Mathematics is the backbone of Data Science. You would have heard people saying that ML/DL models are like black box. But they are not. It is just that we haven’t put enough efforts to understand the maths under the hood. One doesn’t need to become master of maths in order to start their career in Data Science, But if you are good with math then you will be playboy of Data Science world. Spend 2–3 days, just to get comfortable with the topics mentioned below. Few Use-cases to motivate: • Straight Line Geometry and Matrix Operations used in Linear Regression. • Sigmoid Function is the backbone of Logistic regression. • Differential calculus is the backbone of Backpropagation which is the backbone of any Deep Learning Algortihms. • Eigen Values/Vectors is must to understand Principal Component Analysis which is a very popular Dimensionality Reduction Technique. • Gradient Descent utilises differential calculus as well as Optimisation of cost function. • Permutation and combination is used to get understanding of probability which is must for Bayes Theorem and Naive Bayes Model. Topics to be covered: • Linear Algebra — Vector, Matrix Operations, Matrix Types, Eigen Values and Eigen Vectors, Set Theory, Functions, Logarithmic, Exponential Functions. • Differential Calculus • Permutation and Combination • Optimisation Technique: Linear Programming, Maxima/Minima