0 years

0 Lacs

India

Remote

Job Title: Advanced Machine Learning Tutor Location: Fully Remote Type: Part-Time (Weekend Only) Salary: ₹40,000 – ₹50,000 per month Role Overview We are seeking a skilled and passionate Advanced Machine Learning Tutor to teach learners who have already completed foundational ML topics. You will conduct remote weekend classes , mentor students through hands-on projects, and explain the real-world use of advanced algorithms and techniques. This role is perfect for ML professionals who love teaching, enjoy simplifying complex ideas, and want to make a real impact on career-oriented learners. Key Responsibilities Deliver live weekend sessions on advanced ML concepts, such as: Ensemble Methods (Bagging, Boosting, Stacking) Feature Engineering and Selection Techniques Model Evaluation & Validation (Cross-Validation, ROC-AUC, etc.) Unsupervised Learning (PCA, Clustering techniques like K-Means, DBSCAN) Time Series Forecasting (ARIMA, Prophet, LSTM basics) Hyperparameter Tuning (GridSearchCV, RandomizedSearch, Bayesian Optimization) Introduction to Deep Learning (optional, depending on scope) Guide students through project-based learning using real-world datasets. Support students with doubts, debugging, and tool usage (like scikit-learn, XGBoost, LightGBM). Collaborate on curriculum refinement and improve content delivery. Keep updated with current trends in ML and bring them into your teaching. Required Qualifications Strong experience in Machine Learning beyond basic models Proficient with Python, NumPy, pandas, scikit-learn, XGBoost, etc. Familiar with advanced concepts like model selection, bias-variance tradeoff, overfitting/underfitting solutions Ability to explain and mentor real-world ML projects Previous teaching, tutoring, or mentoring experience (preferred) Excellent communication and presentation skills Nice to Have Familiarity with MLOps basics and tools (like MLflow) Experience with real-time or production ML systems Exposure to research papers or Kaggle competitions Understanding of Time Series and basic DL models like LSTMs Prior educational or ed-tech experience Students Will Learn To: Build and fine-tune complex ML models Evaluate model performance using advanced techniques Work with imbalanced datasets and noise Engineer features for better accuracy Solve real-world problems (credit risk, fraud detection, forecasting, etc.)

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