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Mathematics behind ML by Mr. Vimal Daga
Mathematics behind ML
1. Use of Calculus | Optimization of Calculus| Critical point/ maxima| Resources planning |Infinitesimal Calculus| Error function| Differential calculus| Integral calculus| Derivative function | Sympy library | Dependent variable| Independent variable |Symbol function | Integrate function (188:09)
Summary Session 1
2. Sympy Library| Symbols| Substitute function |Assignment Operator| Equality operator| Linear equation graph| intercept| Polynomial curve| Domain| Devide and conquer approach| Derivatives function | Leibniz notation | Euler's notation | Newton notation - 31st March (220:34)
Summary Session 2
3. Dataset| Logfile| Use of calculus in ML| Peak/critical points| Limit| Domain| Range| Overview of Integral| Differential calculus| Derivative function| Slope| Finding critical points using differentiation| Area under curve| Differentiation using sympy| Integral using sympy on - 6th April (130:30)
Summary Session 3
4. "Area Under Curve | Integral | Speed function | Distance vs time | Rate of change | Function prime | ∆y/∆t(delta) vs dy/dt | Slope | Tangent line | Mathematical way of finding or solving derivative | Finding derivative using python " (133:35)
Summary Session 4
5. Tangent | Y-prime | Derivative of trigonometric functions | Sympy.sinx | Derivative of product of functions | Product Rule | Quotient Rule | Sympy.simplify() | Sympy.Eq() | xLim and yLim in sympy.plot() | Chain Rule | Composite Functions(function inside function) (114:03)
Summary Session 5
6. Implicit functions | Explicit vs Implicit functions | Finding derivative of the Implicit functions | Solving Implicit functions mathematically | Sympy.idiff() function | Sympy.exp() function | Chain rule
Summary Session 6
7.Session 04/05/2024 (99:10)
7. What is Higher order derivative | How to solve higher order derivative | Application of higher order derivative | Numpy library | Second order derivative (derivative of derivative) | Finding Higher order derivative using sympy | Solving derivative using the Numpy library | Matplotlib Library (99:10)
Summary Session 7
8.Mathematics behind Machine Learning By Mr Vimal Daga on 12th May_GMT20240512-081933 (82:35)
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5. Tangent | Y-prime | Derivative of trigonometric functions | Sympy.sinx | Derivative of product of functions | Product Rule | Quotient Rule | Sympy.simplify() | Sympy.Eq() | xLim and yLim in sympy.plot() | Chain Rule | Composite Functions(function inside function)
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