Build a solid foundation in Machine Learning: Linear Regression, Logistic Regression and K-Means Clustering in Python
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.
