• Introduction to Machine Learning 
• Python for Machine Learning 
• Supervised vs Unsupervised Learning 

• Introduction to Regression 
• Simple Linear Regression 
• Model Evaluation in Regression Models 
• Evaluation Metrics in Regression Models 
• Multiple Linear Regression 
• Non-linear Regression 

• Introduction to Classification 
• K-Nearest Neighbors 
• Evaluation Metrics in Classification 
• Introduction to Decision Trees 
• Building Decision Trees 
• Introduction to Logistic Regression 
• Logistic Regression vs Linear Regression 
• Logistic Regression Training 
• Support Vector Machines 

• Introduction to Clustering 
• K-Means Clustering 
• Hierarchical Clustering 
• Density-Based Clustering 

• Introduction to Recommender Systems 
• Content-based recommender systems 
• Collaborative Filtering  

What You'll Learn

• Explain the difference between the two main types of machine learning methods: supervised and unsupervised 
• Describe Supervised learning algorithms, including classification and regression 
• Describe Unsupervised learning algorithms, including Clustering and Dimensionality Reduction 
• Explain how statistical modelling relates to machine learning and how to compare them 
• Discuss real-life examples of the different ways machine learning affects society 
• Build a prediction model using classification 

Recommended Skills Before Taking this Course