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The Nuts and Bolts of Machine Learning

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The Nuts and Bolts of Machine Learning

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Gain insight into a topic and learn the fundamentals.
4.8

628 reviews

Advanced level
Designed for those already in the industry
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace
98%
Most learners liked this course

Gain insight into a topic and learn the fundamentals.
4.8

628 reviews

Advanced level
Designed for those already in the industry
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace
98%
Most learners liked this course

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

Details to know

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Assessments

22 assignments

Taught in English

Build your Machine Learning expertise

This course is part of the Google Advanced Data Analytics Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • 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 videosTotal 56 minutes
  • Introduction to Course 54 minutes
  • Susheela: Delight people with data3 minutes
  • Welcome to module 11 minute
  • The main types of machine learning7 minutes
  • Determine when features are infinite3 minutes
  • Categorical features and classification models4 minutes
  • Guide user interest with recommendation systems7 minutes
  • Equity and fairness in machine learning3 minutes
  • Build ethical models4 minutes
  • Python for machine learning4 minutes
  • Different types of Python IDEs2 minutes
  • More about Python packages3 minutes
  • Resources to answer programming questions3 minutes
  • Your machine learning team2 minutes
  • Samantha: Connect to the data professional community3 minutes
  • Wrap-up2 minutes
7 readingsTotal 110 minutes
  • Helpful resources and tips8 minutes
  • Course 5 overview12 minutes
  • Case study: The Woobles: The power of recommendation systems to drive sales20 minutes
  • Reference guide: Python for machine learning20 minutes
  • Python libraries and packages20 minutes
  • Find solutions online20 minutes
  • Glossary terms from module 110 minutes
7 assignmentsTotal 82 minutes
  • Module 1 challenge50 minutes
  • Test your knowledge: Introduction to machine learning6 minutes
  • Test your knowledge: Categorical versus continuous data types and models4 minutes
  • Test your knowledge: Machine learning in everyday life6 minutes
  • Test your knowledge: Ethics in machine learning4 minutes
  • Test your knowledge: Utilize the Python toolbelt for machine learning6 minutes
  • Test your knowledge: Machine learning resources for data professionals6 minutes
4 pluginsTotal 45 minutes
  • Identify: Machine learning solutions10 minutes
  • [Turkish learners ONLY] Identify: Machine learning solutions - Türkçe10 minutes
  • Categorize: Data science tools 10 minutes
  • [Turkish learners ONLY] Categorize: Data science tools - Türkçe15 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 videosTotal 46 minutes
  • Welcome to module 21 minute
  • PACE in machine learning1 minute
  • Plan for a machine learning project2 minutes
  • Ganesh: Overcome challenges and learn from your mistakes3 minutes
  • Analyze data for a machine learning model3 minutes
  • Introduction to feature engineering5 minutes
  • Solve issues that come with imbalanced datasets4 minutes
  • Feature engineering and class balancing8 minutes
  • Introduction to Naive Bayes4 minutes
  • Construct a Naive Bayes model with Python10 minutes
  • Key evaluation metrics for classification models3 minutes
  • Wrap-up1 minute
6 readingsTotal 44 minutes
  • More about planning a machine learning project8 minutes
  • Explore feature engineering8 minutes
  • More about imbalanced datasets8 minutes
  • Naive Bayes classifiers8 minutes
  • More about evaluation metrics for classification models8 minutes
  • Glossary terms from module 24 minutes
3 assignmentsTotal 52 minutes
  • Module 2 challenge 40 minutes
  • Test your knowledge: PACE in machine learning: The plan and analyze stages6 minutes
  • Test your knowledge: PACE in machine learning: The construct and execute stages6 minutes
6 ungraded labsTotal 200 minutes
  • Annotated follow-along guide: Feature engineering with Python20 minutes
  • Activity: Perform feature engineering60 minutes
  • Exemplar: Perform feature engineering20 minutes
  • Annotated follow-along guide: Construct a Naive Bayes model with Python20 minutes
  • Activity: Build a Naive Bayes model60 minutes
  • Exemplar: Build a Naive Bayes model20 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 videosTotal 32 minutes
  • Welcome to module 32 minutes
  • Introduction to K-means5 minutes
  • Use K-means for color compression with Python7 minutes
  • Key metrics for representing K-means clustering4 minutes
  • Inertia and silhouette coefficient metrics4 minutes
  • Apply inertia and silhouette score with Python9 minutes
  • Wrap-up1 minute
4 readingsTotal 24 minutes
  • More about K-means8 minutes
  • Clustering beyond K-means4 minutes
  • More about inertia and silhouette coefficient metrics8 minutes
  • Glossary terms from module 34 minutes
3 assignmentsTotal 52 minutes
  • Module 3 challenge40 minutes
  • Test your knowledge: Explore unsupervised learning and K-means6 minutes
  • Test your knowledge: Evaluate a K-means model6 minutes
4 ungraded labsTotal 120 minutes
  • Annotated follow-along guide: Use K-means for color compression with Python20 minutes
  • Annotated follow-along resource: Apply inertia and silhouette score with Python20 minutes
