We may earn an affiliate commission when you visit our partners.

Machine Learning Fundamentals

Sanjoy Dasgupta

3.9

Based on 8 ratings

, see reviews

Sanjoy Dasgupta

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.

Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.

All programming examples and assignments will be in Python, using Jupyter notebooks.

What you'll learn

Classification, regression, and conditional probability estimation
Generative and discriminative models
Linear models and extensions to nonlinearity using kernel methods
Ensemble methods: boosting, bagging, random forests
Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

Enroll now

What's inside

Learning objectives

Classification, regression, and conditional probability estimation
Generative and discriminative models
Linear models and extensions to nonlinearity using kernel methods

Ensemble methods: boosting, bagging, random forests
Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

Classification, regression, and conditional probability estimation
Generative and discriminative models
Linear models and extensions to nonlinearity using kernel methods
Ensemble methods: boosting, bagging, random forests
Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches fundamental concepts in supervised and unsupervised machine learning, which are foundational for data science

Utilizes a range of real-world case studies, providing practical applications of machine learning techniques

Emphasizes both theoretical foundations and practical implementation, equipping learners with a comprehensive understanding

Covers ensemble methods such as boosting, bagging, and random forests, which are essential techniques in machine learning

Explores representation learning techniques such as clustering, dimensionality reduction, autoencoders, and deep nets, which are cutting-edge approaches in the field

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Solid foundation in machine learning

According to learners, this course provides a solid foundation in machine learning fundamentals, particularly for those new to the field or seeking to deepen their theoretical understanding. Many found the lectures to be clear and well-structured, covering a broad range of essential algorithms. The course content is praised for its rigor and depth in explaining the underlying mathematical concepts. While some students note the homework assignments require significant effort and can be challenging, they are often seen as crucial for reinforcing learning. A few reviewers mentioned the possibility of some sections being potentially outdated in a rapidly evolving field, suggesting a need for occasional updates.

Assumes background in math/programming.

"Definitely requires a solid background in linear algebra, calculus, and probability to keep up."

"Need strong Python programming skills for the assignments; it's not a beginner's coding course."

"Came in without a strong math background and struggled significantly."

"Assumes prior knowledge that might not be explicitly stated upfront, be prepared."

Strong emphasis on the mathematical theory.

"I really appreciated the depth of theoretical and mathematical explanations provided."

"This course doesn't shy away from the math behind the algorithms, which is fantastic."

"If you want to understand *why* things work in ML, this course delivers on the theory."

"Provides a strong theoretical underpinning necessary for further study or application."

Lecture content is well-explained and structured.

"The lectures were incredibly clear and easy to follow, even on complex topics."

"Instructors did a great job explaining the material in a structured manner."

"Found the video lectures concise and effective for understanding the core ideas."

"The way the concepts are presented makes them very accessible."

Homework is challenging but valuable practice.

"Homework assignments are tough and require a significant time commitment, but they are essential for solidifying understanding."

"The programming assignments in Python using Jupyter notebooks were challenging yet highly rewarding."

"Be prepared for demanding problem sets, they really test your grasp of the material."

"Completing the homework is key to truly internalizing the concepts taught in lectures."

Covers essential ML concepts thoroughly.

"The course provides a solid foundation in machine learning concepts, covering essential algorithms and theories."

"It's a comprehensive introduction to the fundamentals, giving a great overview of key topics."

"I learned a lot about various supervised and unsupervised learning algorithms and the theory behind them."

"Covers all the critical basics you need to start understanding ML effectively."

Some methods may need updates.

"While foundational, some sections touched upon methods that feel slightly outdated given recent advancements in the field."

"The rapidly evolving nature of ML means certain topics could benefit from updates."

"Still covers essential principles, but don't expect cutting-edge techniques throughout."

"Could use some refreshers to include more modern approaches alongside the classics."

Course moves quickly through topics.

"The course moves at a very fast pace, especially if you are new to some of the mathematical concepts."

"Found it challenging to keep up with the rapid introduction of new algorithms and theories."

"Each week packs a lot of information, requiring dedicated study time."

"You need to stay on top of the material right from the start because it builds quickly."

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 Machine Learning Fundamentals with these activities:

Read 'Machine Learning For Dummies'

Show steps

Review the fundamental concepts of supervised and unsupervised learning covered in the course.

View Data Analytics & Visualization All-in-One For... on Amazon

Show steps

Locate a copy of the book.
Read the chapters relevant to the course.
Summarize the main concepts and techniques discussed in the book.

Follow online tutorials on machine learning techniques

Show steps

Expand your knowledge of machine learning by following online tutorials on specific techniques.

Browse courses on Self-Directed Learning

Show steps

Find a reputable source for online machine learning tutorials.
Choose a tutorial that covers a technique you want to learn more about.
Follow the tutorial step-by-step.

