Build Regression, Classification, and Clustering Models from Coursera

In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business.

This third course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate introduces you to some of the major machine learning algorithms that are used to solve the two most common supervised problems: regression and classification, and one of the most common unsupervised problems: clustering. You'll build multiple models to address each of these problems using the machine learning workflow you learned about in the previous course.

Ultimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

What's inside

Syllabus

Build Linear Regression Models Using Linear Algebra

In the preceding course, you went through the overall machine learning workflow from start to finish. Now it's time to start digging into the algorithms that make up machine learning. This will help you select the most appropriate algorithm(s) for your own purposes, as well as how best to apply them to solve a problem. A good place to start is with simple linear regression.

Build Regularized and Iterative Linear Regression Models

The simple model you created earlier works well in many cases, but that doesn't mean it's the optimal approach. Linear regression can be enhanced by the process of regularization, which will often improve the skill of your machine learning model. In addition, an iterative approach to regression can take over where the closed-form solution falls short. In this module, you'll apply both techniques.

Train Classification Models

Besides linear regression, the other major type of supervised machine learning outcome is classification. To begin with, you'll train some binary classification models using a few different algorithms. Then, you'll train a model to handle cases in which there are multiple ways to classify a data example. Each algorithm may be ideal for solving a certain type of classification problem, so you need to be aware of how they differ.

Evaluate and Tune Classification Models

It's not enough to just train a model you think is best, and then call it a day. Unless you're using a very simple dataset or you get lucky, the default parameters aren't going to give you the best possible model for solving the problem. So, in this module, you'll evaluate your classification models to see how they're performing, then you'll attempt to improve their skill.

Build Clustering Models

You've built models to tackle linear regression problems and classification problems. One of the other major machine learning tasks that you might want to engage in is clustering, a form of unsupervised learning. In this module, you'll see how a machine learning model can help you identify useful patterns even when the data you have to work with isn't labeled.

Apply What You've Learned

You'll work on a project in which you'll apply your knowledge of the material in this course to practical scenarios.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops models and algorithms for solving regression and classification problems, foundational skills for data scientists

Introduces popular machine learning algorithms used in industry, making it relevant for practitioners

Builds upon the foundational concepts of machine learning established in the previous course in the series, providing a progressive learning experience

Taught by Anastas Stoyanovsky, an experienced instructor with industry expertise, ensuring up-to-date and practical knowledge

Involves hands-on project work, allowing learners to apply the concepts and techniques to real-world scenarios

Reviews summary

Limited content and frustrating

According to students, this course has significant limitations. Students report that the course presented them with barriers from successfully progressing. Students also found peer grading took an excessive amount of time to complete, which left them paying more for courses they had already completed work for. Learners may want to look for alternative courses that do not have restrictive deadlines and more efficiently assist students through their grading.

Course did not allow learner to progress seamlessly.

"Course refused to let me progress beyond Week 1. No reason given."

Peer grading caused substantial delays and extra cost for learner.

"peer grading took much longer than any other course I've taken on Coursera leaving me with open courses that charged me more money instead of acknowledging the work had been completed and letting me complete the course."

"Just exists to make money, do not take this course if you have a deadline or a budget, expect to not get a peer graded review or grade for several days or weeks."

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 Build Regression, Classification, and Clustering Models with these activities:

Find a mentor in the field of machine learning

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Finding a mentor in the field of machine learning will provide you with guidance and support throughout your learning journey.

Browse courses on Machine Learning

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Attend meetups and conferences
Reach out to professionals on LinkedIn

Attend networking events for machine learning professionals

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Attending networking events for machine learning professionals will help you connect with others in the field and learn about new opportunities.

Browse courses on Machine Learning

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Find networking events in your area
Prepare your elevator pitch

Gather resources on machine learning algorithms

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Gathering resources on machine learning algorithms will help you build a strong foundation for your learning.

Browse courses on Machine Learning

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Create a list of online resources (e.g., articles, tutorials, videos)
Organize the resources by topic

Four other activities

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Show all seven activities

Practice building simple linear regression models

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Practice building simple linear regression models will reinforce your understanding of the algorithm and its application.

Browse courses on Linear Regression

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Find a dataset with a continuous target variable
Build a linear regression model using a library like scikit-learn
Evaluate the model's performance

Practice training classification models

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Practice training classification models will help you understand the different algorithms and their application.

Browse courses on Classification

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Find a dataset with a categorical target variable
Train a logistic regression model using a library like scikit-learn
Evaluate the model's performance

Follow tutorials on regularization techniques

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Following tutorials on regularization techniques will help you improve the performance of your machine learning models.

Browse courses on Regularization

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Find tutorials on L1 and L2 regularization
Implement regularization techniques in your linear regression models

Start a project on applying machine learning to a real-world problem

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Starting a project on applying machine learning to a real-world problem will help you apply your knowledge and skills to a practical scenario.

Browse courses on Machine Learning

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Identify a problem that can be solved using machine learning
Collect and preprocess data
Train and evaluate a machine learning model
Deploy the model and monitor its performance

Career center

Learners who complete Build Regression, Classification, and Clustering Models will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, deploy, and maintain machine learning models. They work closely with Data Scientists to identify business problems that can be solved with machine learning, and then they build and implement the models that solve those problems. Being able to build machine learning models is an essential skill for Machine Learning Engineers, and this course will help you do just that.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning algorithms to extract insights from data. They work on a variety of problems, such as predicting customer churn, identifying fraud, and recommending products. This course will help you build a solid foundation in machine learning that you can use to become a successful Data Scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

As a Data Analyst, your job is to collect, clean and analyze data from different sources to help your company make more informed decisions. One of the main techniques Data Analysts use is machine learning algorithms like the ones you will learn about in this course. Machine learning can be used to make predictions, identify trends, and classify data, which can be extremely valuable for businesses. This course will help you build a foundation in machine learning that you can use to advance your career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to help businesses make better decisions. They work with stakeholders to identify business problems and opportunities, and then they use data to develop solutions. Machine learning is a powerful tool that Business Analysts can use to improve their work, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Machine learning is a powerful tool that Quantitative Analysts can use to improve their work, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. Machine learning is increasingly being used to develop new and innovative software applications, and this course will help you learn how to use machine learning in your work as a Software Engineer.

See salaries and explore the career path for Software Engineer

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. Machine learning is a powerful tool that Operations Research Analysts can use to improve their work, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Operations Research Analyst

Market Researcher

Market Researchers collect and analyze data about consumers and markets. Machine learning can be used to automate and improve many of the tasks that Market Researchers perform, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Market Researcher

Fraud Analyst

Fraud Analysts investigate and prevent fraud. Machine learning can be used to automate and improve many of the tasks that Fraud Analysts perform, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Fraud Analyst

Risk Analyst

Risk Analysts identify and assess risks to businesses. Machine learning can be used to automate and improve many of the tasks that Risk Analysts perform, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. Machine learning is a powerful tool that Actuaries can use to improve their work, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Actuary

Financial Analyst

Financial Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Machine learning is a powerful tool that Financial Analysts can use to improve their work, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Financial Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines. Machine learning models require large amounts of data to train and operate, and Data Engineers play a critical role in ensuring that machine learning models have the data they need.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians collect, analyze, and interpret data. Machine learning is a powerful tool that Statisticians can use to improve their work, and this course will help you learn how to use it effectively.

See salaries and explore the career path for Statistician

Product Manager

Product Managers are responsible for the development and launch of new products. Machine learning is increasingly being used to develop new and innovative products, and this course will help you learn how to use machine learning to improve your work as a Product Manager.

See salaries and explore the career path for Product Manager