A deep understanding of what makes algorithms work, and how to tune them for custom implementation.
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Instructor: Luis Serrano
DeepLearning.AI
A deep understanding of what makes algorithms work, and how to tune them for custom implementation.
Statistical techniques that empower you to get more out of your data analysis.
Skills that employers desire, helping you ace machine learning interview questions and land your dream job.
Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you’ll need basic to intermediate Python programming skills to be successful.
Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow visualizations to help you see how the math behind machine learning actually works.
We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.
This is a beginner-friendly course for anyone who wants to develop their mathematical fundamentals for a career in machine learning and data science. A high school level of mathematics (functions, basic algebra), a beginner’s understanding of machine learning concepts, and basic familiarity with a programming language, ideally Python (loops, functions, if/else statements, lists/dictionaries, importing libraries) will help you get the most out of this specialization.
Enroll now and take your career to the next level!
This course is part of Mathematics for Machine Learning and Data Science
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You can download the annotated version of the course slides below.
*Note: The slides might not reflect the latest course video slides. Please refer to the lecture videos for the most up-to-date information. We encourage you to make your own notes.“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
“As a Behavioral Scientist, I was able to adopt methods to understand my customers better, overcome the traditional ‘one-size-fits-all’ approach, and design interventions which account for personality and individual differences.”
“I gained confidence in my knowledge of machine learning. Since then, I’ve become a machine learning mentor, got a research paper published in IEEE, decided to pursue my Masters in Machine Learning, and was able to land a job at JP Morgan Chase.”
“The Machine Learning course became a guiding light. Andrew Ng explains concepts with simple visualizations and plots. I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company.”
Yes! We want to break down the barriers that hold people back from advancing their math skills. In this course, we flip the traditional mathematics pedagogy on its head, starting with the real world use-cases and working back to theory.
Most people who are good at math simply have more practice doing math, and through that, more comfort with the mindset needed to be successful. This course is the perfect place to start or advance those fundamental skills, and build the mindset required to be ‘good at math’.
With out-of-the-box tools, it’s easier than ever to begin a career as a machine learning engineer or data scientist. But to advance deeper in your career, create efficient models, troubleshoot algorithms, and incorporate creative thinking, a deeper understanding of the mathematics behind the models is needed.
With Mathematics for Machine Learning and Data Science, you will have a foundation of knowledge that will equip you to go deeper in your machine learning and data science career.
High school math (functions, basic algebra) and programming (loops, functions, if/else, lists/dictionaries, libraries, debugging) are recommended.
This specialization consists of three courses. At the rate of 5 hours per week, it will take you around 4 weeks to complete Course 1, 3 weeks to complete Course 2, and 4 weeks to complete Course 3 of the Mathematics For Machine Learning and Data Science Specialization.
The Mathematics for Machine Learning and Data Science Specialization is made up of three courses.
You can enroll in the Mathematics for Machine Learning and Data Science specialization on Coursera. You will watch videos and complete assignments on Coursera as well.
No! Most learners would benefit from taking courses one and two together, as they introduce concepts that build upon each other, but course three is independent from the other courses in this specialization.
A Coursera subscription costs $49 / month.
Yes, Coursera provides financial aid to learners who cannot afford the fee.
Yes! You can preview the course for free by accessing the entire first module at no cost. This allows you to explore the learning experience before deciding if you’d like to continue. If you want full access to all modules, assessments, and the certificate of completion, you’ll need to upgrade to the paid version.
You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.
If you complete all 4 courses in the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization.
Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.
Go to your Coursera account.
Click on My Purchases and find the relevant course or Specialization.
Click Email Receipt and wait up to 24 hours to receive the receipt.
You can read more about it here.