Review key linear algebra concepts

Show steps

Reviewing foundational linear algebra concepts such as matrices, vectors, and transformations will help to build a strong foundation for the course material.

Browse courses on Linear Algebra

Show steps

Revisit the basics of matrix algebra, including operations and properties of matrices.
Brush up on vector spaces, including concepts like linear independence and span.
Review linear transformations, focusing on their properties and representation using matrices.

12 other activities

Expand to see all activities and additional details

Show all 15 activities

Attend meetups or conferences related to machine learning

Show steps

Connect with other professionals in the field of machine learning to exchange ideas and learn about the latest trends.

Browse courses on Networking

Show steps

Find a meetup or conference related to machine learning.
Attend the event.
Introduce yourself to other attendees.

Solve practice problems on machine learning algorithms

Show steps

Strengthen your understanding of machine learning algorithms by solving practice problems.

Browse courses on Problem Solving

Show steps

Find a set of practice problems on machine learning algorithms.
Attempt to solve the problems on your own.
Check your solutions against the provided answer key.

Solve classification and regression problems

Show steps

Improve understanding of classification and regression algorithms by solving practice problems.

Browse courses on Classification

Show steps

Review lecture notes and textbook on classification and regression.
Solve a set of practice problems related to classification.
Solve a set of practice problems related to regression.
Compare your solutions to provided answer key.

Solve practice problems on supervised learning algorithms

Show steps

Engaging in regular practice drills on supervised learning algorithms, such as linear regression and logistic regression, will strengthen understanding and improve problem-solving skills.

Browse courses on Supervised Learning

Show steps

Identify a set of practice problems covering various supervised learning algorithms.
Solve the problems, focusing on understanding the underlying concepts and applying them effectively.
Review solutions and identify areas for improvement.

Develop a simple machine learning model

Show steps

Gain hands-on experience applying the machine learning algorithms covered in the course to solve real-world problems.

Browse courses on Classification

Show steps

Identify a suitable dataset for your project.
Choose the appropriate machine learning algorithm.
Train and evaluate your model.
Deploy your model and make predictions.

Follow tutorials on ensemble methods

Show steps

Gain hands-on experience with ensemble methods and boost understanding of their underlying principles.

Browse courses on Ensemble Methods

Show steps

Find online tutorials on ensemble methods.
Follow the tutorials and complete the provided exercises.
Implement a simple ensemble method from scratch.
Apply the implemented method to a real-world dataset.

Create a presentation on a machine learning research paper

Show steps

Develop your research and presentation skills by analyzing and presenting a recent machine learning research paper.

Browse courses on Research

Show steps

Find a recent machine learning research paper.
Read and understand the paper.
Create a presentation that summarizes the paper's main findings.
Present your findings to others.

Attend a machine learning workshop

Show steps

Gain hands-on experience with machine learning tools and techniques through a structured workshop.

Show steps

Find a machine learning workshop that aligns with your interests.
Register for the workshop.
Attend the workshop and actively participate in the activities.

Create a visual representation of clustering results

Show steps

Develop data visualization skills and gain insights into clustering results by creating visual representations.

Browse courses on Clustering

Show steps

Cluster a dataset using a clustering algorithm.
Choose an appropriate data visualization technique for the clustering results.
Create a visual representation of the clustering results.
Analyze the visualization and draw insights from the clustering.

Develop a visual representation of an unsupervised learning algorithm

Show steps

Creating a visual representation of an unsupervised learning algorithm, such as k-means clustering or PCA, will provide a deeper understanding of its functionality and application.

Browse courses on Unsupervised Learning

Show steps

Choose an unsupervised learning algorithm to visualize.
Gather data relevant to the algorithm.
Develop a visual representation using appropriate tools, such as graphs or charts.
Analyze the visualization to identify patterns and insights.

Create a tutorial on a machine learning technique

Show steps

Solidify your understanding of a specific machine learning technique by explaining it to others.

Browse courses on Teaching

Show steps

Choose a machine learning technique to focus on.
Write a detailed explanation of the technique.
Create visual aids or examples to illustrate the technique.
Share your tutorial with others.

Build a simple predictive model

Show steps

Practice data analysis and model building skills by creating a predictive model for a given dataset.

Browse courses on Predictive Modeling

Show steps

Choose a dataset and define a predictive modeling problem.
Explore the data and identify relevant features.
Select and train a predictive model.
Evaluate the model's performance and make improvements.
Document the modeling process and findings in a report.

