Machine Learning

The course bridges the gap between data science, data analysis, and machine learning, helping you understand their interconnections. Data science encompasses the entire process of extracting knowledge from data, while data analysis focuses on interpreting data. Machine Learning plays a critical role by automating data analysis and prediction, enabling you to build models that forecast future outcomes based on historical data. 

The course covers essential Python programming libraries and will guide you through data prediction and AI step-by-step, using real-world data and case studies. Along with theoretical knowledge, you will gain hands-on experience building your own machine learning models. By the end of the course, you'll be proficient in creating robust models for data prediction, classification, and clustering empowering you to make precise forecasts and data-driven decisions.

*One continuing education unit equals 10 contact hours.

1. Discuss fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML)
2. Differentiate between data science and data analysis
3. Prepare data for prediction using preprocessing techniques
4. Apply regression techniques to predict outcomes from data
5. Use classification methods to categorize datasets and clustering to group similar objects
6. Evaluate, select, and optimize machine learning models for best performance.

• Passing Advanced Python for Data Science