Basics of Numerical Methods for Machine Learning & Engg.

🧮 Master Numerical Methods for AI & Engineering - Basics of Numerical Methods for Machine Learning & Engineering by Sandeep Kumar Mathur

🚀 Course Headline: Dive into the fundamental building blocks of Numerical Analysis that power Machine Learning, Deep Learning, Artificial Intelligence, and Data Science. This comprehensive course will equip you with the essential skills to tackle complex mathematical problems in these high-demand fields.

📘 Course Description: Numerical methods are the unsung heroes of machine learning and engineering. They provide solutions to real-world problems that are too complex for analytical solutions. These methods are crucial for optimization, simulation, and analysis in various domains, including deep learning, AI, and data science.

In this course, you'll explore the foundational numerical techniques that form the bedrock of these disciplines. We'll cover a range of topics, from interpolation and integration to solving algebraic and transcendental equations, all through the lens of practical applications in machine learning and engineering.

Key Topics Covered:

📈 The Calculus of Finite Differences 🔍

• The Forward Differences 🠔
• Forward Difference Table 📊
• The Backward Differences 🌟
• Properties of Difference Operator 🎯

🔢 Interpolation with Equal Intervals 🌍

• Assumptions for Methods of Interpolation 📈
• Newton-Gregory Method/Formula 🏗️
• Newton-Gregory Formula for Backward Interpolation 🌛

🔢 Interpolation with Unequal Intervals 🌐

• Lagrange's Interpolation Formula 📉
• Divided Difference Formula 🔧

💡 Numerical Differentiation 🔬

• Understanding the nuances of numerical differentiation 🔍

🔗 Numerical Integration 🌟

• General Quadrature Formula 📚
• Trapezoidal Rule 🏋️‍♂️
• Simpson's One Third (1/3) Rule 🎛️
• Simpson's Three Eighths (3/8) Rule 🧮
• Weddle's Rule 🌟

⚫ Numerical Solution of Algebraic and Transcendental Equations 🔄

• Properties of Algebraic Equations 📊
• Synthetic Division 🔪
• Derivative of a Polynomial with Synthetic Division 📈
• Methods of finding out roots of equation: Graphical Method 🎨
• Bisection Method 🗺️
• Regula Falsi Method/False Position Method ➫
• Iteration Method 🔁
• Newton Raphson Method 🚀

📊 Data Analysis & Processing 📊

• Understanding the role of numerical methods in data science and engineering 💻
• Randomized Linear Algebra 🤖
• Monte Carlo Simulations 🎲

Why Enroll? 🤔 This course is designed to support your learning journey with a Q&A section, where your doubts are resolved. The content is regularly updated based on student feedback and we're committed to expanding the course with new topics in the future.

🎉 Take Action! 🎯 Don't let these essential numerical methods pass you by. Enroll in "Basics of Numerical Methods for Machine Learning & Engineering" today and unlock your potential. With expert guidance, comprehensive resources, and a supportive community, you're set up for success. Embark on this journey to enhance your problem-solving skills and achieve the excellence you aspire to in the dynamic fields of machine learning, deep learning, AI, and data science.