The Nuts and Bolts of Machine Learning from Coursera

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

What's inside

Syllabus

The different types of machine learning

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.

Workflow for building complex models

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.

Unsupervised learning techniques

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.

Tree-based modeling

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.

Course 6 end-of-course project

You’ll complete the final end-of-course project by applying different machine learning models to a workplace scenario dataset.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops professional skills and deep expertise in machine learning, which is highly relevant to data science

Taught by Google employees who currently work in the field

Develops professional skills and deep expertise in advanced data analytics

Exploresthe different types of machine learning,which is standard in industry

Teaches how to apply different machine learning models to business problems, which helps learners do their jobs better

Teaches relevant skills and knowledge for data science and advanced data analytics jobs

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Nuts and Bolts of Machine Learning with these activities:

Attend a local meetup or conference on machine learning

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Foster connections with other individuals in the field and learn about current trends.

Browse courses on Professional Development

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Identify a local meetup or conference on machine learning.
Attend the event and engage in discussions with other attendees.

Review Introduction to Machine Learning

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Get a head start on understanding the fundamentals of machine learning algorithms and techniques.

View Machine Learning, revised and updated edition... on Amazon

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Read chapters 1 and 2 to gain an overview of machine learning concepts and types of machine learning.
Complete the practice exercises at the end of each chapter to reinforce your understanding.

Follow a tutorial on feature engineering for machine learning

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Bolster knowledge of feature engineering techniques and how they are applied in machine learning.

Browse courses on Feature Engineering

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Find a tutorial on feature engineering for machine learning.
Follow the tutorial and apply the techniques to a dataset.

Eight other activities

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Practice Naive Bayes Classification

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Solidify your understanding of the Naive Bayes classification algorithm.

Browse courses on Machine Learning Algorithms

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Work through the example and tutorial on Naive Bayes classification from scikit-learn.
Complete at least 5 practice problems involving Naive Bayes classification.

Create a K-Means Clustering Model

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Develop hands-on experience with unsupervised learning by creating a K-Means clustering model.

Browse courses on Data Clustering

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Choose a dataset and use Python to load it into a DataFrame.
Apply the K-Means algorithm to cluster the data.
Create a visualization that shows the resulting clusters.

Attend a Machine Learning Workshop

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Gain practical experience and deepen your understanding of machine learning concepts through hands-on workshops.

Browse courses on Machine Learning

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Identify and register for a machine learning workshop that aligns with your interests.
Attend the workshop and actively participate in the activities and discussions.

Explore Random Forest Models

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Expand your knowledge of machine learning algorithms by learning about the strengths and use cases of random forests.

Browse courses on Ensemble Learning

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Follow a guided tutorial on creating and evaluating random forest models using Python.
Experiment with different parameters to observe how they impact the model's performance.

Build a machine learning model for a personalized recommendation system

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Instill confidence in students' ability to apply principles learnt in the course to real-world problems, and promote their problem-solving capabilities.

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Choose a dataset of user interactions with a product or service.
Preprocess the data to prepare it for modeling.
Train and evaluate different machine learning models to build the recommender system.
Deploy the recommender system and monitor its performance.

Write a blog post about a recent advancement in machine learning

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Encourage students to stay up-to-date with trends in the field and refine their research abilities.

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Research a specific advancement in machine learning.
Write a blog post explaining the advancement in clear and concise language.
Share the blog post on social media or a personal website.

Develop a Decision Tree Model

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Put your knowledge of supervised learning into practice by building a decision tree model.

Browse courses on Machine Learning Models

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Select a dataset and import it into Python.
Train a decision tree model using scikit-learn.
Evaluate the performance of the model and make adjustments as needed.
Create a presentation or report to explain your findings.

Contribute to a Machine Learning Project

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Get involved in the machine learning community by contributing to open-source projects.

Browse courses on Machine Learning

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Identify an open-source machine learning project that aligns with your skills and interests.
Review the project's documentation and contribute code, documentation, or other improvements.

