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
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
Requirements
Every single line of code will be fully explained so there are no prerequisites for coding skills
This is a foundational course, so no prior knowledge of Data Science is required
Some high-school level mathematics knowledge is recommended but not required
We use Google Colab for coding in Python which is very intuitive, but you can also use Jupyter or another IDE
Description
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.

Who this course is for:
Anyone interested in Data Science
Anyone who wants to become a Data Scientist
Anyone interested in Machine Learning
Anyone who wants to become a ML or AI engineer
Data Science professionals
Machine Learning professionals
Anyone who wants to add Machine Learning to their CV or career toolkit

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