Yeriko Vargas
Computer Science Tutor — Stop Feeling Lost in Python & ML, Start Building Real Projects (Music, Video, AI)
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Yeriko Vargas
Masters degree
/ 55 min
Yeriko - Know your tutor
Hello there , I’m Yeriko. I teach Python, stats, and machine learning through real, exciting projects: sound, video, even game-style systems — so it actually clicks. I’ve built models at Ford Motor Company and Chrysler, and I bring that same real-world energy into how I teach. We’re not just learning, we’re building cool things with data. Here, you won’t just “coding” — you’ll use it to build, experiment, and think like a data scientist. From predicting trends to designing machine learning systems, we’ll break things down so they actually make sense — no fluff, no confusion. Lately, I’ve been building a music recommendation system that treats audio like data you can understand. Instead of guessing songs, the system breaks music down into energy, texture, and dynamics using signal processing, then organizes it with PCA and clustering to create “states” of sound. From there, it selects the next track using similarity + rules like BPM, key, and energy flow — basically thinking like a DJ but powered by machine learning. It’s a perfect example of how we take something creative and turn it into a structured, intelligent system — and that’s exactly how I teach. Also — I’m strong in SQL and tools like Tableau/Studio, so we don’t just build models, we learn how to work with real data end-to-end.
Meet Yeriko
Yeriko graduated from Oakland University
Programming tutor specialities
Project help
Homework help
Exam prep
Job readiness
Paired coding
Debugging
Code Review
CoTutorAI modules
Summary
Podcast
Quiz
Learnings
Flashcard
Spotlight
Zero Risk Guaranteed
15-days refund
Free tutor swap
No cancel fee
1-yr validity
24/7 support
Learner for programming class
ADHD
High School students
College students
Programming class overview
I specialize in tutoring students for homework, exams, and especially big exams, building understanding from the ground up. My approach is comprehensive, covering both theory in statistics and mathematics and practical Python coding. This ensures you're not just exam-ready but also equipped with essential programming skills. I emphasize Python solutions alongside statistical concepts, offering a dual-track learning path. My aim is to prepare you not just for academic success but for a thriving career in data science. With me, you get the tools and knowledge to excel in both the classroom and the professional world.
Your programming tutor also teaches
Artificial Intelligence
Computer Science
Databases
Machine Learning
Python
R
Computer Science concepts taught by Yeriko
The student and tutor worked on establishing Python connectivity for trading applications, troubleshooting API errors, and understanding programming kernels. They then explored the differences between machine learning and deep learning, followed by a discussion on applying data science to music composition using MIDI notation. The session concluded with the creation of a dedicated Python environment for music-related packages.
MIDI for Music Representation
Machine Learning vs. Deep Learning
Understanding Kernels in Computing
Python Environment Management (Conda)
The student and tutor explored the distinctions between supervised and unsupervised machine learning, particularly in the context of computer vision and data representation. They discussed how images and music can be converted into numerical data for analysis and how this can be applied to various fields. Future sessions were planned to cover setting up development environments and further explore music generation projects.
Supervised vs. Unsupervised Learning
Computer Vision: Pixels to Vectors
Feature Extraction and Data Representation
Clustering and Pattern Recognition
The student and tutor worked on various website development tasks, including image optimization, affiliate link integration, and content/styling adjustments on a WordPress site. They planned follow-up sessions to continue these improvements and discuss the potential implementation of a chatbot feature.
Website Content Renaming and Hyperlinking
Responsive Design and Alignment
Website Image Optimization
Affiliate Disclosure Banners
User Interface Elements: Pop-ups and Zoom Features
AI Chatbot Development Stages
The Student presented their WordPress website, detailing specific requirements for mobile optimization of H1 headings, integration of Amazon affiliate banners, and removal of embedded theme text from image banners. The Tutor provided an initial assessment and outlined a plan to implement these web development and design changes, with a follow-up session scheduled to transfer the updated files.
Mobile Responsiveness: H1 Headings & Centering
WordPress Theme Customization & Content Management
Website Performance & SEO Foundations
User Experience (UX) & Design Refinements
Amazon Affiliate Banner Integration
The Student and Tutor engaged in a wide-ranging discussion covering principles of aerodynamics, the application of AI and statistics in fields like rocketry and medical diagnostics, and personal data tracking for self-improvement. The Student also demonstrated and explained techniques for playing complex drum rhythms. They made plans to cover Python and app development in their next session.
Personal Data Analytics for Self-Optimization
Outliers: Rethinking Deviations in Data & Society
Rocket Nozzle Fluid Dynamics & Optimization
Data-Driven AI & Predictive Modeling
Statistics as a Framework for Experimentation & Proof
Exponential Growth & Focused Skill Development
The Student and Tutor engaged in a comprehensive discussion on core Data Science concepts, including data normalization, different types of statistical distributions, and the classification of variables. They explored the stages of Exploratory Data Analysis (EDA), covering data imputation methods and the significance of residuals in statistical modeling. The session also introduced the foundational principles of Artificial Intelligence, explaining its operational mechanics through the analogy of neurons and discussing its real-world applications. The Student expressed interest in learning about optical recognition and applying these concepts to personal data analysis for self-improvement, which was noted for future lessons.
Data Normalization and Distributions
Exploratory Data Analysis (EDA) & Imputing Missing Data
Categorical vs. Numerical Variables & Predictive Models
Residuals and Data Variance
Introduction to Artificial Intelligence (AI) and Neural Networks
Teaching tools used by tutor
PyCharm
Git & GitHub
Google Colab
Visual Studio Code
Xcode
Dynamic programming classes
Note taking
Open Q&A
Pets are welcomed
Record lessons
Parent feedback
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