Description:

This course covers the essential foundations of machine learning, including key algorithms, concepts, and techniques used in the field. You will learn about various supervised and unsupervised learning algorithms, model evaluation, and optimization techniques.

Key Highlights:

• Supervised Learning
• Unsupervised Learning
• Model Evaluation
• Optimization Techniques

What you will learn:

• Understand Supervised Learning
  Explore how supervised learning algorithms work and their applications in solving real-world problems.
• Dive into Unsupervised Learning
  Learn about clustering, dimensionality reduction, and association rule mining in unsupervised learning.
• Evaluate Models Effectively
  Discover techniques for evaluating the performance of machine learning models and interpreting results.
• Optimize Machine Learning Models
  Get insights into hyperparameter tuning, regularization, and cross-validation for model optimization.