The Nuts and Bolts of Machine Learning
About this course
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. 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 eight 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
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What you'll learn
- Apply feature engineering techniques using Python
- Construct a Naive Bayes model
- Describe the differences between supervised and unsupervised learning
- Code a K-means algorithm in Python
- Evaluate and optimize K-means model results
- Explore decision tree models and understand their advantages
- Characterize bagging in random forest models
- Distinguish boosting in machine learning with XGBoost
- Explain model parameter tuning and its impact on performance
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