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Machine Learning And Deep Learning With Opencv




Machine Learning Software Training


Graduates and Technology Aspirants


Online and Classroom Sessions


Week Days and Week Ends

Duration :

Daily 2 hrs during Weekdays

Machine Learning What will you learn?

•Troubleshoot advanced models in Machine Learning.
•What are the advantages of Machine Learning?
•Learn from simple and interactive sessions on Machine Learning
•Learn in depth about the Fundamentals of Machine Learning
•Learn Everything you need to know about Basic Machine Learning
•Discuss all the principles of Machine Learning and demonstrate though Assignment.
•One stop solutions and step by step process for learning Machine Learning
•Learn all the topics from Machine Learning from the basics to advanced topics
•Gain the ability to adapt to any coding language with the concepts of Machine Learning

machine learning and deep learning with opencv Training Highlights

•Additional Sessions for Doubt Clearing
•Free technical support for students
• Helps you stand out in a competitive market
•Regular Brush-up Sessions of the previous classes
• Greater productivity and increased workforce morale
•Courseware that is curated to meet the global requirements
•We also provide Normal Track, Fast Track and Weekend Batches also for Working Professionals
•We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real time Experts

Who are eligible for Machine Learning

•Cloud Computing, Information Security, Network Security, Windows System Administration, Windows Server
•Front End, Javascript, Computer Graphics, Html, Css, Problem Solving, CSS, Web Technologies, Design, Software Development, Full Stack Developer
•Java/J2EE, .Net C#, Networking, Oracle DBA, Embedded Developers, HTML5, Android Framework, Android Developers, MSTR Developer, Cognos, SAN, Windows Admin
•OBIEE, OBIA, ODI, PHP, QA, Oracle Apps DBA, SQL Sever DBA, Dot Net Developer, Automation Testing, Informatica Developer, UI Designer, Agile PLM
•Software Development, .net, java,, Sql Server, database, Software Testing, javascript, Agile Methodology, Cloud Computing, html, application


•Machine Learning for OpenCV Advanced Methods and Deep Learning
•The Course Overview
•Understanding and Implementing Bayesian Classifier
•Classifying Emails Using Naive Bayes Classifier
•Understanding Unsupervised Learning and kmeans Clustering
•Understanding ExpectationMaximization
•Compressing Color Spaces Using kmeans
•Classifying Handwritten Digits Using kmeans
•Organizing Clusters as a Hierarchical Tree
•Understanding and Implementing Perceptron
•Understanding and Implementing Multilayer Perceptrons
•Getting Acquainted with Deep Learning
•Classifying Handwritten Digits
•Understanding Ensemble Methods
•Combining Decision Trees into a Random Forest
•Using Random Forests for Face Recognition
•Implementing AdaBoost
•Combining Different Models into a Voting Classifier
•Evaluating a Model
•Understanding CrossValidation
•Estimating Robustness Using Bootstrapping
•Assessing the Significance of Our Results
•Tuning Hyperparameters with Grid Search
•Chaining Algorithms Together to Form a Pipeline
•HandsOn Deep Learning for Computer Vision
•A HighLevel Overview of Deep Learning
•Installing Keras and TensorFlow
•Building a CNN Based Autoencoder to Denoise Images
•An Introduction to ImageNet Dataset and VGG Model
•Using a PreTrained VGG Model
•Summary and Whats Next
•Introduction to GANs
•Building GANs to Learn MNIST Dataset
•An Introduction to Object Detection and YOLO
•Installing and Setting Up Keras Implementation of YOLO
•Using a PreTrained YOLO Model for Object Detection
•An Introduction to Neural Style Transfer
•Using Keras Implementation of Neural Style Transfer
•Test your knowledge
•Object Detection and Recognition Using Deep Learning in OpenCV
•How to Work with Images in OpenCV
•Enhancement and Filtering Operations in OpenCV
•Saving Images Accessing Camera
•Image Transformations
•Computer Vision Algorithms
•Working with Object Recognition
•Features and Descriptors
•Feature Matching and Homography
•Building an Application
•Getting Started with Neural Networks
•Architecture of a Convolutional Neural Network CNN
•Starting with Caffe
•Implementing Deep Learning Using OpenCV and Caffe
•Defining Problem Statement
•Designing an Algorithm for the Problem
•Training the Network Using Labeled Data
•Classification Problem
•Problem Definition and Gathering Dataset
•Modeling Appropriate Algorithm
•Moving from Algorithm to Code
•Results and Analysis
•Machine Learning for OpenCV – Advanced Methods and Deep Learning
•Understanding Unsupervised Learning and k-means Clustering
•Understanding Expectation-Maximization
•Compressing Color Spaces Using k-means
•Classifying Handwritten Digits Using k-means
•Understanding Cross-Validation
•Hands-On Deep Learning for Computer Vision
•A High-Level Overview of Deep Learning
•Using a Pre-Trained VGG Model
•Summary and What’s Next?
•Using a Pre-Trained YOLO Model for Object Detection
•How to Work with Images in OpenCV?
•Saving Images, Accessing Camera
•Architecture of a Convolutional Neural Network (CNN)