[2023] Machine Learning and Deep Learning Bootcamp in Python

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Price: 199.99$

Interested in Machine Learning, Deep Learning and Computer Vision? 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 Tensor Flow.###MACHINELEARNING###1.) Linear Regressionunderstanding linear regression modelcorrelation and covariance matrixlinear relationships between random variablesgradient descent and design matrix approaches2.) Logistic Regressionunderstanding logistic regressionclassification algorithms basicsmaximum likelihood function and estimation3.) K-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier?non-parametric machine learning algorithms4.) Naive Bayes Algorithmwhat is the naive Bayes algorithm?classification based on probabilitycross-validation overfitting and underfitting5.) Support Vector Machines(SVMs)support vector machines (SVMs) and support vector classifiers (SVCs)maximum margin classifierkernel trick6.) Decision Trees and Random Forestsdecision tree classifierrandom forest classifiercombining weak learners7.)Bagging and Boostingwhat is bagging and boosting?Ada Boost algorithmcombining weak learners (wisdom of crowds)8.) Clustering Algorithmswhat are clustering algorithms?k-means clustering and the elbow method DBSCAN algorithmhierarchical clusteringmarket segmentation analysis### NEURALNETWORKSANDDEEPLEARNING###9.) Feed-Forward Neural Networks single layer perceptron modelfeed. forward neural networksactivation functionsbackpropagation algorithm10.) Deep Neural Networkswhat are deep neural networks?Re LU activation functions and the vanishing gradient problemtraining deep neural networksloss functions (cost functions)11.) Convolutional Neural Networks (CNNs)what are convolutional neural networks?feature selection with kernelsfeature detectorspooling and flattening12.) Recurrent Neural Networks (RNNs)what are recurrent neural networks?training recurrent neural networksexploding gradients problem LSTMand GRUstime series analysis with LSTM networks Numerical Optimization (in Machine Learning)gradient descent algorithmstochastic gradient descent theory and implementation ADAGrad and RMSProp algorithms ADAM optimizer explained ADAM algorithm implementation13.) Reinforcement Learning Markov Decision Processes (MDPs)value iteration and policy iterationexploration vs exploitation problemmulti-armed bandits problem Q learning and deep Q learninglearning tic tac toe with Q learning and deep Q learning### COMPUTERVISION###14.) Image Processing Fundamentals: computer vision theorywhat are pixel intensity valuesconvolution and kernels (filters)blur kernelsharpen kerneledge detection in computer vision (edge detection kernel)15.) Serf-Driving Cars and Lane Detectionhow to use computer vision approaches in lane detection Canny’s algorithmhow to use Hough transform to find lines based on pixel intensities16.) Face Detection with Viola-Jones Algorithm: Viola-Jones approach in computer visionwhat is sliding-windows approachdetecting faces in images and in videos17.) Histogram of Oriented Gradients (HOG) Algorithmhow to outperform Viola-Jones algorithm with better approacheshow to detects gradients and edges in an imageconstructing histograms of oriented gradientsusing support vector machines (SVMs) as underlying machine learning algorithms18.) Convolution Neural Networks (CNNs) Based Approacheswhat is the problem with sliding-windows approachregion proposals and selective search algorithmsregion based convolutional neural networks (C-RNNs)fast C-RNNsfaster C-RNNs19.) You Only Look Once (YOLO)Object Detection Algorithmwhat is the YOLO approach?constructing bounding boxeshow to detect objects in an image with a single look?intersection of union (IOU) algorithmhow to keep the most relevant bounding box with non-max suppression?20.) Single Shot Multi Box Detector (SSD) Object Detection Algorithm SDDwhat is the main idea behind SSDalgorithmconstructing anchor boxes VGG16 and Mobile Net architecturesimplementing SSD with real-time videos You will get lifetime access to 150+ lectures plus slides and source codes for the lectures! This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you’ll get your money back. So what are you waiting for? Learn Machine Learning, Deep Learning and Computer Vision in a way that will advance your career and increase your knowledge, all in a fun and practical way! Thanks for joining the course, let’s get started!