python then type import nltk and run nltk.download() and download all packages.

 

Pre-Processing with NLTK

The main issue with text data is that it's all in text format. However, the Machine learning algorithms need some variety of numerical feature vector so as to perform the task. Thus before we have a tendency to begin with any NLP project we'd like to pre-process it to form it ideal for working. Basic text pre-processing includes:

 

  • Converting the whole text into uppercase or lowercase, in order that the algorithm doesn't treat the same words completely different in several cases.
  • Tokenization: Process of converting the normal text strings into a list of tokens i.e. words that we actually want. The NLTK data package includes a pre-trained Punkt tokenizer for English.

 

           import nltk

           from nltk.tokenize import word_tokenize

           text = "God is Great! I won a lottery."

           print(word_tokenize(text))

           Output: ['God', 'is', 'Great', '!', 'I', 'won', 'a', 'lottery', '.']

 

  • Noise removal: Process of removing everything that isn’t in a standard number or letter.
  • Stop word removal: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”). We would not want these words or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to be stop words. NLTK (Natural Language Toolkit) in python has a list of stopwords stored in sixteen different languages. You can find them in the nltk_data directory.  home/Saad/nltk_data/corpora/stopwords is the directory address.

           import nltk

           from nltk.corpus import stopwords

           set(stopwords.words('english'))

 

  • Stemming: Stemming is the process of reducing the words to its root form. Example if we were to stem the following words: “Connects”, “Connecting”, “Connected”, “and Connection”, the result would be a single word “Connect”.

           # import these modules

           from nltk.stem import PorterStemmer

           from nltk.tokenize import word_tokenize   

           ps = PorterStemmer()  

           # choose some words to be stemmed

           words = ["Connect", "Connects", “Connected”, "Connecting", "Connection", "Connections"]

 

           for w in words:

           print(w, " : ", ps.stem(w)) 

 

  • Lemmatization: Lemmatization is the process of grouping along the various inflected forms of a word in order that they may be analyzed as a single item. Lemmatization is similar to stemming but it brings context to the words. Therefore it links words with similar meaning to one word.

           # import these modules

           from nltk.stem import WordNetLemmatizer  

           lemmatizer = WordNetLemmatizer()  

           print("rocks :", lemmatizer.lemmatize("rocks"))

           print("corpora :", lemmatizer.lemmatize("corpora"))  

           # a denotes adjective in "pos"

          print("better :", lemmatizer.lemmatize("better", pos ="a"))

 

          -> rocks : rock

          -> corpora : corpus

          -> better : good

 

Now we need to transform text into a meaningful vector array. This vector array is a representation of text that describes the occurrence of words within a document. For example, if our dictionary contains the words {Learning, is, the, not, great}, and we want to vectorize the text “Learning is great”, we would have the following vector: (1, 1, 0, 0, 1). A problem is that extremely frequent words begin to dominate within the document (e.g. larger score), however might not contain as much informational content. Also, it will offer additional weight to longer documents than shorter documents.

 

One approach is to rescale the frequency of words or the scores for frequent words called Term Frequency-Inverse Document Frequency.

 

  • Term Frequency: is a scoring of the frequency of the word in the current document.

           TF = (Number of times term t appears in a document)/ (Number of terms in the document)

 

  • Inverse Document Frequency: It is a scoring of how rare the word is across documents.

           IDF = 1+log(N/n), where, N is the number of documents and n is the number of documents a term t has appeared in.

 

           Tf-idf weight is a weight often used in information retrieval and text mining.

           Tf-IDF can be implemented in scikit learn as:

 

           from sklearn.feature_extraction.text import TfidfVectorizer

           corpus = [

           ...     'This is the first document.’

           ...     'This document is the second document.’

           ...     'And this is the third one.’

           ...     'Is this the first document?',]

           >>> vectorizer = TfidfVectorizer()

           >>> X = vectorizer.fit_transform(corpus)

           >>> print(vectorizer.get_feature_names())

           ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']

           >>> print(X.shape)

           (4, 9)

 

  • Cosine similarity: TF-IDF is a transformation applied to texts to get two real-valued vectors in vector space. We can then obtain the Cosine similarity of any pair of vectors by taking their dot product and dividing that by the product of their norms. That yields the cosine of the angle between the vectors. Cosine similarity is a measure of similarity between two non-zero vectors.

