Become a Citizen Data Scientist with HyperSense-AI Studio

Explore the foundations of data science, AI, ML, and deep learning, including neural networks, problem types, and analytics from descriptive to prescriptive across travel, healthcare, self-driving cars, and insurance.

Describe exploratory data analysis as the first step in machine learning pipeline that helps understand data, identify the target and variables, and perform feature engineering with visual and statistical analysis.

Explore predictive machine overview and how machine learning learns input-output relationships from data to build predictive models, using supervised learning in fraud detection with call detail records.

Understand the data science workflow, from data preparation and sourcing to model deployment. Explore EDI, feature engineering, feature selection, learning algorithms, tuning, and model evaluation in an iterative path.

Explore data preparation for machine learning, from ingestion and multi-source integration to problem statements, labels, data collection, then cleaning, transformation, encoding, missing-value treatment, normalization, outliers, and sampling for balanced models.

Explore exploratory data analysis to identify fraud and revenue drivers, create a ratio feature for SIM box diversity, and apply Ice Studio operators like expression builder and summarisation.

Select top key features from thousands to avoid multicollinearity and reduce computation; apply linear correlation checks, hypothesis testing, and feature importance, with auto feature selection favored for citizen data scientists.

Navigate model training workflows in HyperSense-AI Studio by splitting data into training, validation, and test sets, preserving distribution, selecting algorithms, tuning hyperparameters, and evaluating models with cross-validation.

Explore how to evaluate classification models using confusion matrices, thresholds, and metrics like accuracy, precision, recall, and roc/precision-recall curves, with practical tips for imbalanced data.

Explore HyperSense AI Studio, a no-code data science platform with AI automation. Build and customize machine learning pipelines using pre-built accelerators, data readers, AutoML, data preparation, feature engineering, and deployment.

Discover demand forecasting with a no-code ai studio, using train and test sets, normalization, and visualizations to predict hotel room demand and guide pricing and marketing strategies.

Demonstrate building a network intrusion detection model in High percent A.I. Studio, a no-code data science platform, showcasing data preparation, model training, and real-time risk scoring for proactive cyber defense.

Conclude the course by exploring the path from citizen data scientist to data scientist, with expert h1n1 calls available on the platform, and share feedback to improve content.