Python & AL ML Instructor

Role Overview
We are seeking a skilled instructor with strong hands-on expertise in Python and Machine Learning who can deliver live, interactive, and outcome-oriented learning sessions. The candidate should be able to simplify complex and math-heavy ML concepts, use practical datasets and visualizations, and help students independently build and interpret machine learning models.

Prior teaching or edtech experience is preferred. However, candidates with strong real-world or academic ML project experience and excellent communication skills may also be considered.

Key Responsibilities

• Deliver live interactive sessions on Python, from beginner to advanced levels.
• Teach Machine Learning fundamentals with a strong focus on conceptual clarity and practical implementation.
• Cover core Python and ML libraries, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
• Teach supervised and unsupervised learning concepts.
• Cover regression, classification, clustering, feature selection, data preprocessing, and model evaluation.
• Explain algorithms through real-world examples, practical datasets, visualizations, and hands-on coding exercises.
• Guide students through end-to-end ML project development, including data cleaning, preprocessing, model development, evaluation, optimization, and interpretation of results.
• Help students understand why an algorithm works and when a particular model should be used, rather than focusing only on library-based implementation.
• Address student doubts effectively and ensure high learner engagement and participation.
• Assess student progress and support students in becoming capable of building machine learning models independently.

Required Skills

• Strong expertise in Python is mandatory.
• Strong understanding of Machine Learning fundamentals is mandatory.
• Hands-on experience with NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn.
• Good understanding of supervised and unsupervised learning techniques.
• Strong knowledge of regression, classification, clustering, data preprocessing, and feature selection.
• Good knowledge of model evaluation metrics, including accuracy, precision, recall, F1-score, and related concepts.
• Clear understanding of overfitting, underfitting, bias-variance trade-offs, and model improvement techniques.
• Ability to simplify complex technical and mathematical concepts for students.
• Excellent communication and presentation skills in English; proficiency in Hinglish will be preferred.
• Ability to conduct structured, interactive, and practical classroom sessions