Career center

Learners who complete The Nuts and Bolts of Machine Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They work on a variety of projects, such as developing predictive models, identifying customer trends, and optimizing business processes. The Nuts and Bolts of Machine Learning course can help Machine Learning Engineers build a strong foundation in machine learning, which is essential for success in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Machine Learning Engineers develop and implement machine learning models to solve complex business problems.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning and other advanced techniques to extract insights from data. They work on a variety of projects, such as developing predictive models, identifying customer trends, and optimizing business processes. The Nuts and Bolts of Machine Learning course can help Data Scientists build a strong foundation in machine learning, which is essential for success in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Data Scientists develop and implement machine learning models to solve complex business problems.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts work with large datasets to help companies make informed decisions. They use their skills in statistics and programming to clean, analyze, and interpret data. The Nuts and Bolts of Machine Learning course can help Data Analysts build a strong foundation in machine learning, which is an essential skill for success in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Data Analysts develop and implement machine learning models to solve business problems.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They work on a variety of projects, such as developing trading strategies, managing risk, and forecasting financial trends. The Nuts and Bolts of Machine Learning course can help Quantitative Analysts build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Quantitative Analysts develop and implement machine learning models to solve complex financial problems.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex business problems. They work on a variety of projects, such as optimizing supply chains, scheduling production, and managing inventory. The Nuts and Bolts of Machine Learning course can help Operations Research Analysts build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Operations Research Analysts develop and implement machine learning models to solve complex business problems.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

Business Analysts use data to help businesses make informed decisions. They work on a variety of projects, such as developing business plans, evaluating marketing campaigns, and improving customer service. The Nuts and Bolts of Machine Learning course can help Business Analysts build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Business Analysts develop and implement machine learning models to solve complex business problems.

See salaries and explore the career path for Business Analyst

Market Research Analyst

Market Research Analysts use data to understand consumer behavior and trends. They work on a variety of projects, such as developing new products, evaluating marketing campaigns, and identifying new target markets. The Nuts and Bolts of Machine Learning course can help Market Research Analysts build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Market Research Analysts develop and implement machine learning models to solve complex business problems.

See salaries and explore the career path for Market Research Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work on a variety of tasks, such as defining product requirements, managing product development, and marketing the product to customers. The Nuts and Bolts of Machine Learning course can help Product Managers build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Product Managers develop and implement machine learning models to solve complex business problems.

See salaries and explore the career path for Product Manager

Software Engineer

Software Engineers design, develop, and test software applications. They work on a variety of projects, such as developing new software products, maintaining existing software systems, and fixing software bugs. The Nuts and Bolts of Machine Learning course can help Software Engineers build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Software Engineers develop and implement machine learning models to solve complex software problems.

See salaries and explore the career path for Software Engineer

Data Architect

Data Architects design and manage data systems. They work on a variety of projects, such as developing new data warehouses, migrating data to new systems, and improving data quality. The Nuts and Bolts of Machine Learning course can help Data Architects build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Data Architects develop and implement machine learning models to solve complex data problems.

See salaries and explore the career path for Data Architect

Statistician

Statisticians use mathematical and statistical models to analyze data. They work on a variety of projects, such as developing new statistical methods, conducting research studies, and providing statistical consulting. The Nuts and Bolts of Machine Learning course can help Statisticians build a strong foundation in machine learning, which is a rapidly growing field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Statisticians develop and implement machine learning models to solve complex problems.

See salaries and explore the career path for Statistician

Actuary

Actuaries use mathematical and statistical models to assess risk. They work on a variety of projects, such as developing new insurance products, pricing insurance policies, and managing financial risk. The Nuts and Bolts of Machine Learning course can help Actuaries build a strong foundation in machine learning, which is an increasingly important skill in this field. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation. This knowledge can help Actuaries develop and implement machine learning models to solve complex risk problems.

See salaries and explore the career path for Actuary

Risk Manager

Risk Managers identify, assess, and manage risk. They work on a variety of projects, such as developing risk management plans, conducting risk assessments, and implementing risk mitigation strategies. The Nuts and Bolts of Machine Learning course may be useful for Risk Managers, as it can help them build a strong foundation in machine learning. Machine learning is an increasingly important tool for risk management, as it can be used to develop models to predict and mitigate risk.

See salaries and explore the career path for Risk Manager

Financial Analyst

Financial Analysts provide financial advice and guidance to individuals and organizations. They work on a variety of projects, such as developing investment strategies, analyzing financial data, and making investment recommendations. The Nuts and Bolts of Machine Learning course may be useful for Financial Analysts, as it can help them build a strong foundation in machine learning. Machine learning is an increasingly important tool for financial analysis, as it can be used to develop models to predict financial trends and make investment decisions.

See salaries and explore the career path for Financial Analyst

Auditor

Auditors examine financial records and statements to ensure that they are accurate and compliant with regulations. The Nuts and Bolts of Machine Learning course may be useful for Auditors, as it can help them build a strong foundation in machine learning. Machine learning is an increasingly important tool for auditing, as it can be used to develop models to detect fraud and errors in financial data.

See salaries and explore the career path for Auditor

The Nuts and Bolts of Machine Learning

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