Description
This course provides an introduction to the basics of machine learning and explores its applications in data analysis. Participants will learn the fundamental concepts and techniques used in machine learning, including supervised and unsupervised learning, classification, regression, clustering, and dimensionality reduction. Through hands-on experience with real-world datasets using popular programming languages and libraries, participants will gain practical skills in applying machine learning algorithms.
What You’ll Learn
- Fundamental Concepts: Gain insights into supervised and unsupervised learning, classification, regression, clustering, and dimensionality reduction techniques.
- Practical Application: Apply machine learning algorithms to real-world datasets using popular programming languages and libraries.
- Evaluation and Model Selection: Understand evaluation metrics for machine learning models and learn techniques for feature engineering and selection.
Career Prospects
- Data Analysts: Enhance skills in machine learning for improved data analysis capabilities.
- Business Analysts: Apply machine learning techniques to derive insights for business decision-making.
- Data Scientists (Entry-level): Build foundational knowledge in machine learning for career advancement.
After Completing the Course, You’ll Be Able To
- Apply Machine Learning Techniques: Implement supervised and unsupervised learning algorithms to analyze and interpret data.
- Evaluate Model Performance: Use evaluation metrics to assess and optimize machine learning models.
- Perform Feature Engineering: Enhance model performance through feature engineering techniques.
- Apply Machine Learning in Real-World Scenarios: Solve data analysis problems using machine learning approaches effectively.
