Maths behind machine learning

Course Title: Maths behind Machine Learning: A Foundational Approach for Beginners 🚀

Course Headline: 🧙‍♂️ Dive into the Mathematical Core of Machine Learning with Samyak Jain 📚

Course Description:

Are you a curious mind eager to demystify the math behind the machine learning algorithms that power our modern world? Look no further! This introductory course is meticulously designed for budding machine learning engineers who aspire to not just use but truly understand the logic and mathematics that drive these algorithms. 🧮

Why Take This Course?

• Straightforward Learning: Say goodbye to the complexity of understanding machine learning by osmosis. With this course, you'll learn the math step by step, in a clear and logical manner.
• Hands-On Approach: Learn by doing! You'll build algorithms from scratch using Python, without relying on any external libraries. This ensures that you fully grasp how each component works.
• Focus on Fundamentals: We start with the basics – bivariate regression, multivariate regression, support vector regression, and k-nearest neighbors – providing a strong foundation before tackling more complex topics like deep neural networks.
• Real-World Relevance: Understand how these mathematical principles apply to real-world machine learning applications, giving you the tools to innovate and solve complex problems.

What You'll Learn:

• Mathematical Foundations: Dive into the core mathematical concepts that underpin machine learning algorithms.
• Algorithm Construction: Learn how to construct algorithms without the aid of libraries, fostering a deeper understanding of their inner workings.
• Regression Techniques: Master bivariate and multivariate regression, support vector regression, and k-nearest neighbors.
• Python Proficiency: Sharpen your Python skills, applying them to real-world machine learning tasks.
• Future-Proof Learning: Gain insights into how you can expand your knowledge to include advanced techniques like deep neural networks.

Instructor Insight: "My journey with machine learning was a labyrinth of complexity until I pieced together the mathematical puzzles that make these algorithms tick. Now, I'm here to guide you through the same path of discovery and understanding." – Samyak Jain

Course Structure:

• Interactive Lectures: Engage with concise, informative video content that breaks down complex concepts into digestible pieces.
• Exercises & Projects: Apply what you've learned through hands-on projects designed to reinforce your understanding and skills.
• Q&A Interaction: Post your queries in the Q&A section where Samyak Jain will be available to answer and clarify any doubts you may have.

Join a Community of Learners: Embark on this journey with like-minded individuals who are just as passionate about understanding machine learning as you are. Share your progress, discuss challenges, and celebrate achievements together in our vibrant community forum.

Ready to transform your approach to machine learning? Enroll now and start building a solid foundation in the mathematics that make these algorithms possible! 🛠️💡