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brian-kipkoech-tanui/README.md

Hi 👋🏼, I am Brian Kipkoech!

Welcome to my GitHub profile 🙏🏼

About Me:

Machine Learning Engineer | Data Scientist

I create algorithms and models that help computers learn from data and make forecasts, resulting in valuable business insights.

Profile Summary

brian-kipkoech-tanui

I am a passionate data scientist and machine learning expert who is inspired by technology's capacity to resolve complex issues. I have experience in a variety of fields, including statistics, AI, computer vision, deep learning, mathematics, and NLP, and I'm always learning new things in these fields. My strategy is focused on producing high-quality outcomes while also valuing lifelong learning and personal growth.

In addition to my expertise in machine learning and data science, I have also worked with DevOps and AWS (Amazon Web Services). I have worked on using AWS's services like SageMaker, EC2, RDS, S3, Step Functions, and Lambda to implement machine learning models and create scalable and reliable systems. I recognize the value of having a strong DevOps culture in machine learning and data science, and I work hard to incorporate best practices into my workflows to guarantee efficient and effective machine learning model development, testing, and deployment.

I am dedicated to making an impact, with a focus on data science and machine learning. My portfolio, featuring my work and contributions, can be viewed on GitHub (here, under repositories). I am appreciative of chances to advance professionally, demonstrate my knowledge, and demonstrate my dedication to leaving a lasting impression. If you share my vision and wish to collaborate, feel free to reach out at 📫 briankipkoechtanui@gmail.com

Professional Links:

LinkedIn GitHub

Domains of Interests & Expertise

☄️ Statistics
☄️ Data Science
☄️ Machine Learning
☄️ Artificial Intelligence
☄️ Computer Vision
☄️ Deep Learning
☄️ AWS Machine Learning Services
☄️ MLops
☄️ NLP

Skills

Languages, Libraries, Tools and Frameworks:



💻 Programming

Python NumPy Pandas Scikit--learn statsmodels SciPy Java SpringBoot JavaScript

💾 Database Management

PostgreSQL MySQL Oracle DataBase

📷 Image Processing

OpenCV Scikit--image SciPy NumPy

📊 Data Visualization

PowerBI Matplotlib Seaborn

🤖 ML/DL Frameworks

TensorFlow Keras PyTorch

🤖 AI/ML Applications

Classification Regression Clustering Image Classification Object Detection Face Detection Image Captioning Anomaly Detection Fraud Detection Object Tracking and Localization Q-Learning DQN

📊 Data Science and ML

Data Wrangling Data Cleaning EDA Feature Engineering Model Building and Evaluation

☁️ AWS

SageMaker CloudFront EFS EC2 RDS VPC EFS APIGateway CloudWatch EventBridge AWS CLI AWS Python SDK Data Pipeline Lambda Batch Step Functions IAM CloudWatch

📜 Miscellaneous

Git Django React Jupyter Notebook Lab Google Colab MS Office Requests Beautiful Soup

Top Langs


Thank you for taking the time to visit my GitHub profile! 🙏🏼

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  1. TimeSeries TimeSeries Public

    The class applies the DRY principle on Time Series Analysis processes.

    Jupyter Notebook 6 1

  2. AutoGluon AutoGluon Public

    In this project, you'll use the AutoGluon library to train several models for the Bike Sharing Demand competition in Kaggle. You will be using Tabular Prediction to fit data from CSV files provided…

    Jupyter Notebook 4

  3. Give-Your-Application-Auto-Deploy-Superpowers Give-Your-Application-Auto-Deploy-Superpowers Public

    In this project, you will prove your mastery Continuous Delivery

    TypeScript 4

  4. IACCloudFormationCode IACCloudFormationCode Public

    Deploy underlying infrastructure components that provides security and services to our servers. Provisioning of Infrastructure using CloudFormation following the architecture diagram created using …

    Shell 5

  5. Infrastructure-creation-circleci Infrastructure-creation-circleci Public

    JavaScript 3

  6. promote_to_production promote_to_production Public

    A set of circleci jobs that promotes a new environment to production and decommissions the old environment in an automated process.

    Shell 4