Data powers AI and every domain

Machine learning is a branch of artificial intelligence that enables computers to learn from data and improve their performance on specific tasks without being explicitly programmed. It uses algorithms to identify patterns in data, make decisions, and predict outcomes based on historical information.

Data visualization turns raw data into graphical formats (charts, maps, dashboards), making patterns, trends, and outliers easier to understand.For example, a line chart can help track sales growth over time better than a table of numbers.

Pandas is a powerful Python library used for:Reading/writing data (CSV, Excel, SQL, JSON), Data cleaning, filtering, reshaping, Aggregation and pivoting, Time series handling

Machine learning is used in business for customer segmentation, demand forecasting, recommendation systems, fraud detection, process automation, predictive maintenance, sentiment analysis, customer churn prediction, quality control, and optimizing marketing campaigns. It helps businesses increase efficiency, reduce costs, and create new revenue opportunities some of the top domain where Machince Learning is used (Finance, Healthcare, Defence and more).

A Power BI Dashboard is a one-page, interactive canvas created by pinning visuals from multiple reports. It offers a high-level view of KPIs, metrics, and trends in real-time. Dashboards are ideal for decision-makers to monitor performance at a glance. Example: A retail manager may use a dashboard to track sales by region, product category, and returns.

Common metrics use to measure the performance of Machine Learning model are 1)Accuracy: The proportion of correct predictions 2) Precision, Recall, F1-Score: Used for imbalanced datasets, especially in classification tasks.3) Mean Squared Error (MSE) or Mean Absolute Error (MAE): For regression tasks. 4) Area Under the ROC Curve (AUC-ROC): For binary classification.

Most companies store their data in relational databases like MySQL, PostgreSQL, SQL Server, or Oracle. SQL is the standard language to retrieve that data efficiently.SQL helps you filter, clean, aggregate, and join data — essential steps before any analysis or modeling.These tasks can be done with high performance directly in the database, reducing memory usage in tools like Python or Excel.