Exploring Machine Learning (ML) with Python, this course provides an introduction to ML concepts, including supervised vs unsupervised learning, linear & non-linear regression, simple regression, and more. It delves into classification techniques using algorithms like K-Nearest Neighbors (KNN), decision trees, and Logistic Regression, while emphasizing hands-on learning with Python libraries such as SciPy and scikit-learn, making it suitable for those seeking to advance their Data Science career or begin their journey in Machine Learning and Deep Learning.
Time: 10:00am – 12:00pm
Dates:
Saturday, March 30
Saturday, April 6
Saturday, April 13
Saturday, April 20
