Language: English
Duration: 24 hours
Course Description:
This course offers a practical, hands-on journey into machine learning and data science, focused on real-world applications. With a curated blend of key topics—including data exploration, clustering, recommendation engines, regression, decision systems, forecasting, and neural networks—learners will build a strong foundation in solving analytical problems using structured and unstructured data. Through Python-based tools and libraries (like Scikit-learn, Pandas, and TensorFlow), the course emphasizes practical implementation over theory, preparing learners for data-driven roles across industries.
Pre Requisites:
- Basic programming skills (preferably in Python)
- Fundamental knowledge of statistics (mean, median, correlation, distributions)
- Understanding of data types, control flow, and functions
- Introductory exposure to linear algebra and probability concepts
Course Objectives
By the end of the course, learners will be able to:
- Explore and visualize datasets to identify patterns, trends, and anomalies
- Apply clustering techniques (e.g., K-Means, Hierarchical) to group similar data
- Build recommendation systems using collaborative filtering and content-based techniques
- Perform regression analysis to model relationships and make predictions
- Design decision systems using decision trees and rule-based approaches
- Develop forecasting models to predict time-series data using statistical and ML-based methods
- Implement basic neural network models for classification and prediction tasks
- Evaluate and optimize models using appropriate metrics and validation techniques
Contents
- Data Exploration & Clustering
- Recommendation Systems
- Regression Analysis
- Decision Systems
- Forecasting Systems
- Neural Networks
