The Nuts and Bolts of Machine Learning
- Offered byCoursera
The Nuts and Bolts of Machine Learning at Coursera Overview
Duration | 33 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
The Nuts and Bolts of Machine Learning at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Coursera Labs Includes hands on learning projects. Learn more about Coursera Labs External Link
- Advanced Level
- Approx. 33 hours to complete
- English Subtitles: English
The Nuts and Bolts of Machine Learning at Coursera Course details
- This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more.
- Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
- Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
- By the end of this course, you will:
- -Apply feature engineering techniques using Python
- -Construct a Naive Bayes model
- -Describe how unsupervised learning differs from supervised learning
- -Code a K-means algorithm in Python
- -Evaluate and optimize the results of K-means model
- -Explore decision tree models, how they work, and their advantages over other types of supervised machine learning
- -Characterize bagging in machine learning, specifically for random forest models
- -Distinguish boosting in machine learning, specifically for XGBoost models
- -Explain tuning model parameters and how they affect performance and evaluation metrics
The Nuts and Bolts of Machine Learning at Coursera Curriculum
The different types of machine learning
Introduction to Course 6
Susheela: Delight people with data
Welcome to week 1
The main types of machine learning
Determine when features are infinite
Categorical features and classification models
Guide user interest with recommendation systems
Equity and fairness in machine learning
Build ethical models
Python for machine learning
Different types of Python IDEs
More about Python packages
Resources to answer programming questions
Your machine learning team
Samantha: Connect to the data professional community
Wrap-up
Helpful resources and tips
Course 6 overview
Case study: The Woobles: The power of recommendation systems to drive sales
Reference guide: Python for machine learning
Python libraries and packages
Find solutions online
Glossary terms from week 1
Test your knowledge: Introduction to machine learning
Test your knowledge: Categorical versus continuous data types and models
Test your knowledge: Machine learning in everyday life
Test your knowledge: Ethics in machine learning
Test your knowledge: Utilize the Python toolbelt for machine learning
Test your knowledge: Machine learning resources for data professionals
Weekly challenge 1
Workflow for building complex models
Welcome to week 2
PACE in machine learning
Plan for a machine learning project
Ganesh: Overcome challenges and learn from your mistakes
Analyze data for a machine learning model
Introduction to feature engineering
Solve issues that come with imbalanced datasets
Feature engineering and class balancing
Introduction to Naive Bayes
Construct a Naive Bayes model with Python
Key evaluation metrics for classification models
Wrap-up
More about planning a machine learning project
Explore feature engineering
More about imbalanced datasets
Follow-along instructions: Feature engineering with Python
Naive Bayes classifiers
Follow-along instructions: Construct a Naive Bayes model with Python
More about evaluation metrics for classification models (R-256)
Glossary terms week 2
Test your knowledge: PACE in machine learning: The plan and analyze stages
Test your knowledge: PACE in machine learning: The construct and execute stages
Weekly challenge 2
Unsupervised learning techniques
Welcome to week 3
Introduction to K-means
Use K-means for color compression with Python
Key metrics for representing K-means clustering
Inertia and silhouette coefficient metrics
Apply inertia and silhouette score with Python
Wrap-up
More about K-means
Follow-along instructions: Use K-means for color compression with Python
More about inertia and silhouette coefficient metrics
Follow-along instructions: Apply inertia and silhouette score with Python
Glossary terms from week 3
Test your knowledge: Explore unsupervised learning and K-means
Test your knowledge: Evaluate a K-means model
Weekly challenge 3
Tree-based modeling
Welcome to week 4
Tree-based modeling
Build a decision tree with Python
Tune a decision tree
Verify performance using validation
Tune and validate decision trees with Python
Bootstrap aggregation
Explore a random forest
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
Introduction to boosting: AdaBoost
Gradient boosting machines
Tune a GBM model
Build an XGBoost model with Python
Wrap-up
Explore decision trees
Follow-along instructions: Build a decision tree with Python
Hyperparameter tuning
More about validation and cross-validation
Follow-along instructions: Tune and validate decision trees with Python
Bagging: How it works and why to use it
More about random forests
Follow-along instructions: Build and cross-validate a random forest model with Python
Reference guide: Random forest tuning
Case Study: Machine learning model unearths resourcing insights for Booz Allen Hamilton
More about gradient boosting
Reference guide: XGBoost tuning
Follow-along instructions: Build an XGBoost model with Python
Glossary terms from week 4
Test your knowledge: Additional supervised learning techniques
Test your knowledge: Tune tree-based models
Test your knowledge: Bagging
Test your knowledge: Boosting
Weekly challenge 4
Course 6 end-of-course project
Welcome to week 5
Uri: Impress interviewers with your unique solutions
Introduction to Course 6 end-of-course portfolio project
End-of-course project wrap-up and tips for ongoing career success
Course wrap-up
Course 6 end-of-course portfolio project overview: Automatidata
Activity Exemplar: Create your Course 6 Automatidata project exemplar
Course 6 glossary
Get started on the next course
Activity: Create your Course 6 Automatidata project
Assess your Course 6 end-of-course project
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionThe Nuts and Bolts of Machine Learning