Dr. Gurinderjeet Kaur
Hands-on Computer Science tutor with engaging, problem-solving lessons
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Private tutor - Dr. Gurinderjeet Kaur
Doctorate degree
/ 30 min
Dr. Gurinderjeet - Know your tutor
Hello, I am Dr. Gurinderjeet Kaur, a dedicated computer science educator with a Doctorate in Computer Science Engineering. I simplify complex concepts and emphasize a hands-on learning approach, guiding students through coding exercises and real-world projects. My goal is to help students build practical skills, confidence, and critical thinking. I specialize in Computer Science, CSS, Databases, HTML, Java, JavaScript, Matlab, Python, R, SQL, Artificial Intelligence, Microsoft Excel, C, C++, and Coding for Kids. Additionally, I teach Mathematics and Science up to grade 10 level. Whether you're a school student, a college learner, or a professional, I tailor lessons to fit your level. Additionally, I can also teach Mathematics and Science subjects up to grade 10 level. My teaching approach in these subjects emphasizes clarity of concepts, with a focus on making abstract theories understandable through real-world examples and practical exercises. I tailor lessons to the learning pace of each student, ensuring that they grasp every concept fully before moving on. My teaching is interactive, encouraging curiosity and active participation. I focus on helping students apply their knowledge practically, fostering problem-solving abilities, and creating a supportive environment where they feel comfortable asking questions. Tailoring Learning for Every Level Whether you are a school student, college student, or a professional looking to upskill, I can cater to learners at all levels. I have experience teaching students with varying degrees of expertise and can adapt my teaching style to suit their individual needs.
Meet Dr. Gurinderjeet
Dr. Gurinderjeet graduated from Thapar Institute of Engineering and Technology India


Programming tutor specialities
Next Generation Science Standards - NGSS (USA)
GCSE (UK)
Assignment help
Paired coding
Provincial-specific curriculum (CA)
Project help
Upskilling
Advanced Placement (AP) Program (USA)
Common Core State Standards - CCSS (USA)
Homework help
Job readiness
Exam prep
Programming class overview
My teaching style is designed to foster a supportive and interactive learning environment where students feel empowered to ask questions, share ideas, and collaborate with others. I firmly believe that the best learning happens when students are encouraged to think critically and apply their knowledge in real-world situations. I focus on: Interactive Learning: I regularly use live coding sessions, real-time problem-solving, and collaborative projects to ensure that students not only understand the material but can also apply it practically. This helps students stay engaged and gives them a sense of accomplishment when they see their code come to life. Encouraging Curiosity: I encourage students to ask questions and explore beyond the curriculum. In programming, learning never stops. I provide guidance on how to approach learning new languages, frameworks, or concepts on their own, fostering a spirit of curiosity that will benefit them long after the course ends. Practical Applications: Every concept I teach is paired with practical examples and real-world scenarios. Whether it's building a website, solving a complex algorithm problem, or developing an AI model, I ensure that students are ready to apply their skills in real-world environments.

Computer Science concept taught by Dr. Gurinderjeet
The student reviewed homework and then learned about the JavaScript `sort`, `every`, and `some` array methods. The Tutor explained the functionality of each method, including examples for sorting numbers, strings, and objects, as well as checking conditions with `every` and `some`. The student will learn DOM manipulation next session.
Every Method
Sort Method
Some Method
The Student practiced deploying a containerized ML model to Kubernetes, including creating a model, building a Flask API, and writing a Dockerfile. The session covered error handling, input validation, and Docker image configuration. The Student will complete the YAML file and test the deployment.
Kubernetes Deployment for ML Models
Flask API for Model Serving
Model Training and Persistence
Input Data Validation in APIs
Containerizing ML Models with Docker
The student and tutor reviewed functions to process lists of any, including calculating depth and removing elements. The student then worked on understanding expression trees and their evaluation using mutual recursion. The student will send notes for the next session, which will cover lambda and abstract functions.
List of Any Definition
Determining Depth of a List of Any
Atoms in Racket
Evaluating Expression Trees (Mutual Recursion)
Expression Trees
Removing Elements from a List of Any
The Tutor and Student reviewed advanced JavaScript concepts, including higher-order functions, callbacks, map, filter, and reduce methods. The Student was assigned practice questions related to these topics as homework. They scheduled their next session to continue practicing and increase the difficulty level.
Higher-Order Functions
Callbacks
Map Method
Filter Method
Reduce Method
The session involved an extra credit assignment focused on creating an ETL and ML pipeline for real-time fraud detection. The student and tutor reviewed the project requirements, data structure, and expected components. The student began setting up the project repository and folder structure, with plans to start filling in the code and addressing the 'todos' outlined in the assignment.
ETL Pipeline
ML Pipeline
Data Quality Issues
Git Branching Strategy
AI Prompt Engineering
Pydantic Schemas
Synthetic Data Generation
The session covered Bayesian statistics, including Poisson and Gamma distributions, and uniform distributions. The Student worked on problems involving finding posterior distributions with Gamma priors and uniform likelihoods. The Tutor assigned the Student the task of watching videos and creating cheat sheets for various distributions for future sessions.
Poisson Distribution
Gamma Distribution
Poisson-Gamma Conjugate Prior
Uniform Distribution
Posterior Distribution Calculation
Likelihood Function for Uniform Distribution
Your programming tutor also teaches
Computer Science
Databases
Web Development
App Development
Artificial Intelligence
Learner for programming class
Home schooled
All Levels
School
Adult / Professional
College
Teaching tools used by tutor
Bitbucket
Jupyter Notebook
NetBeans
Android Studio
Dynamic programming classes
Mobile joining
Chat for quick help
Open Q&A
Pets are welcomed
Record lessons
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