  • Activity: Build a K-means model60 minutes
  • Exemplar: Build a K-means model20 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 videosTotal 77 minutes
  • Welcome to module 42 minutes
  • Daisy: Highlight both technical and people skills3 minutes
  • Tree-based modeling 4 minutes
  • Build a decision tree with Python 6 minutes
  • Tune a decision tree5 minutes
  • Verify performance using validation 3 minutes
  • Tune and validate decision trees with Python 5 minutes
  • Bootstrap aggregation5 minutes
  • Explore a random forest3 minutes
  • Tuning a random forest 4 minutes
  • Build and cross-validate a random forest model with Python5 minutes
  • Build and validate a random forest model using a validation data set8 minutes
  • Introduction to boosting: AdaBoost 5 minutes
  • Gradient boosting machines5 minutes
  • Tune a GBM model 5 minutes
  • Build an XGBoost model with Python 7 minutes
  • Wrap-up2 minutes
11 readingsTotal 84 minutes
  • Explore decision trees8 minutes
  • Hyperparameter tuning8 minutes
  • More about validation and cross-validation8 minutes
  • Bagging: How it works and why to use it8 minutes
  • More about random forests8 minutes
  • Reference guide: Random forest tuning8 minutes
  • Reference guide: Validation and cross-validation8 minutes
  • Case Study: Machine learning model unearths resourcing insights for Booz Allen Hamilton8 minutes
  • More about gradient boosting8 minutes
  • Reference guide: XGBoost tuning8 minutes
  • Glossary terms from module 4 4 minutes
5 assignmentsTotal 80 minutes
  • Module 4 challenge50 minutes
  • Test your knowledge: Additional supervised learning techniques8 minutes
  • Test your knowledge: Tune tree-based models8 minutes
  • Test your knowledge: Bagging 8 minutes
  • Test your knowledge: Boosting6 minutes
10 ungraded labsTotal 320 minutes
  • Annotated follow-along guide: Build a decision tree20 minutes
  • Annotated follow-along guide: Tune and validate decision trees20 minutes
  • Activity: Build a decision tree60 minutes
  • Exemplar: Build a decision tree20 minutes
  • Annotated follow-along guide: Build and cross-validate a random forest model20 minutes
  • Activity: Build a random forest model60 minutes
  • Exemplar: Build a random forest model20 minutes
  • Annotated follow-along guide: Build an XGBoost model with Python20 minutes
  • Activity: Build an XGBoost model60 minutes
  • Exemplar: Build an XGBoost model20 minutes
2 pluginsTotal 20 minutes
  • Identify: Parts of the decision tree 10 minutes
  • [Turkish learners ONLY] Identify: Parts of the decision tree - Türkçe10 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 videosTotal 12 minutes
  • Welcome to module 52 minutes
  • Uri: Impress interviewers with your unique solutions2 minutes
  • Introduction to your Course 5 end-of-course portfolio project2 minutes
  • End-of-course project wrap-up and tips for ongoing career success3 minutes
  • Course wrap-up3 minutes
10 readingsTotal 52 minutes
  • Explore your Course 5 workplace scenarios8 minutes
  • Course 5 end-of-course portfolio project overview: Automatidata8 minutes
  • Activity Exemplar: Create your Course 5 Automatidata project exemplar 4 minutes
  • Course 5 end-of-course portfolio project overview: TikTok8 minutes
  • Activity Exemplar: Create your Course 5 TikTok project exemplar 4 minutes
  • Course 5 end-of-course portfolio project overview: Waze8 minutes
  • Activity Exemplar: Create your Course 5 Waze project exemplar 4 minutes
  • Course 5 glossary2 minutes
  • Reflect and connect with peers2 minutes
  • Get started on the next course4 minutes
4 assignmentsTotal 165 minutes
  • Assess your Course 5 end-of-course project75 minutes
  • Activity: Create your Course 5 Automatidata project30 minutes
  • Activity: Create your Course 5 TikTok project30 minutes
  • Activity: Create your Course 5 Waze project30 minutes
6 ungraded labsTotal 360 minutes
  • Activity: Create your Course 5 Automatidata project lab60 minutes
  • Exemplar: Course 5 Automatidata project exemplar lab60 minutes
  • Activity: Course 5 TikTok project lab60 minutes
  • Exemplar: Course 5 TikTok project exemplar lab60 minutes
  • Activity: Course 5 Waze project lab60 minutes
  • Exemplar: Course 5 Waze project exemplar lab60 minutes

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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.

The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data analy

During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.

This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools. To succeed in this certificate program, you should already know about key foundational aspects of data analysis, such as the data analysis process and data life cycle, databases and general database elements, programming language basics, and project stakeholders. 

The content in this certificate program builds upon data analytics concepts taught in the Google Data Analytics Certificate. These include key foundational aspects of data analysis such as the data analysis process and data life cycle, databases and general database elements such as primary and foreign keys, SQL and programming language basics, and project stakeholders. If you haven’t completed that program or if you’re unsure whether you have the necessary prerequisites, you can take an ungraded assessment in Course 1 Module 1 of this certificate to evaluate your readiness.

You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analytics.

We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Financial aid available,