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- 2024-12-22
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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 2.33 GB | Duration: 8h 37mSolving regression problems (linear regression and logistic regression)Solving classification problems (naive Bayes classifier, Support Vector Machines – SVMs) Using neural networks (feedforward neural networks, deep neural networks, convolutional neural networks and recurrent neural networks The most up to date machine learning techniques used by firms such as Google or Facebook Face detection with OpenCV TensorFlow and Keras Deep learning – deep neural networks, convolutional neural networks (CNNS), recurrent neural networks (RNNs) Reinforcement learning – Q learning and deep Q learning approaches RequirementsBasic Python – we will use Panda and Numpy as well (we will cover the basics during implementations) DescriptionInterested 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 SkLearn, Keras and TensorFlow.### MACHINE LEARNING ###1.) Linear Regressionunderstanding linear regression model correlation and covariance matrix linear relationships between random variables gradient descent and design matrix approaches 2.) Logistic Regressionunderstanding logistic regression classification algorithms basics maximum likelihood function and estimation 3.) K-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier? non-parametric machine learning algorithms 4.) Naive Bayes Algorithmwhat is the naive Bayes algorithm? classification based on probability cross-validation overfitting and underfitting 5.) Support Vector Machines (SVMs)support vector machines (SVMs) and support vector classifiers (SVCs) maximum margin classifier kernel trick 6.) Decision Trees and Random Forestsdecision tree classifier random forest classifier combining weak learners 7.) Bagging and Boostingwhat is bagging and boosting? AdaBoost algorithm combining weak learners (wisdom of crowds) 8.) Clustering Algorithmswhat are clustering algorithms? k-means clustering and the elbow method DBSCAN algorithm hierarchical clustering market segmentation analysis ### NEURAL NETWORKS AND DEEP LEARNING ###9.) Feed-Forward Neural Networkssingle layer perceptron model feed.forward neural networks activation functions backpropagation algorithm 10.) Deep Neural Networkswhat are deep neural networks? ReLU activation functions and the vanishing gradient problem training deep neural networks loss functions (cost functions) 11.) Convolutional Neural Networks (CNNs)what are convolutional neural networks? feature selection with kernels feature detectors pooling and flattening 12.) Recurrent Neural Networks (RNNs)what are recurrent neural networks? training recurrent neural networks exploding gradients problem LSTM and GRUs time series analysis with LSTM networks 13.) Reinforcement LearningMarkov Decision Processes (MDPs) value iteration and policy iteration exploration vs exploitation problem multi-armed bandits problem Q learning and deep Q learning learning tic tac toe with Q learning and deep Q learning ### COMPUTER VISION ###14.) Image Processing Fundamentalscomputer vision theory what are pixel intensity values convolution and kernels (filters) blur kernel sharpen kernel edge detection in computer vision (edge detection kernel) 15.) Serf-Driving Cars and Lane Detectionhow to use computer vision approaches in lane detection Canny’s algorithm how to use Hough transform to find lines based on pixel intensities 16.) Face Detection with Viola-Jones AlgorithmViola-Jones approach in computer vision what is sliding-windows approach detecting faces in images and in videos 17.) Histogram of Oriented Gradients (HOG) Algorithmhow to outperform Viola-Jones algorithm with better approaches how to detects gradients and edges in an image constructing histograms of oriented gradients using support vector machines (SVMs) as underlying machine learning algorithms 18.) Convolution Neural Networks (CNNs) Based Approacheswhat is the problem with sliding-windows approach region proposals and selective search algorithms region based convolutional neural networks (C-RNNs) fast C-RNNs faster C-RNNs 19.) You Only Look Once (YOLO) Object Detection Algorithmwhat is the YOLO approach? constructing bounding boxes how to detect objects in an image with a single look? intersection of union (IOU) algorithm how to keep the most relevant bounding box with non-max suppression? 20.) Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDDwhat is the main idea behind SSD algorithm constructing anchor boxes VGG16 and MobileNet architectures implementing SSD with real-time videos Who this course is forThis course is meant for newbies who are not familiar with machine learning, deep learning, computer vision and reinforcement learning or students looking for a quick refresher
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