How I Established Myself as a Machine Studying Researcher

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Lukman, what’s your background?

I’m a Nigerian and a doctoral researcher at LARIS Laboratory, Polytech Angers, College of Angers, France. I specialise in utilizing machine studying strategies for the automated recognition of practical mind networks utilizing rs-fMRI photos. 

I take pleasure in cross-disciplinary collaborative tasks and exhibit readiness to tackle difficult issues by way of medical imaging, graph idea, predictive modeling, and scalable algorithms. 

My expertise at Omdena has been extremely fulfilling throughout a number of roles, together with  

  • Lead Machine Studying Engineer
  • Venture Supervisor
  • Neighborhood Supervisor at Omdena-Paris Chapter

I’ve had the chance to collaborate with a implausible workforce of AI specialists from around the globe on sensible and revolutionary AI tasks to supply lasting options to a spread of on a regular basis issues.

Lukman Enegi Ismaila

Lukman Enegi Ismaila

Which DataCamp course(s) did you take part in? 

I’ve accomplished the course Biomedical Picture Evaluation in Python.

Particularly, it gave me confidence when tackling complicated issues in medical imaging.

Fig.1 Biomedical Image Analysis in Python

Fig.1. Biomedical Picture Evaluation in Python. Supply DataCamp

How did collaborating in Omdena tasks allow you to in your profession? Which tasks have been most necessary to you? Why?

Engaged on collaborative tasks at Omdena has uncovered and impressed me to resolve more difficult duties efficiently. It additionally has improved my AI mission administration expertise to successfully handle a various workforce of machine studying engineers.

Some tasks I took half in:

1. Enhancing Meals Safety and Crop Yield in Senegal

This problem was initiated to assist farmers know the place so as to add water or fertilizer utilizing knowledge similar to soil PH, temperature, and moisture ranges, mixed with different knowledge sources. The information and predictions revealed the place to take a position and helped strengthen the understanding of crop losses whereas maximizing revenues and minimizing losses.

Crop yield prediction – Source: Omdena

Crop yield prediction – Supply: Omdena

Within the span of two months, Omdena’s collaborators have been in a position to implement a Deep Studying mannequin that predicts crop yield in Senegal.

2. Detecting the Violence Between Elders and Caregivers Utilizing Laptop Imaginative and prescient

The AI for Kids Violence mission by Omdena utilized varied knowledge sources to coach and take a look at their AI fashions. The sources included public datasets such because the Baby Imaginative and prescient and Abuse Dataset and the UNICEF Violence Towards Kids Survey Dataset. Moreover, the workforce collected their very own dataset by manually annotating movies from YouTube and Vimeo. The workforce additionally used switch studying strategies to leverage pre-trained fashions and additional enhance their mannequin’s efficiency.

3. Credit score Scoring for Making Meals Reasonably priced to the Hundreds of thousands of Underserved in Africa

The Credit score Scoring in Africa mission by Omdena utilized varied AI applied sciences to supply credit-scoring options to the unbanked and underbanked populations in Africa. The workforce employed machine studying algorithms, pure language processing (NLP) strategies, and deep studying fashions to foretell the creditworthiness of people primarily based on non-traditional knowledge sources similar to cell phone utilization, social media exercise, and utility invoice funds. The mission concerned a workforce of over 70 knowledge scientists, engineers, and specialists from around the globe. Our fashions achieved excessive accuracy charges in predicting the creditworthiness of people primarily based on non-traditional knowledge sources.

How has Omdena modified your worldview?

Earlier than my expertise with Omdena, I had little confidence in my expertise and talent with machine studying. Quick ahead to now, I’ve gained confidence and improved my collaborative management expertise by way of a number of machine studying tasks at Omdena.

Throughout my first Omdena mission as a Junior Machine Studying Engineer, I used to be promoted mid-project primarily based on my efficiency and contribution. This promotion allowed me to finish the mission as a Machine Studying Engineer.

What was probably the most troublesome a part of your job? How did you overcome obstacles?

At first, I felt uneasy to leap into machine studying issues from real-world eventualities as a result of it usually requires a well-thought-out course of and the prospect of success might be slim. 

Nevertheless, as time handed I benefited from the recommendation of different AI specialists from the Omdena group. I used to be in a position to tackle difficult issues and drive them to success throughout a number of tasks similar to:

How did it assist your profession? Did you get a job, internship, or every other accomplishment you need to share?

As a doctoral researcher in machine studying, my primary aim was to familiarize myself with addressing complicated issues on this area. I used to be in a position to perceive the required steps concerned in addressing real-life issues and proceed to take pleasure in constructive suggestions as a mission supervisor and Omdena-Paris Neighborhood Lead. 

Moreover, I used to be not too long ago awarded a Datasphere Analysis Fellowship by the Datasphere Institute. I regard this as sturdy proof that I’ve improved my self-confidence in making use of my expertise for real-life problem-solving.

Would you want so as to add one thing extra?

I firmly imagine that the Omdena group has positively impacted my private progress and I hope to proceed on the projected trajectory in direction of a groundbreaking affect in AI for good. 

The publish How I Established Myself as a Machine Studying Researcher appeared first on Omdena | Constructing AI Options for Actual-World Issues.

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