Free Math Classes For Adults – When I first started teaching myself data science, I had no idea how much math was required to fully answer an entry-level data science position.
After starting my first job in data science, I realized that the amount of math required to work in industry was much less than I expected.
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Many companies are developing machine learning models to solve similar business problems. Because of this, there is often no need to reinvent the wheel and create algorithms from scratch. You can often use existing solutions that are suitable for your current use.
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But it’s always good to at least have a basic understanding of the workings behind these algorithms.
For example, gradient descent is used to find the optimal slope value in linear regression models. This is a calculation guide and it is always useful to know how to calculate the best straight line.
Likewise, linear algebra has applications in data preparation for modeling and is widely used in implementing dimension reduction solutions. If you want to expand your deep learning knowledge, you will need to learn matrix manipulation techniques.
Finally, statistics is the most important branch of mathematics you should learn to become a data scientist. While working in the field, you must analyze trends in data, form hypotheses, and turn observations into meaningful insights. These are all applications of statistics and you should learn them if you want to get a job in data science.
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When learning the above concepts, I initially spent a lot of time calculating by hand. I learned to differentiate, integrate and solve linear equations. However, this is not a prerequisite for studying data science.
We have computer programs to do these calculations for us, and we need a high-level, intuitive understanding of these mathematical concepts.
Again, the above advice only applies if you are trying to learn data science to get a job in industry. If you want to become a machine learning researcher or enter academia, the amount of math you need to learn will increase significantly as you work to create new solutions from scratch.
In this article, I’ll give you 6 resources to learn the math of machine learning. I include free online materials (YouTube videos, online courses, textbooks) that you can use to learn enough math to become a data scientist.
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3Blue1Brown is a popular YouTube channel that uses a visual approach to analyze the most complex mathematical concepts.
Their Linear Algebra series introduces you to basic linear algebra concepts: vectors, linear combinations, linear transformations, matrix multiplication, eigenvalues, and eigenvectors.
They do not go too deep into mathematical calculations. Instead, the goal of this series is to give you a thorough understanding of linear algebra. Instead of memorizing simple formulas, they explain why they work and how you can achieve them yourself.
This is the second resource in this list created by 3Blue1Brown. Their accounting series is also interesting and insightful.
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They walk you through concepts like derivatives, the chain rule, and implicit differentiation—all of which are directly applicable to implementing gradient descent algorithms.
While their Computation series is aimed at general and those looking to improve their knowledge of mathematics, 3Blue1Brown’s Deep Learning series is designed specifically for students interested in artificial intelligence.
The Deep Learning series takes you into the inner workings of neural networks and how they learn.
You will be given an in-depth explanation of the backpropagation algorithm and how it works, as well as the computational concepts behind it.
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This is one of the best introductory statistics textbooks to read as a machine learning enthusiast. It is available online and can be downloaded for free.
If you’ve used algorithms like linear regression, logistic regression, and decision trees before, but you’re not sure how these models work and when to use them, this book is a great place to start.
You will understand how linear models, tree-based algorithms, and unsupervised techniques work. Information is provided on how to deal with overfitting in the construction of various statistical models.
Each chapter of this book includes a lab exercise in R that you can code, so you can not only understand the topic in theory, but also put the concepts you’ve learned into practice.
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The last resource on this list is a publicly available YouTube series from Harvard University called Statistics 110.
This is one of the best lectures I’ve found online about statistics and probability. It covers almost every statistical concept you’d typically see in an undergraduate statistics class—probability axons, distribution models, the Monty Hall problem, covariance, correlation, chi-square tests, t-tests, Markov chains, and more.
The lectures start you off at an introductory level, and even if you have no background in statistics and probability, you can follow them with little or no difficulty.
The resources above are a great way to dip your toe into the world of machine learning mathematics. They give you a solid grasp of mathematical concepts, which improves your understanding of the workings behind the models you create every day.
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If you don’t have time to work through all the resources on this list, I recommend at least reading Introduction to Statistical Learning, as the concepts explained in this textbook will be very relevant to your daily workflow as a data scientist. has a direct application. The growing popularity of STEM-focused education is prompting more students to take challenging math courses.
However, for some students, the subject can be a real challenge; But knowing where to turn for help can greatly reduce the struggle and improve their understanding.
Many schools offer students opportunities to get creative with math resources—turning to apps, websites, and online programs to help them verbally solve the problems they face. Choosing the most useful, convenient, and engaging online math tools can be a challenge in itself, but fortunately, with the help of several teachers at CalPac, an online charter school serving Southern California, this problem is easily solved.
Here are 15 of the best online resources for teaching math online – helping make math accessible and fun for students of all grades.
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Thanks to ByteLearn, teachers can spend less time preparing materials, while providing individualized training for each student. Teachers can use this platform to track student progress, monitor their performance, and tailor the curriculum to each student’s needs. With one click, teachers can create math worksheets, math quizzes, math unit tests, and math practice questions.
Khan Academy is a completely free personal learning resource with online courses, videos and exercises. Students can complete daily reviews and track progress from the learning dashboard of the platform. Math lessons are categorized by topic and grade level for easy navigation, and use specialized content courtesy of organizations like NASA, the California Academy of Sciences, and the Museum of Modern Art to bring lessons to life.
What teachers like: Practice problems offer step-by-step guidance so students can get help when they get stuck, but don’t need help with the entire problem. This allows them to work independently and learn at their own pace.
Although IXL is a subscription-based learning website, it offers daily math practice problems. Students can improve their math skills by completing ten free quizzes per day (on each subject). Subscription membership includes unlimited practice questions, analysis, papers, and personalized skill suggestions.
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What teachers like: If a student gets a problem wrong, the program shows all the steps to complete the problem so they can see where they went wrong and learn from their mistakes.
Desmos is a free online graphing calculator that students can use to graph functions, graph data, and evaluate equations. The website includes math examples and even creative art – so students can get the most out of the calculator.
What teachers like: The website and software are very user-friendly, with an extensive help center; And with Desmos, families don’t have to worry about buying an expensive graphing calculator.
Quizlet is a website and app that teaches all kinds of things using digital flashcards. In this guide, students can use flash cards, matching cards, short-term memory cards, long-term memory cards, etc. Students or teachers can use pre-made cards or create their own deck. This app is free and available on every platform.
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MathWorld is a free online resource for all things math. The site includes interactive GIFs and demonstrations, downloadable notebooks, and “capsule summaries” of various math concepts. Students can explore over 13,000 texts to strengthen their mathematical foundation and strengthen their knowledge.
What teachers love: This site allows older and more advanced students to really dig deeper into math,
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