medini bv
Engaging Science Tutor using interactive tools for immersive learning experiences and conceptual understanding.
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medini bv
Bachelors degree
Enroll after the free trial
Each lesson is 55 min
50 lessons
20% off
/ lesson
30 lessons
15% off
/ lesson
20 lessons
10% off
/ lesson
10 lessons
5% off
/ lesson
5 lessons
-
/ lesson
1 lessons
-
/ lesson
About your science tutor
I'm Medini BV, a Bachelors-educated tutor with a focus on School Science for college students. With years of experience, I offer tutoring in Health Science, Astronomy, and Environmental Science. My specialities range from Career guidance to French pronunciation skills and Math Tricks. I excel in personalized learning plans, ensuring each student benefits from tailored approaches. Let's embark on a learning journey together to conquer science subjects with ease and confidence!
medini graduated from GOVERNMENT ENGINEERING COLLEGE RAMANAGARA
Specialities of your science tutor
Personalized learning plans
Science experiments
Real world application
Science lab skills
Review sessions
Student types for science class
Learning Disabilities
College
Science class overview
My teaching methodology revolves around laying a strong foundation by focusing on fundamentals first, followed by hands-on learning experiences. I engage students through interactive and visually stimulating lessons, incorporating data analysis to enhance comprehension. Specializing in subjects like Environmental Science, Health Science, Astronomy, and School Science, I cater to college-level students. Through a blend of online platforms like digital whiteboards, interactive quizzes, and game-based learning tools along with offline resources, I ensure personalized tutoring that aligns with various curricula such as A-Levels, AP Program, and IB standards. This approach not only fosters a deeper understanding of the subjects but also cultivates critical thinking skills for academic success.
Your science tutor also teaches
Environmental Science
Astronomy
Health Science
School Science
School Science concepts taught by medini
Git Workflow: Working Directory
Staging
Commit
Git Configuration: Username and Email
Connecting Local Repository to Remote (GitHub)
Basic Git Commands
Repositories (Repo) and Branches
The session focused on machine learning, covering supervised learning algorithms like linear regression, logistic regression, decision trees, and random forests. The student learned about feature engineering, model selection, and deployment strategies. The tutor assigned the student to create a GitHub account and explore uploading projects, with a follow-up lesson planned to implement machine learning models on a dataset.
Random Forests
Machine Learning vs. Deep Learning vs. AI
Supervised
Unsupervised
and Reinforcement Learning
Linear Regression
Logistic Regression
Decision Trees
The session focused on data preprocessing techniques in Python, including column selection, data transformation using encoding methods, scaling, normalization, and data grouping. The Student practiced implementing these techniques using Pandas and Scikit-learn on a sample dataset. The next steps involve further data transformation, visualization, and potentially implementing machine learning models.
Feature Selection: Dropping Irrelevant Columns
Data Analysis: Grouping and Aggregation
Data Transformation: Scaling and Normalization
Data Transformation: Encoding Categorical Variables
The student and tutor discussed data analysis and engineering using Python, focusing on data cleaning techniques. The student learned to use Pandas and other libraries to handle missing data, remove duplicates, and correct data types in preparation for analysis and machine learning. The next steps include categorical cleansing and statistical analysis.
Data Pipelining in Data Analysis/Engineering
Exploratory Data Analysis (EDA) Tools
Python Libraries for Data Analysis
Data Cleaning and Preprocessing
Data Transformation
Descriptive Statistics and Data Understanding
The session involved debugging line sensor and encoder code for a robot. The student worked on resolving errors in the encoder code and troubleshooting issues with sensor readings. The tutor provided guidance and code modifications to achieve functional sensor readings. The next step is to integrate the sensor code with the motor control code for robot movement, planned for the next session.
Analog vs. Digital Readings
Library Integration and Troubleshooting
Hardware Connections and Pin Assignments
Understanding Sensor Calibration
Identifying and Resolving Code Errors
The Student and Tutor worked on debugging the Student's line-following robot code by adjusting sensor pin configurations and integrating the code with the robot's hardware. They tested and modified code related to line sensor readings, motor control, and calibration routines to improve the robot's performance. The tutor will rewrite the code and share it for testing in the next session, focusing on assessment file requirements.
Motor Speed Configuration
Line Sensor Pin Configuration
Library Integration and Management
Calibration Process for Line Sensors
Debugging Strategy: Step-by-Step Approach
Teaching tools used by science tutor
Interactive diagrams
Flashcards
Quizzes
Note taking
Interactive 3D models
Science tutors on Wiingy are vetted for quality
Every tutor is interviewed and selected for subject expertise and teaching skill.
