1st lesson belanja
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Adalina
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1st lesson belanja
- Computer programming
- Python
University of California, San Diego Data Science graduate teaches Python coding from middle school to high school in San Diego
- Computer programming
- Python
Lesson location
About Adalina
About Me as a Tutor I am an experienced Data Science and Machine Learning tutor with a strong academic background and hands-on project experience. As a college senior specializing in Data Science with a minor in Cognitive Science, I have worked extensively with machine learning models, neural networks, clustering techniques, and real-world applications of AI. My expertise spans Python, statistical modeling, and deep learning frameworks, and I have applied these skills in both academic research and practical projects. My Teaching Philosophy I believe that anyone can master data science and machine learning with the right guidance. My approach is: Concept-First Learning – I break down complex topics into intuitive, real-world explanations. Hands-On Problem Solving – Learning happens by doing, so I emphasize coding exercises and debugging strategies. Personalized Guidance – Every student has a unique learning style, and I tailor my lessons accordingly. Real-World Applications – I connect theoretical knowledge to industry use cases to make learning engaging and practical. What to Expect from My Lessons Clear Explanations – No jargon overload. I ensure you understand the “why” behind every concept. Interactive Coding Sessions – Whether it’s KNN, neural networks, or clustering, we’ll work through real datasets together. Debugging & Troubleshooting Skills – I teach not just how to write code, but how to fix it when things go wrong. Confidence Building – I guide students to think critically and solve problems independently.
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About the lesson
- Primary
- Secondary
- SPM
- +10
levels :
Primary
Secondary
SPM
Form 6
STPM
Adult education
Masters
Doctorate
Beginner
Intermediate
Advanced
Professional
Kids
- English
All languages in which the lesson is available :
English
My Approach as a Tutor Teaching Method & Techniques My tutoring style is hands-on, adaptive, and problem-solving-oriented. I focus on: Conceptual Understanding First – Before diving into coding or equations, I ensure students grasp the "why" behind concepts. Real-World Applications – I relate topics to practical examples, especially in data science, machine learning, and cognitive science. Guided Problem-Solving – Instead of giving direct answers, I ask leading questions to help students think critically and build confidence. Iterative Learning – I encourage testing ideas, debugging, and reflecting on mistakes as a way to deepen learning. Visual Aids & Intuition-Based Learning – I use visualizations, analogies, and code walkthroughs to make abstract topics clearer. A Typical Lesson Plan Example: Understanding K-Nearest Neighbors (KNN) with EEG Data Warm-Up & Review (5-10 min) Briefly discuss last session or foundational concepts (e.g., distance metrics in machine learning). Quick discussion on why KNN matters in classification problems. Concept Breakdown & Interactive Discussion (20 min) Explain KNN with an analogy (e.g., "finding similar friends based on interests"). Show a step-by-step breakdown of train/test split vs. LOOCV using real-world EEG data. Engage in a short discussion: When would LOOCV be better than a normal split? Hands-on Practice & Code Implementation (30 min) Walk through a Jupyter notebook together, visualizing KNN clusters. Debug errors together (teaching debugging strategies). Challenge the student with an open-ended question (e.g., How would KNN perform with high-dimensional data?). Wrap-Up & Reflection (5-10 min) Review key takeaways. Assign a mini-project (e.g., Apply KNN to another dataset and compare results). What Sets Me Apart as a Tutor Personalized Learning Paths – I adapt my teaching to the student's pace, strengths, and goals. Interdisciplinary Approach – My background in data science & cognitive science allows me to connect machine learning with human decision-making. Debugging & Problem-Solving Focus – Many students struggle with troubleshooting. I teach them how to break down problems logically. Collaboration-Oriented – I approach sessions like a peer discussion rather than a lecture, which makes students more comfortable exploring ideas. Who My Lessons Are For Level: Undergraduate & Graduate Students in Data Science, Cognitive Science, or CS Beginners to Intermediate learners in Machine Learning or Python Students working on projects involving AI, ML, clustering, or neural networks
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Rates
Rate
- RM102
Pack rates
- 5h: RM512
- 10h: RM1024
free lessons
This first lesson is free to allow you to get to know your teacher so that they can best meet your needs.
- 1hr
online
- RM102/h
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