# 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***Get Started**

#### 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.