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•Linear Algebra fundamentals: Vectors and matrices•Introduction to Numpy: matrix manipulation and built-in functions, plotting with Matplotlib•Vector Spaces, bases and dimension: spans, linear combination and Linear independence of vectors, Changing bases•Linear transformation using matrices: scaling, reflection, rotation, shear•Matrix Determinant, inverse, Solving linear equations sys•Eigenvalues and eigenvectors•Principal component analysis: definition and code example•Revision+ Coding practice session: solving a real problem in python and Numpy.
•Basics of probability: Sample space, event and axioms•Calculating probability: For single event and mutually exclusive events.•Counting principle and calculating probability using counting techniques •Random variables, joint/marginal probabilities•Statistical tools: Mean, variance, standard deviation, Covariance, Correlation•Independence of events and their probability•Probability distributions of random variables: Probability mass function (PMF), Probability density function (PDF), Cumulative density function (CDF)•Discrete probability distributions: Bernoulli, Binomial, Poisson •Continuous probability distributions: Uniform distribution, exponent...