Machine Learning Jobs

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Fixed-Price - Expert ($$$) - Est. Budget: $1,000 - Posted
We want to setup google tensor flow's "Show and Tell: A Neural Image Caption Generator", with OUR data. the steps of the setup descibed here (without the clustering): we currently have about 9 million images and captions. we want to be able to run the setup by ourselves and in relatively short time(one to a few days max).
Skills: Machine learning TensorFlow
Hourly - Entry Level ($) - Est. Time: Less than 1 month, 30+ hrs/week - Posted
We are looking for someone who has good expertise over predictionIO ( which is ML server. It is long term project, candidate having prior experience on predictionIO will be preferred. Please share your previous work while sending your cover letter
Skills: Machine learning
Fixed-Price - Intermediate ($$) - Est. Budget: $100 - Posted
Objective: Recommend user for software configuration with others user configurations. Data Set: Using IPython to present the data - Introduce data (with Visualization) - Methods (MLP, collaborative filtering, Random Forest, XGBoost, SVM, and two other methods you think will be useful) - Report which methods are good (with cross validation, etc.) - Any insight on how to provide good recommendation?
Skills: Machine learning Python
Fixed-Price - Expert ($$$) - Est. Budget: $200 - Posted
The main goal of this project is to finish Program 4. Lab 7 is the first part of program 4, implementation of the heap that allocates blocks from an area of memory allocated by a single call to malloc. Lab 8 is the second part, add to Lab 7 code to provide an implementation of the heap that includes a non-compacting garbage collector. Lab 7 specifications: Lab 8 specifications: Program 4 specifications: I have included Lab 7 code and public testing files. Your job is to continue working on Lab 8 and then Program 4. Please see the specifications very carefully and let me know if you can handle the project. We can renegotiate the price if needed. Just let me know.
Skills: Machine learning C
Hourly - Entry Level ($) - Est. Time: Less than 1 month, Less than 10 hrs/week - Posted
OVERVIEW We need an experienced computer engineer, data scientist or statistical analyst to help construct a natural language model for a set of semantic text and corresponding psychometric data. DETAILS We have a data set of individuals who have completed a) psychological profiles and b) prompted essays, and we would like to build a natural language dictionary / semantic text engine which would be capable of showing the types of words used by those who are high or low on certain scores in the psychological profiles. Without specific experience building a model such as this, we think we could base the process on that used to develop the engine of the World WellBeing Project ( They describe their process here: and here: DELIVERABLES Complete natural language dictionary for each psychometric measure of interest Complete report highlighting findings & word correlations APPLICATIONS Please do not waste our time with your favourite statistics formula, or the name of your employer or degree - all we want to know initially, in order to select people for interview stage, is answers to the following questions: - how you would go about building this model - the minimum number of records you would need to build it - how you would present the results - any further recommendations
Skills: Machine learning Analytics Data mining IBM SPSS
Fixed-Price - Expert ($$$) - Est. Budget: $1,999 - Posted
Looking for one person for a 2-month Proof of Concept: 1. Script (with me), a full image processing pipeline in AWS. Something similar to CloudCV. 2. You are a scripting guru, having used Vagrant, Docker, etc., Kubernetes, Fabric8 etc. etc. with great success. 3. You know how to collect logs, performance metric, etc. etc. You will be working remotely with me, not independently. Small deliverables for short test cycles. Fail often, fail early.
