In this course you will master the foundations of Machine Learning and practice building ML models with real-world case studies. We will start from scratch and explain:

• What Machine Learning is

• The Machine Learning Process of how to build a ML model

• Regression: Predict a continuous number

• Simple Linear Regression

• Ordinary Least Squares

• Multiple Linear Regression

• R-Squared

• Adjusted R-Squared

• Classification: Predict a Category / Class

• Logistic Regression

• Maximum Likelihood

• Feature Scaling

• Confusion Matrix

• Accuracy

• Clustering: Predict / Identify a Pattern

• K-Means Clustering

• The Elbow Method

• We will also do the following the three following practical activities:

• Real-World Case Study: Build a Multiple Linear Regression model

• Real-World Case Study: Build a Logistic Regression model

• Real-World Case Study: Build a K-Means Clustering model

The Course Objectives are the following:
- Get the right basics of how machine learning works and how models are built.
- Understand what is regression.
- Understand the theory behind the linear regression model.
- Know how to build, train and evaluate a linear regression model for a real-world case study.
- Understand what is classification.
- Understand the theory behind the logistic regression model.
- Understand and apply feature scaling including both normalization and standardization.
- Know how to build, train and evaluate a logistic regression model for a real-world case study.
- Understand what is clustering.
- Understand the theory behind the k-means clustering model.
- Know how to build, train and evaluate the k-means clustering model for a real-world case study.