Debasish Maji

Years of Experience in Data Science Classes

9

Teaching Experience in detail in Data Science Classes

My Teaching Experience in Data Science & AI/ML I have designed and delivered structured learning paths for Data Science, Machine Learning, and AI to help learners progress from beginners to expert level, focusing on mathematical depth, hands-on implementation, and research-driven learning. 📌 Teaching Approach 1. Structured, Step-by-Step Learning • Theory → Visualization → Coding → Real-World Projects • Mathematics First: Foundations in Linear Algebra, Probability, and Calculus • Concept Building: Intuitive explanations with real-world analogies • Hands-on Coding: Implement algorithms from scratch before using libraries • Project-Driven: Solve industry-level problems 2. Adaptability to Learning Styles • For beginners: Focus on intuition, interactive coding exercises, and visual explanations • For intermediate learners: Emphasize deep understanding of ML models, optimization techniques, and hyperparameter tuning • For advanced learners: Guide research projects, optimization techniques, and real-world deployment 📌 Teaching Experience Across Key Areas 1. Mathematics for Machine Learning • Designed Linear Algebra & Probability Crash Courses for ML learners • Conducted hands-on coding sessions for PCA, Eigenvectors, and Probability Distributions • Developed real-world applications like anomaly detection using probability theory 📜 Example Lesson Plan • Week 1: Vectors, Matrices, Eigenvalues, PCA • Week 2: Probability Distributions, Bayes Theorem • Week 3: Optimization, Gradient Descent 2. Machine Learning (Supervised & Unsupervised) • Created hands-on ML courses covering: • Regression & Classification (Logistic Regression, Random Forest, SVM) • Clustering (K-Means, DBSCAN) • Dimensionality Reduction (PCA, t-SNE) 📜 Example Projects • Credit Risk Prediction using Logistic Regression • Customer Segmentation with K-Means Clustering • Feature Selection using Principal Component Analysis (PCA) 3. Deep Learning (Neural Networks & Transformers) • Taught Deep Learning using TensorFlow & PyTorch • Explained Backpropagation, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) • Hands-on implementation of LLMs (BERT, GPT) & GANs 📜 Example Labs • Implementing a CNN from Scratch for Image Classification • Fine-tuning BERT for Sentiment Analysis • Building a GAN for Generating Synthetic Faces 4. AI/ML for Finance & Time Series • Taught Time Series Forecasting using ARIMA, LSTMs, and DeepAR • Explained Reinforcement Learning for Trading Strategies • Built AI-powered trading bots for real-time market data analysis 📜 Example Projects • Stock Price Prediction using DeepAR • AI Trading Bot using Reinforcement Learning 5. MLOps & AI Deployment • Covered ML Pipelines, Model Monitoring, and Deployment using AWS SageMaker • Hands-on building CI/CD pipelines for ML models 📜 Example Labs • Deploying ML models as APIs using FastAPI • Monitoring ML models in production using MLflow 6. AI Research & Advanced Topics • Guided learners on reading, implementing, and improving research papers • Conducted deep dives on LLM hallucinations, AI for software engineering, and AI for anomaly detection 📜 Example Research Discussions • Reproducing SOTA Transformers for Text Summarization • Analyzing Hallucinations in AI Models for Code Generation 📌 Key Achievements ✔️ Taught 100+ learners from beginner to advanced ML research level ✔️ Mentored AI Engineers & Researchers on real-world AI applications ✔️ Guided AI-driven startups & fintech companies on deploying AI models ✔️ Helped learners publish research papers on AI/ML This experience makes my teaching highly practical, research-driven, and aligned with cutting-edge AI advancements. Let’s get started on your journey to becoming an AI/ML Scientist! 🚀