This is the fifth course 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.

The Nuts and Bolts of Machine Learning
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The Nuts and Bolts of Machine Learning
This course is part of Google Advanced Data Analytics Professional Certificate

Instructor: Google Career Certificates
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What you'll learn
Identify characteristics of the different types of machine learning
Prepare data for machine learning models
Build and evaluate supervised and unsupervised learning models using Python
Demonstrate proper model and metric selection for a machine learning algorithm
Skills you'll gain
- Random Forest Algorithm
- Machine Learning Algorithms
- Feature Engineering
- Performance Tuning
- Model Evaluation
- Supervised Learning
- Model Optimization
- Analytics
- Model Training
- Unsupervised Learning
- Applied Machine Learning
- Machine Learning
- Statistical Machine Learning
- Predictive Modeling
- Advanced Analytics
- Decision Tree Learning
Tools you'll learn
Details to know

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Build your Machine Learning expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google

There are 5 modules in this course
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Reviewed on Oct 8, 2023
Wonderful course......THANK YOU to the instructors as they all were amazing and encouraging.
Reviewed on Jan 31, 2025
great explanation on random forest & ensembles. Now I know what random forest and boosting means
Reviewed on Jan 14, 2024
Very useful course! Concise overview of strengths and weaknesses of various cutting edge machine learning techniques.




