The Machine Learning in Python Series: Level 1 (Beginners)

The Machine Learning in Python Series: Level 1 (Beginners)

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

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What you'll learn

• Machine Learning
• The Machine Learning Process
• Regression
• Ordinary Least Squares
• Simple Linear Regression
• Multiple Linear Regression
• R-Squared
• Adjusted R-Squared
• Classification
• Maximum Likelihood
• Feature Scaling
• Confusion Matrix
• Accuracy
• Clustering
• K-Means Clustering
• The Elbow Method
• K-Means++
• Build Machine Learning models in Python
• Make Predictions

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.