Math Essentials for Machine Learning with Python
Master foundational math concepts for AI and machine learning using Python. Ideal for aspiring data scientists and ML engineers.
Master foundational math concepts for AI and machine learning using Python. Ideal for aspiring data scientists and ML engineers.
Dive into the mathematical foundations crucial for machine learning and artificial intelligence with this hands-on course. Designed for those who find advanced math intimidating, this program breaks down complex concepts into accessible modules. You'll explore essential topics like calculus, linear algebra, probability, and statistics, all through the lens of practical application in Python. The course emphasizes understanding foundational concepts and notation, rather than pure mathematical theory. By leveraging Python's powerful libraries, you'll gain hands-on experience in applying mathematical techniques to real-world data and machine learning scenarios. Perfect for aspiring AI practitioners looking to solidify their mathematical grounding in a practical, code-driven context.
4.4
(11 ratings)
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What you'll learn
Understand and apply equations, functions, and graphs in machine learning contexts
Master differentiation techniques and optimization principles for ML algorithms
Utilize vectors and matrices in data representation and manipulation
Apply statistics and probability concepts to analyze and interpret ML results
Implement mathematical techniques using Python for practical ML applications
Develop a strong foundation in the math underlying AI and machine learning
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This course provides a comprehensive introduction to the essential mathematical concepts underlying machine learning and artificial intelligence. It covers four main areas: Equations, Functions, and Graphs; Differentiation and Optimization; Vectors and Matrices; and Statistics and Probability. The curriculum is designed to bridge the gap between basic math knowledge and the advanced mathematical principles used in AI and ML. Each topic is presented with a focus on practical application using Python, allowing students to implement mathematical techniques in code. The course emphasizes hands-on learning, with demonstrations and exercises that apply mathematical concepts to real-world data and machine learning scenarios. By the end of the course, students will have a solid foundation in the mathematical principles crucial for understanding and implementing machine learning algorithms, all while developing their Python programming skills in the context of data analysis and AI applications.
Fee Structure
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4.4 course rating
11 ratings
Frequently asked questions
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