niteen kumar
Active this weekExperienced Python & Data Science trainer specializing in Python, NumPy, Pandas, Data Analysis and Machine Learning. I teach through simple explanations, practical examples and hands-on coding, helping beginners and students build strong concepts and confidence step-by-step.
Hyderabad, AttapurView on map →
Python Programming Python fundamentals and programming logic Variables, data types, operators and control flow Lists, tuples, sets and dictionaries Functions, modules and packages List/dictionary comprehensions Exception handling File handling Object-Oriented Programming (OOP) Practical coding exercises and mini projects Data Science with Python NumPy – arrays, indexing, slicing, reshaping, broadcasting and vectorization Pandas – Series, DataFrames, data cleaning and data manipulation Data visualization with Matplotlib Statistics for Data Science Exploratory Data Analysis (EDA) Data preprocessing and feature engineering Machine Learning Machine Learning fundamentals Supervised and unsupervised learning Regression and classification Linear Regression Logistic Regression Decision Trees Random Forest KNN SVM K-Means Clustering Model evaluation and performance metrics Train/test split and cross-validation Scikit-learn implementation End-to-end ML projects AI / Generative AI — Foundation AI and Machine Learning fundamentals Introduction to Deep Learning Neural Networks and CNN fundamentals Introduction to NLP LLM fundamentals Prompt engineering Embeddings and vector databases RAG fundamentals Practical GenAI applications Concepts are explained step-by-step using simple examples, followed by hands-on coding, exercises and practical projects. Suitable for beginners, college students and working professionals.
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About niteen kumar
Python Programming Python fundamentals and programming logic Variables, data types, operators and control flow Lists, tuples, sets and dictionaries Functions, modules and packages List/dictionary comprehensions Exception handling File handling Object-Oriented Programming (OOP) Practical coding exercises and mini projects Data Science with Python NumPy – arrays, indexing, slicing, reshaping, broadcasting and vectorization Pandas – Series, DataFrames, data cleaning and data manipulation Data visualization with Matplotlib Statistics for Data Science Exploratory Data Analysis (EDA) Data preprocessing and feature engineering Machine Learning Machine Learning fundamentals Supervised and unsupervised learning Regression and classification Linear Regression Logistic Regression Decision Trees Random Forest KNN SVM K-Means Clustering Model evaluation and performance metrics Train/test split and cross-validation Scikit-learn implementation End-to-end ML projects AI / Generative AI — Foundation AI and Machine Learning fundamentals Introduction to Deep Learning Neural Networks and CNN fundamentals Introduction to NLP LLM fundamentals Prompt engineering Embeddings and vector databases RAG fundamentals Practical GenAI applications Concepts are explained step-by-step using simple examples, followed by hands-on coding, exercises and practical projects. Suitable for beginners, college students and working professionals.
