Caleb Chima

Data Scientist /ML Engr. @nouvells_

[Caleb Chima @_C4LEB]

Just completed a 3-month contract on @Upwork as a #DataScientist! 📊💻
Data mining, advanced #Python analysis, ETL, & database management. Grateful for 5-star feedback & ready for new challenges!

https://t.co/RY3xIDgIIx

[Caleb Chima @_C4LEB]

I just made a video explaining precision, recall, f1-score with the best analogy you can possibly find:

https://t.co/CIFlcJR5Ea

[Caleb Chima @_C4LEB]

I just uploaded a video explaining confusion matrix in machine learning with basic analogies .

https://t.co/M3FOCD87Mg

[Caleb Chima @_C4LEB]

I just made a video explaining Parameterss and hyper-parameters in machine learning using basic analogy. #data #ml #machinelearning

https://t.co/u0vtLLoOcB

[Caleb Chima @_C4LEB]

Just made a video explaining Cross Validation in ML with basic concepts #ml #machinelearning #data

https://t.co/PFyZVgTjYm

[Caleb Chima @_C4LEB]

New video explaining difference between cost and lost function in ml

https://t.co/ksUP2FzuUC

[Caleb Chima @_C4LEB]

Los function explained using basic analogy.

https://t.co/TFDGaFyCPL

[Caleb Chima @_C4LEB]

Overfitting explained using basic analogy
1) Overfit = great on training, terrible on new data. 2) Caused by too-complex models or too-little data. 3) Fix: more data, simpler model, regularization. 4) Always check train vs test gap.

https://t.co/u8KJPzpNTQ

[Caleb Chima @_C4LEB]

https://t.co/EAopP4IUQr

[Caleb Chima @_C4LEB]

I just created a video explaining what it means to train a Machine Learning Model. Here

https://t.co/YcCxtHKcsk

[Caleb Chima @_C4LEB]

I just created a new video explaining what machine learning model is using basic concepts/analogies

https://t.co/6ySeBzH6VR

[Caleb Chima @_C4LEB]

Checkout the pt-2: Three types of ML explained using basic analogy .
https://t.co/EvvEGqnJB9

[Caleb Chima @_C4LEB]

https://t.co/webu2LkWC0

[Caleb Chima @_C4LEB]

Three types of Machine Learning explained using the most basic/common analogy:

https://t.co/webu2LkWC0

[Caleb Chima @_C4LEB]

Machine learning explained using its most widely used aspect (Recommendation system):

https://t.co/sjMarMTyEo

[Caleb Chima @_C4LEB]

Subscribe on YouTube here :

https://t.co/fH17gBoif0

[Caleb Chima @_C4LEB]

I help tech professionals find their path without the overwhelm

#techprofessionals

[Caleb Chima @_C4LEB]

Grateful to everyone who joined. Let’s keep pushing what’s possible with clean data and smart systems.

[Caleb Chima @_C4LEB]

Last month, I had the pleasure of speaking(hands-on) at a session where I broke down how teams can use dbt (Data Build Tool) to turn raw, messy data into clean, AI-ready pipelines.

I shared a real-world workflow, showcasing how

[Caleb Chima @_C4LEB]

and connect it all to ML workflows. Whether you’re building dashboards or training models, your data has to be right. This session was all about making that happen—and I appreciated the opportunity to share practical insights from my work as a Data Scientist & ML Engineer.

[Caleb Chima @_C4LEB]

dbt integrates into a Docker-based orchestration layer—transforming raw Snowflake data, embedding business logic, and preparing it for downstream machine learning systems and analytics platforms. We explored how to clean and structure data with dbt, design testable data models,