Trees, SVM and Unsupervised Learning from Coursera

"Trees, SVM and Unsupervised Learning" is designed to provide working professionals with a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, you will learn how to build powerful predictive models using these techniques and understand the advantages and disadvantages of each. The course will also cover how and when to apply them to different scenarios, including binary classification and K > 2 classes. Additionally, you will gain valuable experience in generating data representations through PCA and clustering. With a focus on practical, real-world applications, this course is a valuable asset for anyone looking to upskill or move into the field of data science.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

What's inside

Syllabus

Welcome!

The module provides an introductory overview of the course and introduces the course instructor.

Support Vector Machines (SVMs)

To begin the course, we will learn about support vector machines (SVMs). SVMs have become a popular method in the field of statistical learning due to their ability to handle non-linear and high-dimensional data. SVMs seek to maximize the margin, or distance between the decision boundary and the closest data points, to improve generalization performance. Throughout the week, you will learn how to apply SVMs to classify or predict outcomes in a given dataset, select appropriate kernel functions and parameters, and evaluate model performance

Introduction to Neural Networks

Neural Networks have become increasingly popular in the field of statistical learning due to their ability to model complex relationships in data. In this module, we will cover introductory concepts of neural networks, such as activation functions and backpropagation. You will have the opportunity to apply Neural Networks to classify or predict outcomes in a given dataset and evaluate model performance in the labs for this module.

Decision Trees-Bagging-Random Forests

Welcome to the final module for the course. This module will focus on the ensemble methods decision trees, bagging, and random forests, which combine multiple models to improve prediction accuracy and reduce overfitting. Decision Trees are a popular machine learning method that partitions the feature space into smaller regions and models the response variable in each region using simple rules. However, Decision Trees can suffer from high variance and instability, which can be addressed by Bagging and Random Forests. Bagging involves generating multiple trees on bootstrapped samples of the data and averaging their predictions, while Random Forests further decorrelate the trees by randomly selecting subsets of features for each tree.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops foundational skills in support vector machines, neural networks, decision trees, and XG boost for data scientists

Taught by Osita Onyejekwe, who is recognized for their work in machine learning

Offered in partnership with CU Boulder, which is recognized for its work in data science

Covers practical applications of machine learning techniques

Includes hands-on labs and interactive materials

Can be taken for academic credit towards CU Boulder’s Master of Science in Data Science

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 Trees, SVM and Unsupervised Learning with these activities:

Review probability concepts

Show steps

Brushing up on your understanding of probability will help you succeed in this course, as many of the concepts rely on it. Start by revisiting your notes for a quick refresh.

Browse courses on Probability

Show steps

Identify the main topics within the course that utilize probability
Reread your old notes that cover the review topics

Look for resources to learn the basics of Support Vector Machines

Show steps

Start the course prepared by seeking out content that will lay a foundation in support vector machines. This will let you begin taking in course materials with a stronger foundation.

Browse courses on Support Vector Machines

Show steps

Find a few online tutorials on Support Vector Machines and complete them
Go through the SVM tutorial provided by the course instructor

Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

Show steps

Expand your knowledge of machine learning algorithms and techniques.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

Show steps

Read the relevant chapters on SVMs, Neural Networks, and Decision Trees.
Work through the practice exercises provided in the book.

12 other activities

Expand to see all activities and additional details

Show all 15 activities

Organize all supplementary materials for the course

Show steps

Compiling all of your supplementary materials for the course will help keep you organized and ensure you have easy access to them.

Show steps

Gather the materials including notes, assignments, and quizzes
Create an organized system for storing and accessing the materials

Attend a Data Science Meetup

Show steps

Connect with others in the field and learn about the latest trends.

Browse courses on Machine Learning

Show steps

Find a local Data Science meetup group.
Register for an upcoming event.
Attend the event and participate in discussions.

Learn about Neural Networks

Show steps

Neural Networks are an important part of machine learning, and this course will cover introductory concepts. Complete these tutorials to build a solid foundation.

Browse courses on Neural Networks

Show steps

Locate online materials for learning about Neural Networks
Take notes to maintain a record of what you learn

Practice with SVMs using Jupyter

Show steps

Increase your familiarity with the different aspects of SVMs through a structured walkthrough.

Browse courses on Support Vector Machines

Show steps

Install Jupyter Notebook on your computer.
Find a suitable SVM library for Python.
Import the necessary libraries and datasets.
Preprocess and explore the data.
Build and train an SVM model.

Join study group with other classmates

Show steps

Finding a study group will help you stay motivated and give you a support system for learning.

Show steps

Identify classmates who are interested in forming or joining a study group
Meet regularmente to review class materials and discuss concepts

Organize a Study Group for Decision Trees

Show steps

Collaborate with peers to enhance your understanding through discussion and mutual support.

Browse courses on Decision Trees

Show steps

Find a group of fellow learners who share your interests.
Set regular meeting times and locations.
Discuss course materials, share resources, and work through problems together.

Implement a Neural Network from Scratch

Show steps

Deepen your understanding of Neural Networks by building one from scratch.

Browse courses on Neural Networks

Show steps

Choose a simple Neural Network architecture.
Define the forward and backward pass.
Implement the training loop.
Evaluate the performance of the model.