Skills: Machine learning Amazon EC2 Amazon MWS Amazon Relational Database Service
Hourly - Intermediate ($$) - Est. Time: More than 6 months, 10-30 hrs/week - Posted
I work at a self-paced, Python data science bootcamp. We are looking to expand our mentor capacity. Students go through pre-recorded lectures, assignments and complete projects. Depending on their desired pace, they select a track. The 12 month track has 1 mentor meeting a week, the 6 month track has 2 a week, and the 3 month track has 4 per week. You may have heard of Springboard ( We are similar to them, but we have a thorough 500-800 hour curriculum meant for already highly technical people to transition careers. A comparable company in the web development space is ( Compensation: $50 per hour Mentor Responsibilities - Video chat with students on a weekly basis via Skype or Google Hangouts - Help with any questions that may arise - Lead the job support prep (review resume, cover letter, github, linkedin, mock phone screens and practice technical interviews) Other Info - You can start off with 1 student, but easily take on more depending on your schedule - We will have a community slack channel with 2 full-time TA's if students have questions in-between mentor meetings - Each 45-min meeting will be paid as an hour to encompass any prep, follow-up or emailed questions in between
Skills: Machine learning Data Science Python
Fixed-Price - Expert ($$$) - Est. Budget: $5,000 - Posted
Looking for one person for a 2-month Proof of Concept: 1. Enhance OpenCV functionality in the cloud (particularly stitching class) with CUDA/OpenCL. Needs to stitch large canvases in record time. 2. I also need help with creating a fast image processing pipeline for other processing and signal processing. You will be working remotely with me, not independently. Small deliverables for short test cycles. Fail often, fail early.
Skills: Machine learning C++ Data Science Mathematics
Hourly - Expert ($$$) - Est. Time: More than 6 months, 30+ hrs/week - Posted
Online tutor for Machine Learning and Algorithms for Genomics course for almost blind bioinformatics graduate student needed Hallo I am a severely visually impaired student. My eyesight is not good enough to see the projector in class but all the class material is freely available online. To access the course material for this class, please follow the steps below: 1) Go to the course website, which is 2) Switch language to English by clicking on the fourth pull-down menu from the left (= most right pull down menu), which is shown with white font on dark blue background. It may be set to German since it is a German website. Then it would say at this pull down menu “Deutsch (de)’. Just click onto the white arrow to the right and select English (en). 3) The course name should read: “Machine Learning and Algorithms for Genomics” 4) Go to the bottom of the page it says Guest access 5) In the box, above which it says Guest access key, type the access key for this course, which is MLGenomics16 6) Press on the Submit button at the bottom of the page and you can see all the course materials. 7) If you are having trouble accessing the course material but would like to take a look at it in order to decide whether you’d like to teach it to me please contact me so that we can access the course website remotely together. In case you feel it is too complicated to access the course material below is the syllabus of what will be covered in this course: 1) Week 1: Introduction • Classification + background (probability, biology) 2) Week 2: Probabilistic, generative models • Weight matrices and other sequence motif models • Chomsky hierarchy and stochastic grammars: Markov chain models • Hidden Markov Models: parsing + applications (gene finding, protein domains: annotation of genomes & proteomes) 3) Week 3: Generative models (cont.) • HMMs: training + applications • Motif finding: overview & probabilistic approaches (EM & Gibbs sampling) • Enumerative algorithms 4) Week 4: Deep sequencing data • Data structures and read alignments • Peak calling, transcript annotation & gene expression 21 Syllabus 5) Week 5: From generative models to probabilistic networks • RNA structure prediction & RNA genes • Gene expression networks, hierarchical Bayes 6) Week 6: Discriminative approaches I • Support Vector Machines: theoretical foundations • SVMs: applications and sequence kernels • Random Forests 7) Week 7: Discriminative approaches II • Dimensionality reduction & unsupervised learning • Deep learning My math, statistics and programming knowledge is very limited since I am a molecular biologist by training. I just recently had to switch to bioinformatics because my vision had worsened to a point that I could no longer perform wet-lab work efficiently under time pressure. That is why I am looking for a very patient tutor, who is willing to take the time to teach me how to apply all the concepts taught in this course and who could potentially assist me in writing a proposal about how to apply machine learning techniques to make new discoveries I thank you very much in advance for considering taking your time to assist me in learning the skills and techniques needed for earning a PhD in bioinformatics despite me being almost blind. I am very much looking forward to discuss more details with you soon. Best regards Thomas
Skills: Machine learning Statistical Computing Statistics