Linear Regression
Supervised Machine Learning Linear Equations begin{equation} label{eq:sys} w_1x_1^{(1)}+w_2x_2^{(1)}+dots+{w_n}x_n^{(1)}+b=y^{(1)} end{equation} begin{equation} label{eq:sys} w_1x_1^{(2)}+w_2x_2^{(2)}+dots+{w_n}x_n^{(2)}+b=y^{(2)} end{equation} [ dots ] begin{equation} label{eq:sys} w_1x_1^{(m)}+w_2x_2^{(m)}+dots+{w_n}x_n^{(m)}+b=y^{(m)} end{equation} are weights are datasets So in linear equation weight remain same but dataset varies. System of Sentences Non Singular System Such system comprises of non contradictory statements. Which results in more info for analysis. e.g. System-1 The […]
Standard Identities
begin{equation} label{eq:poly}(a + b)^2=a^2+b^2+2abend{equation} begin{equation} label{eq:poly}(a – b)^2=a^2+b^2-2abend{equation} begin{equation} label{eq:poly}(a + b)^3=a^3+3a^2b+3ab^2+b^3end{equation} begin{equation} label{eq:poly}(a + b)^3=a^3+b^3+3ab(a+b)end{equation} begin{equation} label{eq:poly}(a – b)^3=a^3-3a^2b+3ab^2-b^3end{equation} begin{equation} label{eq:poly}(a – b)^3=a^3-b^3-3ab(a-b)end{equation} begin{equation} label{eq:poly}(a + b+c)^2=a^2+b^2+c^2+2ab+2bc+2acend{equation} begin{equation} label{eq:poly}a^3+b^3+c^3-3abc=(a+b+c)(a^2+b^2+c^2-ab-bc-ac)end{equation} if then begin{equation} label{eq:poly}a^3+b^3+c^3=3abcend{equation} begin{equation} label{eq:poly}a^3+b^3=(a+b)(a^2-ab+b^2)end{equation} begin{equation} label{eq:poly}a^3-b^3=(a-b)(a^2+ab+b^2)end{equation} begin{equation} label{eq:poly}(a + b)(a-b)=a^2-b^2end{equation} begin{equation} label{eq:poly}(x+a)(x+b)=x^2+(a+b)x+abend{equation} begin{equation} label{eq:poly}(x+a)(x-b)=x^2+(a-b)x-abend{equation} begin{equation} label{eq:poly}(x-a)(x+b)=x^2+(b-a)x-abend{equation} begin{equation} label{eq:poly}(x-a)(x-b)=x^2-(a+b)x+abend{equation}