Interested in Machine Learning and Deep Learning ? Then this course is for you!This course is about the fundamental concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking.In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with Sk Learn, Keras and TensorFlow. MACHINE LEARNING Linear Regressionunderstanding linear regression modelcorrelation and covariance matrixlinear relationships between random variablesgradient descent and design matrix approaches Logistic Regressionunderstanding logistic regressionclassification algorithms basicsmaximum likelihood function and estimationK-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier?non-parametric machine learning algorithms Naive Bayes Algorithmwhat is the naive Bayes algorithm?classification based on probabilitycross-validation overfitting and underfitting Support Vector Machines (SV Ms)support vector machines (SV Ms) and support vector classifiers (SV Cs)maximum margin classifierkernel trick Decision Trees and Random Forestsdecision tree classifierrandom forest classifiercombining weak learners Bagging and
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