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
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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
- Analytics
- Model Training
- Supervised Learning
- Statistical Machine Learning
- Advanced Analytics
- Machine Learning
- Random Forest Algorithm
- Model Evaluation
- Feature Engineering
- Applied Machine Learning
- Predictive Modeling
- Machine Learning Algorithms
- Unsupervised Learning
- Decision Tree Learning
- Performance Tuning
- Model Optimization
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
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
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
You’ll start by exploring the basic concepts of machine learning and the role of machine learning in data science. Then, you’ll review the four main types of machine learning: supervised, unsupervised, reinforcement, and deep learning.
What's included
16 videos7 readings7 assignments4 plugins
16 videos•Total 56 minutes
- Introduction to Course 5•4 minutes
- Susheela: Delight people with data•3 minutes
- Welcome to module 1•1 minute
- The main types of machine learning•7 minutes
- Determine when features are infinite•3 minutes
- Categorical features and classification models•4 minutes
- Guide user interest with recommendation systems•7 minutes
- Equity and fairness in machine learning•3 minutes
- Build ethical models•4 minutes
- Python for machine learning•4 minutes
- Different types of Python IDEs•2 minutes
- More about Python packages•3 minutes
- Resources to answer programming questions•3 minutes
- Your machine learning team•2 minutes
- Samantha: Connect to the data professional community•3 minutes
- Wrap-up•2 minutes
7 readings•Total 110 minutes
- Helpful resources and tips•8 minutes
- Course 5 overview•12 minutes
- Case study: The Woobles: The power of recommendation systems to drive sales•20 minutes
- Reference guide: Python for machine learning•20 minutes
- Python libraries and packages•20 minutes
- Find solutions online•20 minutes
- Glossary terms from module 1•10 minutes
7 assignments•Total 82 minutes
- Module 1 challenge•50 minutes
- Test your knowledge: Introduction to machine learning•6 minutes
- Test your knowledge: Categorical versus continuous data types and models•4 minutes
- Test your knowledge: Machine learning in everyday life•6 minutes
- Test your knowledge: Ethics in machine learning•4 minutes
- Test your knowledge: Utilize the Python toolbelt for machine learning•6 minutes
- Test your knowledge: Machine learning resources for data professionals•6 minutes
4 plugins•Total 45 minutes
- Identify: Machine learning solutions•10 minutes
- [Turkish learners ONLY] Identify: Machine learning solutions - Türkçe•10 minutes
- Categorize: Data science tools •10 minutes
- [Turkish learners ONLY] Categorize: Data science tools - Türkçe•15 minutes
You’ll learn how data professionals use a structured workflow for machine learning. You'll identify the main steps of the workflow and the importance of each step in the overall process. Then, you'll learn how to apply specific machine learning models to business problems.
What's included
12 videos6 readings3 assignments6 ungraded labs
12 videos•Total 46 minutes
- Welcome to module 2•1 minute
- PACE in machine learning•1 minute
- Plan for a machine learning project•2 minutes
- Ganesh: Overcome challenges and learn from your mistakes•3 minutes
- Analyze data for a machine learning model•3 minutes
- Introduction to feature engineering•5 minutes
- Solve issues that come with imbalanced datasets•4 minutes
- Feature engineering and class balancing•8 minutes
- Introduction to Naive Bayes•4 minutes
- Construct a Naive Bayes model with Python•10 minutes
- Key evaluation metrics for classification models•3 minutes
- Wrap-up•1 minute
6 readings•Total 44 minutes
- More about planning a machine learning project•8 minutes
- Explore feature engineering•8 minutes
- More about imbalanced datasets•8 minutes
- Naive Bayes classifiers•8 minutes
- More about evaluation metrics for classification models•8 minutes
- Glossary terms from module 2•4 minutes
3 assignments•Total 52 minutes
- Module 2 challenge •40 minutes
- Test your knowledge: PACE in machine learning: The plan and analyze stages•6 minutes
- Test your knowledge: PACE in machine learning: The construct and execute stages•6 minutes
6 ungraded labs•Total 200 minutes
- Annotated follow-along guide: Feature engineering with Python•20 minutes
- Activity: Perform feature engineering•60 minutes
- Exemplar: Perform feature engineering•20 minutes
- Annotated follow-along guide: Construct a Naive Bayes model with Python•20 minutes
- Activity: Build a Naive Bayes model•60 minutes
- Exemplar: Build a Naive Bayes model•20 minutes
You’ll learn more about one of the major types of machine learning: unsupervised learning. You'll begin by exploring the difference between supervised and unsupervised techniques and the benefits and uses of each approach. Then, you’ll learn how to apply two unsupervised machine learning models: clustering and K-means.
What's included
7 videos4 readings3 assignments4 ungraded labs
7 videos•Total 32 minutes
- Welcome to module 3•2 minutes
- Introduction to K-means•5 minutes
- Use K-means for color compression with Python•7 minutes
- Key metrics for representing K-means clustering•4 minutes
- Inertia and silhouette coefficient metrics•4 minutes
- Apply inertia and silhouette score with Python•9 minutes
- Wrap-up•1 minute
4 readings•Total 24 minutes
- More about K-means•8 minutes
- Clustering beyond K-means•4 minutes
- More about inertia and silhouette coefficient metrics•8 minutes
- Glossary terms from module 3•4 minutes
3 assignments•Total 52 minutes
- Module 3 challenge•40 minutes
- Test your knowledge: Explore unsupervised learning and K-means•6 minutes
- Test your knowledge: Evaluate a K-means model•6 minutes
4 ungraded labs•Total 120 minutes
- Annotated follow-along guide: Use K-means for color compression with Python•20 minutes
- Annotated follow-along resource: Apply inertia and silhouette score with Python•20 minutes
- Activity: Build a K-means model•60 minutes
- Exemplar: Build a K-means model•20 minutes
Next, you’ll focus on supervised learning. You’ll learn how to test and validate the performance of supervised machine learning models such as decision tree, random forest, and gradient boosting.
