The Nuts and Bolts of Machine Learning

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Learn how machine learning uses algorithms and statistics to find patterns in data, helping professionals solve complex problems and make accurate predictions. Learn about supervised and unsupervised machine learning, and apply models like Naive Bayes, decision tree, and random forest. This is the sixth course in the Google Advanced Data Analytics Certificate, a series designed to prepare you for an advanced data analytics role.

Syllabus

The different types of machine learning

Introduction to Course 5
Susheela: Delight people with data
Course 5 overview
Welcome to module 1
The main types of machine learning
Practice Quiz: Test your knowledge: Introduction to machine learning
Determine when features are infinite
Categorical features and classification models
Practice Quiz: Test your knowledge: Categorical versus continuous data types and models
Guide user interest with recommendation systems
Case study: The Woobles: The power of recommendation systems to drive sales
Practice Quiz: Test your knowledge: Machine learning in everyday life
Equity and fairness in machine learning
Build ethical models
Practice Quiz: Test your knowledge: Ethics in machine learning
Python for machine learning
Different types of Python IDEs
Reference guide: Python for machine learning
More about Python packages
Python libraries and packages
Practice Quiz: Test your knowledge: Utilize the Python toolbelt for machine learning
Resources to answer programming questions
Find solutions online
Your machine learning team
Samantha: Connect to the data professional community
Practice Quiz: Test your knowledge: Machine learning resources for data professionals
Wrap-up
Glossary terms from module 1
Graded Quiz: Module 1 challenge

Workflow for building complex models

Welcome to module 2
PACE in machine learning
Plan for a machine learning project
More about planning a machine learning project
Ganesh: Overcome challenges and learn from your mistakes
Analyze data for a machine learning model
Introduction to feature engineering
Explore feature engineering
Solve issues that come with imbalanced datasets
More about imbalanced datasets
Feature engineering and class balancing
Practice Quiz: Test your knowledge: PACE in machine learning: The plan and analyze stages
Introduction to Naive Bayes
Naive Bayes classifiers
Construct a Naive Bayes model with Python
Key evaluation metrics for classification models
More about evaluation metrics for classification models
Activity: Workflow for building complex models - Part A
Activity: Workflow for building complex models - Part B
Exemplar: Workflow for building complex models
Practice Quiz: Test your knowledge: PACE in machine learning: The construct and execute stages
Wrap-up
Glossary terms from module 2
Graded Quiz: Module 2 challenge

Unsupervised learning techniques

Welcome to module 3
Introduction to K-means
More about K-means
Use K-means for color compression with Python
Clustering beyond K-means
Practice Quiz: Test your knowledge: Explore unsupervised learning and K-means
Key metrics for representing K-means clustering
Inertia and silhouette coefficient metrics
More about inertia and silhouette coefficient metrics
Apply inertia and silhouette score with Python
Unsupervised learning techniques
Activity: Unsupervised learning techniques
Exemplar: Unsupervised learning techniques
Practice Quiz: Test your knowledge: Evaluate a K-means model
Wrap-up
Glossary terms from module 3
Graded Quiz: Module 3 challenge

Tree-based modeling

Welcome to module 4
Daisy: Highlight both technical and people skills
Tree-based modeling
Explore decision trees
Build a decision tree with Python
Practice Quiz: Test your knowledge: Additional supervised learning techniques
Tune a decision tree
Hyperparameter tuning
Verify performance using validation
More about validation and cross-validation
Tune and validate decision trees with Python
Tree-based modeling
Activity: Tree-based modeling - Part A
Exemplar: Tree-based modeling - Part A
Practice Quiz: Test your knowledge: Tune tree-based models
Bootstrap aggregation
Bagging: How it works and why to use it
Explore a random forest
More about random forests
Tuning a random forest
Build and cross-validate a random forest model with Python
Build and validate a random forest model using a validation data set
Reference guide: Random forest tuning
Reference guide: Validation and cross-validation
Case Study: Machine learning model unearths resourcing insights for Booz Allen Hamilton
Activity: Tree-based modeling - Part B
Exemplar: Tree-based modeling - Part B
Practice Quiz: Test your knowledge: Bagging
Introduction to boosting: AdaBoost
Gradient boosting machines
More about gradient boosting
Tune a GBM model
Reference guide: XGBoost tuning
Build an XGBoost model with Python
Activity: Tree-based modeling - Part C
Exemplar: Tree-based modeling - Part C
Practice Quiz: Test your knowledge: Boosting
Wrap-up
Glossary terms from module 4
Graded Quiz: Module 4 challenge