Machine Learning in Python

Description: Learn the fundamentals of machine learning with Python in this comprehensive course designed to take you from beginner to expert. Covering both supervised and unsupervised learning techniques, you will gain hands-on experience building, training, and evaluating machine learning models to solve real-world problems. Prerequisites • Basic understanding of Python programming. • Familiarity with basic statistical concepts. Outcomes • Understand the core concepts of machine learning. • Apply supervised learning techniques, such as linear regression and logistic regression. • Utilize unsupervised learning techniques, including clustering and dimensionality reduction. • Develop ensemble models using techniques like bagging, boosting, and random forests. • Engineer features and select the best models for machine learning problems. • Evaluate model performance and optimize through tuning. • Deploy machine learning models using Flask or Streamlit.