What's included
17 videos11 readings5 assignments10 ungraded labs2 plugins
17 videos•Total 77 minutes
- Welcome to module 4•2 minutes
- Daisy: Highlight both technical and people skills•3 minutes
- Tree-based modeling •4 minutes
- Build a decision tree with Python •6 minutes
- Tune a decision tree•5 minutes
- Verify performance using validation •3 minutes
- Tune and validate decision trees with Python •5 minutes
- Bootstrap aggregation•5 minutes
- Explore a random forest•3 minutes
- Tuning a random forest •4 minutes
- Build and cross-validate a random forest model with Python•5 minutes
- Build and validate a random forest model using a validation data set•8 minutes
- Introduction to boosting: AdaBoost •5 minutes
- Gradient boosting machines•5 minutes
- Tune a GBM model •5 minutes
- Build an XGBoost model with Python •7 minutes
- Wrap-up•2 minutes
11 readings•Total 84 minutes
- Explore decision trees•8 minutes
- Hyperparameter tuning•8 minutes
- More about validation and cross-validation•8 minutes
- Bagging: How it works and why to use it•8 minutes
- More about random forests•8 minutes
- Reference guide: Random forest tuning•8 minutes
- Reference guide: Validation and cross-validation•8 minutes
- Case Study: Machine learning model unearths resourcing insights for Booz Allen Hamilton•8 minutes
- More about gradient boosting•8 minutes
- Reference guide: XGBoost tuning•8 minutes
- Glossary terms from module 4 •4 minutes
5 assignments•Total 80 minutes
- Module 4 challenge•50 minutes
- Test your knowledge: Additional supervised learning techniques•8 minutes
- Test your knowledge: Tune tree-based models•8 minutes
- Test your knowledge: Bagging •8 minutes
- Test your knowledge: Boosting•6 minutes
10 ungraded labs•Total 320 minutes
- Annotated follow-along guide: Build a decision tree•20 minutes
- Annotated follow-along guide: Tune and validate decision trees•20 minutes
- Activity: Build a decision tree•60 minutes
- Exemplar: Build a decision tree•20 minutes
- Annotated follow-along guide: Build and cross-validate a random forest model•20 minutes
- Activity: Build a random forest model•60 minutes
- Exemplar: Build a random forest model•20 minutes
- Annotated follow-along guide: Build an XGBoost model with Python•20 minutes
- Activity: Build an XGBoost model•60 minutes
- Exemplar: Build an XGBoost model•20 minutes
2 plugins•Total 20 minutes
- Identify: Parts of the decision tree •10 minutes
- [Turkish learners ONLY] Identify: Parts of the decision tree - Türkçe•10 minutes
You’ll complete the final end-of-course project by applying different machine learning models to a workplace scenario dataset.
What's included
5 videos10 readings4 assignments6 ungraded labs
5 videos•Total 12 minutes
- Welcome to module 5•2 minutes
- Uri: Impress interviewers with your unique solutions•2 minutes
- Introduction to your Course 5 end-of-course portfolio project•2 minutes
- End-of-course project wrap-up and tips for ongoing career success•3 minutes
- Course wrap-up•3 minutes
10 readings•Total 52 minutes
- Explore your Course 5 workplace scenarios•8 minutes
- Course 5 end-of-course portfolio project overview: Automatidata•8 minutes
- Activity Exemplar: Create your Course 5 Automatidata project exemplar •4 minutes
- Course 5 end-of-course portfolio project overview: TikTok•8 minutes
- Activity Exemplar: Create your Course 5 TikTok project exemplar •4 minutes
- Course 5 end-of-course portfolio project overview: Waze•8 minutes
- Activity Exemplar: Create your Course 5 Waze project exemplar •4 minutes
- Course 5 glossary•2 minutes
- Reflect and connect with peers•2 minutes
- Get started on the next course•4 minutes
4 assignments•Total 165 minutes
- Assess your Course 5 end-of-course project•75 minutes
- Activity: Create your Course 5 Automatidata project•30 minutes
- Activity: Create your Course 5 TikTok project•30 minutes
- Activity: Create your Course 5 Waze project•30 minutes
6 ungraded labs•Total 360 minutes
- Activity: Create your Course 5 Automatidata project lab•60 minutes
- Exemplar: Course 5 Automatidata project exemplar lab•60 minutes
- Activity: Course 5 TikTok project lab•60 minutes
- Exemplar: Course 5 TikTok project exemplar lab•60 minutes
- Activity: Course 5 Waze project lab•60 minutes
- Exemplar: Course 5 Waze project exemplar lab•60 minutes
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Reviewed on Feb 6, 2024
I’m so grateful for the excellence, well crafted and clearly delivered career-oriented course you have offered.
Reviewed on Jan 14, 2024
Very useful course! Concise overview of strengths and weaknesses of various cutting edge machine learning techniques.
Reviewed on May 17, 2024
This course helped me take my ML skills to another level entirely, I would certainly recommend it to anyone looking for a breakthrough in data analytics.
Frequently asked questions
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.
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