Machine Learning
Explore predictive modeling and algorithmic pattern recognition. Study data preprocessing, feature engineering, regression, classification models (Decision Trees, Random Forests, SVM), clustering, and evaluation metrics using practical Python toolkits.
Key Topics & Practical Competencies
Designed around real application scenarios rather than abstract textbook theory.
ML fundamentals & problem taxonomy
Data preparation, scaling & train-test splitting
Supervised algorithms: Linear regression & Logistic classification
Unsupervised learning: K-Means clustering
Model evaluation, cross-validation & confusion matrix
Who Should Attend
Computer science students, analytics enthusiasts, and engineers exploring AI disciplines.
Prerequisites
Python programming and foundational algebra/statistics.
Duration Standard: Course duration is based on institute baseline standards; batch-specific hours confirmed upon enquiry.
Credential Award: Candidates who complete the required practical hours and pass the laboratory evaluation earn an official Certificate of Completion issued by Techva Educational Institute under Techva Educational Foundation.
More in AI & Data Science
Data Science
End-to-end data lifecycle: data wrangling with Pandas/NumPy, exploratory visualization, statistical inference, and insight generation.
Core Competencies
Artificial Intelligence Tools
Leverage cutting-edge generative AI platforms, productivity copilots, automated workflows, and practical workplace AI tooling.
Core Competencies
Prompt Engineering
Structured prompt crafting, role prompting, few-shot conditioning, chain-of-thought logic, and AI response optimization.
Core Competencies