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No more wondering why applications vanish into the void.
Try it: https://t.co/co31Ie9IRM
Works well for Tech jobs only for now.
I am working on a system that helps job applicants know whether a role is worth applying for before wasting time.
No more applying based on gut feeling, this helps you decide apply or skip with confidence.
Still in progress. You can look forward to the final product.
Chapters 1 & 2 in 12 minutes.
I built MATRA: an AI assistant that guides final-year students from
βIβm stuckβ to a structured, submission-ready project.
Not a demo. Itβs live.
This is an MVP.
Try it. Break it. Tell me whatβs missing.
https://t.co/3sXj5WWQU9
#AI#EdTech
Just launched "Kenney Studio π¨" : an AI art app I built that turns photos into realistic pencil sketches βοΈ
Made with Stable Diffusion + ControlNet, hosted on Hugging Face.
See it in action π
https://t.co/liycND42E7
#AI#MachineLearning#ControlNet#Streamlit#KenneyStudio
Hey everyone, π my latest project "Chase BTC"!
Chase BTC is a live system that predicts Bitcoin movements & delivers signals on a web app + Telegram bot π
π Try the app: https://t.co/ONG1Cs7CkH
π GitHub repo: https://t.co/bLBdGVKp3s
#ML#Bitcoin#Telegrambot#DataScience
This is my MVP version; I plan to upgrade with deep learning (Keras/TensorFlow) for smarter feedback once I finish my DL roadmap.
Big thanks to everyone inspiring my learning journey.
Feedback is welcome π
#AI#SpeechRecognition#Streamlit#DeepLearning
I am excited to share my latest project: "Hey Rodea": An AI Speech Coach & Transcriber.
πTry it live: https://t.co/mNejESWIb6
Upload or record your speech β get transcript + scores (Clarity, Confidence, Engagement) + feedback
#AI#SpeechTech#DataScience
But resilience was key I kept iterating, and here we are.
For v2.0, Iβm planning to expand the dataset (500β1000 images per class) and push performance to the next level, I hope you are as excited as I am to see it.
I recently deployed my first Deep Learning project: "Little Botanist"
A Plant Classifier & Explorer built with TensorFlow (CNN)
Trained on 100 classes, optimized to run smoothly on CPU, and deployed live.
Try it here: https://t.co/TcXi1Szhb4
#DeepLearning#ML#AI
And I tackled the underfitting by tuning augmentations & model strategy.
- Dataset size (10k images / 100 classes) resulted in limited accuracy (~60%)
- I had to train overnight, created multiple Colab accounts and son, this project really taught me a lot.
This is just v1.0, and I faced quite some challenges during the development:
- Training on CPU = 7+ hours per session
- Colab usage limits forced me to get creative
- Overfitting (though, solved with data augmentation (Mixup, AugMix, CutMix) which resulted in Underfitting
I also built a predictive data mining model for clinical data.
From diabetes prediction to explainable association rules, the model identifies high-risk profiles & supports smarter healthcare decisions.
I rounded it by deploying via Streamlit for easy use.
Over the past few weeks, Iβve had the privilege of completing several data analysis and machine learning jobs: from supporting undergraduate and postgraduate research to practical business studies.
Iβll be sharing highlights from some of them here. Stay tuned!
#DataAnalytics
I explored the impact of IoT on SMEs in Osogbo, and the analysis showed that IoT is transforming SMEs in Osogbo.
Findings:
72% adoption β better efficiency, growth & profitability
Challenges: funding, skills, awareness.
Repo: https://t.co/vlEUxECxO2
#IoT#DataAnalysis#SMEs
I just completed an exciting unsupervised learning project!
I used K-Means Clustering and t-SNE to group 20 popular Nigerian foods based on their nutritional content.
Project: https://t.co/jmdgDScuhO
#MachineLearning#UnsupervisedLearning#DataScience#Python#GitHub
Iβve recently completed two real-world data analysis projects, helping both undergraduate and postgraduate students make sense of their academic data.
I Explored how age at admission affects academic performance
Just built an Accent Classifier that predicts the native language (Hausa, Igbo, Yoruba) of a speaker from their English accent, using only traditional machine learning (KNN)!
I manually gathered the audio clips from YouTube and built the classifier from scratch.