Learning Objectives

• Job-ready foundational machine learning skills in Python in just 6 weeks, including how to use Scikit-learn to implement machine learning models. 
• How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance. 
• How to build supervised learning models and implement core machine learning algorithms for classification and regression tasks, including linear regression, decision trees, and SVM. 
• How to build unsupervised learning models and implement clustering models and dimension reduction algorithms. 

Course Syllabus

• Video: Course Introduction and Welcome
• Reading: Course Overview
• Learning Objectives and Syllabus
• Plugin/Reading: Helpful Tips for Course Completion
• Grading Scheme

• An Overview of Machine Learning
• Machine Learning Model Lifecycle
• A Day in the Life of a Machine Learning Engineer
• Data Scientist vs AI Engineer
• Tools for Machine Learning
• Scikit-learn Machine Learning Ecosystem
• Practice Quiz: Intro to Machine Learning
• Module Summary and Highlights
• Graded Quiz: Intro to Machine Learning

• Introduction to Regression
• Simple Linear Regression
• Lab: Simple Linear Regression
• Multiple Linear Regression
• Lab: Multiple Linear Regression
• Non-Linear Polynomial Regression
• Practice Quiz: Linear Regression 
• Introduction to Logistic Regression
• Logistic Regression Training
• Lab: Logistic Regression
• Practice Quiz: Regression
• Module Summary
• Cheatsheet
• Graded Quiz: Regression

• Classification
• Lab: Multiclass Prediction
• Decision Trees
• Lab: Decision Trees
• Regression Trees
• Lab: Regression Trees
• Practice Quiz
• Supervised Learning with SVMs
• Lab: Credit Card Fraud Detection with Decision Trees and SVM 
• Supervised Learning with KNN
• Lab: KNN Classification
• Bias, Variance, and Ensemble Models  
• Lab: Random Forests and XGBoost
• Practice Quiz: Other Supervised Learning Models
• Module Summary
• Cheatsheet
• Graded Quiz

• Clustering Strategies and Real-World Applications
• k-means and More on k-means
• Lab: K-Means
• DBSCAN and HDBSCAN
• Lab: Comparing DBSCAN and HDBSCAN
• Practice Quiz
• Clustering, Dimension Reduction & Feature Engineering
• Dimension reduction Algorithms
• PCA Principle Component Analysis
• t-SNE and UMAP
• Practice Quiz: Advance Clustering Models
• Module Summary
• Cheatsheet
• Graded Quiz

• Classification Metrics and Evaluation Techniques
• Lab: Evaluating Classification Models
• Regression Metrics and Evaluation Techniques
• Lab: Evaluating random forest performance
• Evaluating Unsupervised Learning Models: Heuristics and Techniques
• Lab:Evaluating k-means clustering
• Practice Quiz: Evaluating ML Models
• Cross-Validation and Advanced Model Validation Techniques
• Regularization in Regression and Classification
• Lab: Regularization in Linear Regression
• Data Leakage and other Pitfalls
• Lab: Machine Learning Pipelines and GridSearchCV
• Practice Quiz
• Module Summary
• Cheatsheet
• Graded Quiz

• Course Wrap up
• Final Exam
• Final Project Scenario
• Final Project: Building a Rainfall Prediction Classifier
• Final Project Submission and Evaluation
• Congratulations and Next Steps
• Thanks from the Course Team

Recommended Skills Prior to Taking this Course