           Cosine Similarity (d1, d2) =  Dot product(d1, d2) / ||d1|| * ||d2||

 

          import numpy as np

          from sklearn.metrics.pairwise import cosine_similarity

          # vectors

          a = np.array([1,2,3])

          b = np.array([1,1,4])

          # manually compute cosine similarity

          dot = np.dot(a, b)

          norma = np.linalg.norm(a)

          normb = np.linalg.norm(b)

          cos = dot / (norma * normb)

 

After completion of cosine similarity matric we perform algorithmic operation on it for Document similarity calculation, sentiment analysis, topic segmentation etc.

 

I have done my best to make the article simple and interesting for you, hope you found it useful and interesting too.

Welcome to Python programming world! We presume you are trying to find information concerning why and how to get started with Python. Fortunately, an experienced coder in any programing language (whatever it's going to be) will pick up Python very quickly. It is also easy for beginners to learn and use.

 

Why you should learn Python

Python is one of the most popular general-purpose programming languages used for both large and small-scale applications. With Python, you can discover how to bridge web development and data analytics. Python’s widespread adoption is due to its large standard library, easy readability, and support of multiple paradigms such as functional, procedural and object-oriented programming styles. Python modules interact with a variety of databases, making it an excellent choice for large-scale data analysis. The Python programming language is often the best choice for introductory courses in data science and machine learning. If you've been wondering how to learn python online to advance your career, you've come to the right destination.

 

A popular Python slogan “life is happier without braces”.

 

Install Python

Installing Python is generally easy, and today several Linux and UNIX system distributions include a recent Python. Even some Windows computers currently go along with Python already installed. If you do need to install Python download from Python official website.

 

Learning Python

Before getting started, you may want to find out which IDEs and text editor are best, IDE usually has plenty of useful features such as autocomplete, debugger and refactoring tools. Some will even check your Python code for little mistakes and encourage best practices through warnings. IDE will help you to find bugs and develop code faster. Learn basics of Python programming and syntax from online Python tutorials.

 

What you need to learn

  1. Python Syntax
  2. String and Console output
  3. Conditionals and control flow
  4. Function
  5. List and Dictionaries
  6. Loops and array
  7. Classes
  8. File input and output
  9. Advanced topic in python

 

Here are some tips to help you make the new concepts you are learning as a beginner programmer:

  1. Code Everyday: Consistency is very important when you are learning a new language. We recommend making a commitment to code every day.
  2. Write it out: As you progress on your journey as a new programmer, you may wonder if you should be taking notes. This will be especially beneficial for those working towards the goal of becoming a full-time developer, as many interviews will involve writing code on a whiteboard.
  3. Go Interactive: Whether you are learning about basic Python data structures (strings, lists, dictionaries, etc.) for the first time, or you are debugging an application, the interactive Python shell will be one of your best learning tools.
  4. Become bug hunter: Once you begin writing complicated programs that you just can run into bugs in your code. It happens to all or any of us! Don’t let bugs frustrate you. Instead, embrace these moments proudly and consider yourself as a bug bounty hunter.
  5. Surround yourself with others: It is extremely important when you are learning to code in Python that you simply surround yourself with others who are learning additionally. This may allow you to share the information and tricks you learn on the approach.
  6. Teach: It is said that the most effective way to learn something is to teach it. This is often true once you are learning Python. There are many ways to try to do this: white boarding with other Python lovers, writing blog posts explaining recently learned ideas, recording videos during which you explain something you learned, or simply talking to yourself at your computer.
  7. Pair program: Pair programming is a technique that involves two developers working to complete a task. The two developers switch between them. One developer writes the code, while other helps guide the problem solving and reviews the code as it is written. Switch frequently to get the benefit of both sides.
  8. Build something: For beginners, there are many small exercises that will really help you become confident with Python.

Articles Related To Machine Code


Choose Python Language for Bright Future
Choose Python Language for Bright Future
Other - Software Development

Python is one of the fastest growing programming languages. It has undergone more than 28 years of the successful span. Python itself reveals its success story and a promising futu...

Read More
Natural Language Processing in Python
Natural Language Processing in Python
Web Development

NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient mann...

Read More
Python Scripting – Learn and Earn
Python Scripting – Learn and Earn
Scripts & Utilities

Welcome to Python programming world! We presume you are trying to find information concerning why and how to get started with Python. Fortunately, an experienced coder in any progr...

Read More

What our users are discussing about Machine Code