Machine Learning Jobs

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Hourly - Expert ($$$) - Est. Time: 3 to 6 months, Less than 10 hrs/week - Posted
I am looking for a Technical blog writer who understands Business Intelligence and the difference between SAP Business Objects and Microsoft Reporting Services and the pros and cons between the two. If you also have knowledge of Machine leaning and data mining or Azure this would be an advantage . Also, you will need to be able to create visually pleasing workflow diagrams to be added to the blog, this will be an ongoing relationship. Please review the website www.bicg.com.au for more information about the company.
Skills: Machine learning Article Writing Blog Writing Business intelligence
Hourly - Intermediate ($$) - Est. Time: Less than 1 month, 10-30 hrs/week - Posted
My existing ML algorithms with the prerequisites: numpy,scikit-learn,matplotlib,biosppy need to be linked to a mongodb database. Currently, the ML algorithms are processing and use patterns from csv files. For this project I will provide: ML technical documentations, mongodb technical documentation and API technical documentation.
Skills: Machine learning AngularJS MongoDB NoSQL
Hourly - Entry Level ($) - Est. Time: Less than 1 week, 10-30 hrs/week - Posted
Looking for patterns in large spreadsheet analyzing our profit of our pay per click campaigns. Variables will include 1. 4 locations 2. keywords 3. days of the week (in general, as well as per keyword) 4. times of day (in general, as well as per keyword) 5. Actual profit generated 6. yes/no customer came into office 7. cost of ad-words campaign 8. method of communication phone or email Please indicate what process you will use as specifically as possible to gain insights into data (please do not only use buzzwords.... but describe so we can see you have an understanding of task)
Skills: Machine learning Microsoft Excel Pattern recognition Quantitative Analysis
Fixed-Price - Intermediate ($$) - Est. Budget: $50 - Posted
Hi, I am looking at creating a simple UBCF recommendation system using R. I will be providing dummy data for you to work with which is a good representative of the internal data we have. The data consists of user id, business id and star ratings. You will need to work in steps so we can review and make modifications if need be before we proceed to the next step. We are not looking at optimizing the model above at this stage, that will be a follow up project after we are done with the first 5 steps. We do not want to use recommenderLab in R for this project, just use simple functions. We are currently wanting to work with you to go through Step 1 - 5 of the project. We will request you to send us your code at the end of each step which we will validate before you proceed to next steps. Step 1 - Convert the provided large training data set to a sparse matrix which can then be used to create a similarity matrix (method = cosine) for the users. Step 2 - Write a cosine similarity function in R to create a user to user similarity matrix using the sparse training matrix Step 3 - Using the sparse training matrix, for each user find the topN (n=50) nearest neighbors and calculate average rating for each business the user has not rated Step 4 - The model created with the training matrix should now applied to the test data and an average rating be predicted for each user for the businesses they have rated Step 5 - Calculate and report the RMSE between the predicted values and the actual values in the test data set
Skills: Machine learning Data mining Data Science Predictive Analytics
Hourly - Intermediate ($$) - Est. Time: Less than 1 month, Less than 10 hrs/week - Posted
CANCER 3D Modeling from 2D DICOM Images ==================================== I am a student in a university with a health background and some basic information in Python. I am working in a cancer visualization and extraction project and looking for an experienced software programmer using python and have experience in medical imaging as DICOM images (digital imaging and communication in medicine).This project continues for one month and through it I want the programmer to make 3D reconstruction from 2D DICOM images. The program also makes extraction and segmentation of the tumor tissue from the background and segmentation of other tissues. Then it renders the 3D model of the extracted tumor. We need to use different kinds of algorithms as marching cube algorithm including interpolation and approximation for 3D reconstruction. Developer may use segmentation algorithms as K-means, region growing, threshold and the likes. I already started the work and I have online resources for DICOM images, I performed some scripts related to this work and downloaded some open-source of DICOM images software processing images as 3D slicer and imageJ. All of these I will provide the programmer to start his/her work. Program Requirements A program is needed to be written in Python. It will do the following: 1. Read sequence of DICOM .DCM images (2D) and render them in sequence and the sequence can be controlled with the mouse back and forth. (Already completed) 2. Segmenting and analyzing the 2D DICOM images and separate areas that are likely to be cancer (We will give the programmer some algorithms recommendations as well as the density that are likely to be cancer), separate cancer areas in new sequence of DICOM images, example: a. Original DICOM images (1.dcm, 2.dcm, 3.dcm, 4.dcm, 5.dcm, 6.dcm, 7.dcm, 8.dcm, 9.dcm, 10.dcm, 11.dcm, 12.dcm). b. The DICOM that contains cancer would be: (3.dcm, 4.dcm, 5.dcm, 11.dcm, 12.dcm). c. The following requirement in number (3), it will build the 3D model from the cancer DICOM images in (b) above. 3. Then build a 3D construction from the DICOM sequence. The 3D model should be controlled with degrees of depth (From skin to the deepest structure). (Completed partly) Figure 1: (2D DICOM image) Shows cancer in blue circle, the cancer can be analyzed according to several algorithms, some of them consider the brightness, some the circular shape, and some the density. The developer can use any algorithm that sees fits, to determine the cancer area in DICOM images.
Skills: Machine learning 3D Rendering Image Editing Image Processing
Hourly - Expert ($$$) - Est. Time: 3 to 6 months, Less than 10 hrs/week - Posted
We are in the plant business and would like to offer an optimized platform for other plant businesses online and offline. The goal is to collect data to create data sets and feed them into machine learning utilities. We need a solution that does the following Hadoop Ecosystem - create Hadoop environment to create a data engine to collect and distribute data sets - Collects data from consumer purchases/activity and online through API's and offline (a swipe card or bar code on app to assign user ID's ) - machine learning Vendor Applications The development of user applications that provide analytics to clients. The following technologies will be developed to support clients needs: Data Reporting Statistical Analysis Interactive Web Applications Enterprise Software The main goal is to develop a Big Data as a service solution for the Plant Industry Please give an overview of the solution - this will be a long-term development
Skills: Machine learning Apache Hive Big Data Cloudera