Career center

Learners who complete Machine Learning Fundamentals will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of machine learning, statistics, and data analysis to extract insights from data. They work with businesses to identify problems that can be solved with data, and then they develop and implement the solutions. This course will help you build a foundation in machine learning, which is essential for a successful career as a Data Scientist. You will learn about the different types of machine learning algorithms, how to train and evaluate them, and how to apply them to real-world problems.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. They work with data scientists to identify and solve problems that can be solved with machine learning, and then they develop and implement the models that will solve those problems. They also monitor and maintain the models to ensure that they are performing as expected. This course will help you build a foundation in machine learning, which is essential for a successful career as a Machine Learning Engineer. You will learn about the different types of machine learning algorithms, how to train and evaluate them, and how to apply them to real-world problems.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts use their knowledge of data analysis, statistics, and computer programming to extract insights from data. They work with businesses to identify problems that can be solved with data, and then they develop and implement the solutions. This course will help you build a foundation in machine learning, which is increasingly being used to solve problems in data analysis. Data Analysts who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use their knowledge of business, data analysis, and computer programming to identify and solve problems in businesses. They work with businesses to identify and solve problems that can be solved with data, and then they develop and implement the solutions. This course may be helpful for Business Analysts who want to learn more about machine learning. Machine learning is increasingly being used to solve problems in business, and Business Analysts who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course may be helpful for Product Managers who want to learn more about machine learning. Machine learning is increasingly being used to develop new products, and Product Managers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They work with creative teams, media buyers, and other marketing professionals to develop and execute marketing campaigns that reach target audiences. This course may be helpful for Marketing Managers who want to learn more about machine learning. Machine learning is increasingly being used to develop and execute marketing campaigns, and Marketing Managers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Marketing Manager

Sales Manager

Sales Managers are responsible for leading and managing sales teams. They work with sales representatives to develop and execute sales strategies that achieve sales goals. This course may be helpful for Sales Managers who want to learn more about machine learning. Machine learning is increasingly being used to develop and execute sales strategies, and Sales Managers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Sales Manager

Operations Manager

Operations Managers are responsible for the day-to-day operations of a business. They work with employees, customers, and suppliers to ensure that the business runs smoothly. This course may be helpful for Operations Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the efficiency and effectiveness of business operations, and Operations Managers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Operations Manager

Financial Manager

Financial Managers are responsible for the financial planning and management of a business. They work with senior management to develop and implement financial plans that achieve the business's financial goals. This course may be helpful for Financial Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the accuracy and efficiency of financial planning and management, and Financial Managers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Financial Manager

Human Resources Manager

Human Resources Managers are responsible for the human resources function of a business. They work with employees, managers, and executives to develop and implement human resources policies and programs that support the business's goals. This course may be helpful for Human Resources Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the efficiency and effectiveness of human resources functions, and Human Resources Managers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Human Resources Manager

Project Manager

Project Managers are responsible for the planning, execution, and closure of projects. They work with project teams to develop and execute project plans that achieve project goals. This course may be helpful for Project Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the efficiency and effectiveness of project management, and Project Managers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Project Manager

Consultant

Consultants provide advice and guidance to businesses on a variety of topics. They work with businesses to identify and solve problems, and to develop and implement solutions. This course may be helpful for Consultants who want to learn more about machine learning. Machine learning is increasingly being used to solve problems in a variety of industries, and Consultants who have a strong understanding of machine learning will be more valuable to their clients.

See salaries and explore the career path for Consultant

Entrepreneur

Entrepreneurs are individuals who start and run their own businesses. They work to develop and launch new products and services, and to build and grow their businesses. This course may be helpful for Entrepreneurs who want to learn more about machine learning. Machine learning is increasingly being used to develop and launch new products and services, and Entrepreneurs who have a strong understanding of machine learning will be more successful in their businesses.

See salaries and explore the career path for Entrepreneur

Software Engineer

Software Engineers design, develop, and maintain software systems. They work with customers to identify and solve problems that can be solved with software, and then they develop and implement the software that will solve those problems. This course may be helpful for Software Engineers who want to learn more about machine learning. Machine learning is increasingly being used to solve problems in software development, and Software Engineers who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use their knowledge of mathematics, statistics, and computer programming to analyze financial data. They work with investment banks, hedge funds, and other financial institutions to identify and exploit opportunities in the financial markets. This course may be helpful for Quantitative Analysts who want to learn more about machine learning. Machine learning is increasingly being used to analyze financial data, and Quantitative Analysts who have a strong understanding of machine learning will be more valuable to their employers.

See salaries and explore the career path for Quantitative Analyst

Reading list

We've selected 22 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning Fundamentals.

The Elements of Statistical Learning

Save

Provides a comprehensive introduction to statistical learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about statistical learning.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Deep Learning

Save

Comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone interested in learning about deep learning.

Machine Learning Fundamentals

What you'll learn

What's inside

Learning objectives

Traffic lights

Save this course

Reviews summary

Solid foundation in machine learning

Activities

Career center

Reading list

Share

Similar courses

edX Buyer Protection & Guarantee

Free Audit Track Available

14-Day Full Refund Window

Verified Academic & Industry Partner