Harshit Srivastava - IT Professional with 10+ YoE MTech | BTech

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in BTech Tuition

10

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Python Training classes

10

Teaching Experience in detail in Python Training classes

I have experience teaching Python programming with a strong focus on practical learning and real-world applications. My training covers Python fundamentals such as data types, control structures, functions, and object-oriented programming, followed by hands-on coding exercises and mini projects. I also guide students in problem-solving using Python, debugging techniques, and best coding practices, and introduce relevant libraries and basic data structures to help them build strong programming and interview-ready skills.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Interview Skills Training

5

Teaching Experience in detail in Interview Skills Training

I have experience mentoring candidates for **technical interviews**, focusing on improving **problem-solving ability, coding approach, and communication during interviews**. My sessions cover **data structures, algorithms, Python-based problem solving, and common coding interview patterns**. I conduct **mock interviews, live problem-solving sessions, and resume discussions** to help candidates understand how to structure their answers and think aloud while solving problems. The training also includes **interview strategies, time management, and handling technical and behavioral questions**, helping candidates build confidence and perform effectively in real interview scenarios.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in C++ Language Classes

10

Teaching Experience in detail in C++ Language Classes

I have been teaching Data Structures and Algorithms to students of BTech in CSE and IT and Diploma Students in order to prepare them for their placement interviews and become extremely familiar with the programming language such that they are able to develop thinking abili to approach any given problem statement.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Java Training Classes

10

Teaching Experience in detail in Java Training Classes

Have been teaching Engineering and Diploma students for subjects such as DS&Algo, Problem Solving Using Object Oriented Programming Language, API development, Micro Services, Multi Threading, Multi Processing, Selenium Testing, Core Java, SOAP Architecture, Spring Frame Work, etc using Java. It all starts with the basics and fundamentals of programming language, where in we start with understanding whats, whys, why nots, where, how, etc.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in django

10

Class Location

Online class via Zoom

I am Willing to Travel

Tutor's Home

Years of Experience in Deep Learning Training

8

Teaching Experience in detail in Deep Learning Training

I have extensive practical experience in Deep Learning, particularly in Computer Vision, developed through more than six years of working on AI-driven and automotive perception systems. My teaching approach is grounded in real-world engineering, helping learners understand not only how deep-learning models are created but also how they are evaluated, optimised, and integrated into practical applications. I teach the fundamental concepts of artificial neural networks, including activation functions, forward propagation, backpropagation, loss functions, gradient descent, regularisation, batch normalisation, and different optimisation techniques. I explain these concepts through intuitive examples, visual demonstrations, and hands-on Python exercises so that learners can connect mathematical principles with actual model behaviour. For Computer Vision, I provide practical training in image preprocessing, data augmentation, convolutional neural networks, transfer learning, image classification, object detection, semantic and instance segmentation, and object tracking. Learners work with Python, C++, OpenCV, NumPy, PyTorch or TensorFlow, and commonly used pretrained architectures. I also explain performance metrics such as precision, recall, F1-score, Intersection over Union, mean Average Precision, inference latency, and model size. My professional background in Advanced Driver Assistance Systems enables me to introduce industry-oriented examples involving camera data, road-scene understanding, object recognition, dataset quality, edge cases, model validation, and real-time inference. I also teach learners how to diagnose overfitting, class imbalance, poor generalisation, data leakage, and unreliable predictions. Where appropriate, I demonstrate how trained models can be integrated into C++ applications for performance-sensitive deployment. This includes model export, image preprocessing, inference pipelines, memory considerations, and optimisation for real-time environments. My sessions follow a structured progression from foundational concepts to guided implementation and independent projects. I encourage learners to experiment, interpret results, debug systematically, and explain the reasoning behind their technical decisions. My objective is to ensure that students can independently design, train, evaluate, optimise, and deploy deep-learning solutions rather than simply reproduce code from tutorials.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in AI Training

8

Teaching Experience in detail in AI Training

I have extensive practical experience in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Generative AI, and Retrieval-Augmented Generation. My AI training approach combines conceptual understanding, hands-on implementation, and real-world industry applications, enabling learners to progress from foundational concepts to production-oriented AI systems. I teach learners how to understand data, select suitable algorithms, train models, evaluate performance, and improve results systematically. My sessions cover supervised and unsupervised learning, feature engineering, regression, classification, clustering, neural networks, model evaluation, hyperparameter tuning, and overfitting prevention using Python and widely used AI libraries. In Deep Learning, I explain neural networks, backpropagation, convolutional neural networks, recurrent networks, attention mechanisms, and transformers through visual explanations and practical coding exercises. Drawing on more than six years of professional experience in AI and Computer Vision, including Advanced Driver Assistance Systems, I incorporate industry-relevant examples involving image classification, object detection, segmentation, tracking, real-time inference, edge cases, and model validation. I also provide training in Natural Language Processing and Generative AI. Learners explore text preprocessing, embeddings, transformers, large language models, prompt engineering, structured outputs, tool calling, AI agents, and multimodal applications. For advanced learners, I cover Retrieval-Augmented Generation systems, including document ingestion, chunking, embeddings, vector databases, semantic search, reranking, grounding, citations, and RAG evaluation. My teaching style is practical, structured, and project-oriented. I break complex AI concepts into understandable steps and reinforce them through demonstrations, coding assignments, debugging exercises, and end-to-end projects. I emphasize not only how to implement a model but also why a particular approach should be selected and how its limitations should be assessed. I guide learners in handling real-world challenges such as poor-quality data, class imbalance, hallucinations, bias, data leakage, scalability, latency, model reliability, and responsible AI use. My objective is to help students independently design, build, evaluate, and deploy reliable AI applications while developing the problem-solving mindset required for professional AI roles.