The Machine Learning Series in Python: Level 1

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 isThe Machine Learning Process of how to build a ML modelRegression: Predict a continuous numberSimple Linear RegressionOrdinary Least SquaresMultiple Linear RegressionR-SquaredAdjusted R-SquaredClassification: Predict a Category / ClassLogistic RegressionMaximum LikelihoodFeature ScalingConfusion MatrixAccuracyClustering: Predict / Identify a PatternK-Means ClusteringThe Elbow Method We will also do the following the three following practical activities:Real-World Case Study: Build a Multiple Linear Regression modelReal-World Case Study: Build a Logistic Regression...

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