Interactive Physics tutoring with hands-on experiments for engaging and effective learning experiences.

Student and Tutor reviewed various supervised machine learning models, including logistic regression, decision trees, and SVMs. The Tutor then introduced deep learning, covering the fundamentals of neural networks, different architectures like CNNs and LSTMs, and the evolution to Large Language Models (LLMs) and Agentic AI. They explored tools like Groq and Flowise for building and testing AI agents, and the Tutor committed to sharing reference materials for further study on deep learning and agents.

The Student and Tutor reviewed and practiced problems related to RC circuits, focusing on discharging calculations for capacitance, charge, and current over time. The Tutor then introduced the fundamental concepts of RL circuits in a DC context, highlighting the time constant and distinguishing them from AC applications. The next session is planned to cover AC RL, RC, and RLC circuits, including phasor diagrams.

Student and Tutor reviewed fundamental RC circuit concepts, including capacitor charging/discharging, time constants, and their application in AC-DC rectifier circuits. They solved several problems involving voltage, current, and time constant calculations for various RC circuits. The next session will cover additional RC circuit problem formats and then move on to RL and RLC circuits.

Student and Tutor began a new topic on AC circuits, building upon the Student's recent success with DC circuit concepts like KVL, KCL, and superposition. They discussed the fundamental roles of resistors, capacitors, and inductors in circuits, then delved into the specifics of RC circuits, including the concept of the time constant and its implications for charging and discharging. The Tutor recommended the Student watch videos for further clarification on specific concepts before the next session.

The Tutor and Student explored supervised learning classification models, focusing on Logistic Regression. They discussed various classification algorithms, their applications, and implemented Logistic Regression using a heart disease dataset, including data cleaning, model evaluation via confusion matrices and classification reports, and interpretation of feature importance using SHAP values. The next session will be scheduled after the Student's interview.

The Student and Tutor focused on the Maximum Power Transfer Theorem in electrical circuits. They reviewed its principles, derived the formula for maximum power transfer, and practiced solving problems involving various resistor networks to find the optimal load resistance and maximum power delivered. The next session will cover RLC circuits and related concepts like resonance.