With six years of experience in teaching, I have developed a comprehensive and effective teaching methodology that leverages my extensive knowledge and expertise in various domains. I specialize in mastering data structures and algorithms using C and C++, machine learning operations (MLOps), machine learning, improving deep neural networks through regularization and optimization, neural networks and deep learning, computer vision, structuring machine learning projects, natural language processing specialization, and introduction to statistical learning using R/Python. My programming skills include proficiency in C, C++, Python, R, SQL, PostgreSQL, MATLAB, HTML, CSS, JavaScript, and Amazon Web Service. Additionally, I am well-versed in frameworks such as ReactJS, Django Rest Framework, TensorFlow, PyTorch, Keras, Git/GitHub, Google Colab, and Jupyter. My educational background includes courses in quantum physics and application, calculus 1 and 2, computer programming and utilization, differential equations, linear algebra, economics, remote sensing, and image processing.

In my teaching approach, I focus on creating a student-centered learning environment that begins with assessing each student's current knowledge and understanding of the subject. This assessment helps me tailor personalized learning plans to address their specific strengths, weaknesses, and learning goals. I incorporate active learning techniques such as problem-solving, coding exercises, and hands-on projects to engage students actively in the learning process. Real-world examples and applications are used to explain complex concepts, making the material more relatable and easier to understand.