Work on exercise sets for Decision Trees

Show steps

Work through some practice problems involving Decision Trees. Use the course material to help you complete these problems and reinforce what you're learning.

Browse courses on Decision Trees

Show steps

Get extra exercise problems from the course instructor
Find additional example problems from external sources

Participate in a Hands-On Workshop on Neural Networks

Show steps

Accelerate your learning by applying your knowledge in a hands-on setting.

Browse courses on Neural Networks

Show steps

Find a workshop that aligns with your interests.
Register and prepare for the workshop.
Actively participate in the hands-on exercises.
Follow up after the workshop to reinforce your learning.

Solve a Real-World Problem using Decision Trees

Show steps

Apply Decision Trees to a practical problem to enhance your problem-solving skills.

Browse courses on Decision Trees

Show steps

Identify a suitable dataset.
Preprocess and explore the data.
Train a Decision Tree model.
Evaluate the performance of the model.
Deploy the model to solve the real-world problem.

Create a Presentation on Real-World Applications of Tree-Based Models

Show steps

Reinforce your understanding of Tree-Based Models by presenting their practical applications.

Browse courses on Decision Trees

Show steps

Research different use cases of Decision Trees in industry.
Gather examples and case studies.
Develop slides and visual aids for the presentation.
Practice delivering the presentation.

Start a project for a topic of your choice

Show steps

Working on a project of your choice can help you apply your understanding of the concepts learned in the course.

Show steps

Brainstorm a few ideas for your project
Choose a topic that aligns with your interests
Research the topic and gather materials
Create a plan for your project
Start working on your project

Career center

Learners who complete Trees, SVM and Unsupervised Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models that help organizations automate tasks and improve decision-making. To succeed in this role, one should understand support vector machines, neural networks, decision trees, and XG boost, all of which are covered in Trees, SVM and Unsupervised Learning.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists help organizations understand their data and use it to make more informed decisions. They use a variety of techniques, including those that Trees, SVM and Unsupervised Learning covers, such as neural networks and clustering. These skills can help a Data Scientist advance their career.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use data to solve problems and make informed decisions. They may work in a variety of fields, including healthcare, finance, and marketing. Trees, SVM and Unsupervised Learning will help one wanting to become a Statistician by teaching them how to build predictive models and generate data representations, which are essential skills in the field.

See salaries and explore the career path for Statistician

Data Engineer

Data Engineers build and maintain data pipelines that collect, clean, and transform data. They may work in a variety of industries, including technology, healthcare, and finance. Those who wish to become Data Engineers will find Trees, SVM and Unsupervised Learning helpful, as it covers techniques for generating data representations through PCA and clustering.

See salaries and explore the career path for Data Engineer

Data Architect

Data Architects design and build data systems that support an organization's data needs. They may work in a variety of industries, including technology, healthcare, and finance. Those who wish to become Data Architects will find Trees, SVM and Unsupervised Learning helpful, as it covers techniques for generating data representations through PCA and clustering.

See salaries and explore the career path for Data Architect

Data Analyst

Data Analysts scour data to provide insights that help organizations make more informed decisions and understand market trends. Those that complete Trees, SVM and Unsupervised Learning will gain skills such as how to generate data representations through PCA and clustering, which will be necessary to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to understand business processes and make recommendations for improvements. They may work in a variety of industries, including technology, healthcare, and finance. The skills taught in Trees, SVM and Unsupervised Learning, such as how to build predictive models and generate data representations, can help a Business Analyst succeed in their role.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make predictions about financial markets. Those wishing to enter this field may find Trees, SVM and Unsupervised Learning to be useful for learning how to build predictive models, a key skill for a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior and market trends. They may work for a variety of organizations, including marketing firms, advertising agencies, and consultancies. Market Researchers may find Trees, SVM and Unsupervised Learning helpful, as it covers topics such as building predictive models and generating data representations, which are relevant to the field.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts use data to analyze financial markets and make investment recommendations. They may work for a variety of organizations, including banks, investment firms, and hedge funds. Trees, SVM and Unsupervised Learning may be helpful for those who wish to become Financial Analysts, as it covers topics such as building predictive models and generating data representations, which are relevant to the field.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts identify and assess risks that organizations face. They may work for a variety of organizations, including banks, insurance companies, and consultancies. Trees, SVM and Unsupervised Learning may be helpful for those who wish to become Risk Analysts, as it covers topics such as building predictive models and generating data representations, which are relevant to the field.

See salaries and explore the career path for Risk Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. They may work in a variety of industries, including technology, healthcare, and finance. While not strictly necessary, the skills taught in Trees, SVM and Unsupervised Learning, such as neural networks and decision trees, may be helpful for Software Engineers who want to specialize in data science or machine learning.

See salaries and explore the career path for Software Engineer

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve problems in a variety of industries, including transportation, logistics, and manufacturing. They may also work for government agencies. Trees, SVM and Unsupervised Learning could be helpful for those who wish to become Operations Research Analysts, as it covers topics such as building predictive models and generating data representations, which are relevant to the field.

See salaries and explore the career path for Operations Research Analyst