The Machine Learning Series in Python: Level 1 Course
Build a solid foundation in Machine Learning: Linear Regression, Logistic Regression and K-Means Clustering in Python
In this The Machine Learning Series in Python: Level 1 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.
Best Seller Course: Complete Machine Learning & Data Science Bootcamp 2023
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.
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