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Machine Learning for OpenCV

  • Course Code: Artificial Intelligence - Machine Learning for OpenCV
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Expand OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to Expand OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today’s most exciting application fields of Machine Learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and Machine Learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine Learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the course progresses, so will your Machine Learning skills, until you are ready to take on today’s hottest topic in the field: Deep Learning. By the end of this, you will be ready to take on your own Machine Learning problems, either by building on the existing source code or developing your own algorithm from scratch! 

Working in a hands-on learning environment, led by our Machine Learning instructor, students will learn about and explore: 

  • Load, store, edit, and visualize data using OpenCV and Python 
  • Grasp the fundamental concepts of classification, regression, and clustering 
  • Understand, perform, and experiment with Machine Learning techniques using this easy-to-follow guide 
  • Evaluate, compare, and choose the right algorithm for any task 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Explore and make effective use of OpenCV’s Machine Learning module 
  • Learn deep learning for computer vision with Python 
  • Master linear regression and regularization techniques 
  • Classify objects such as flower species, handwritten digits, and pedestrians 
  • Explore the effective use of support vector machines, boosted decision trees, and random forests 
  • Get acquainted with neural networks and Deep Learning to address real-world problems 
  • Discover hidden structures in your data using k-means clustering 
  • Get to grips with data pre-processing and feature engineering 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to expand OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. A Taste of Machine Learning 
  • A Taste of Machine Learning 
  • Getting started with machine learning 
  • Problems that machine learning can solve 
  • Getting started with Python 
  • Getting started with OpenCV 
  • Installation 
  1. Working with Data in OpenCV and Python 
  • Working with Data in OpenCV and Python 
  • Understanding the machine learning workflow 
  • Dealing with data using OpenCV and Python 
  1. First Steps in Supervised Learning 
  • First Steps in Supervised Learning 
  • Understanding supervised learning 
  • Using classification models to predict class labels 
  • Using regression models to predict continuous outcomes 
  • Classifying iris species using logistic regression 
  1. Representing Data and Engineering Features 
  • Representing Data and Engineering Features 
  • Understanding feature engineering 
  • Preprocessing data 
  • Understanding dimensionality reduction 
  • Representing categorical variables 
  • Representing text features 
  • Representing images 
  1. Using Decision Trees to Make a Medical Diagnosis 
  • Using Decision Trees to Make a Medical Diagnosis 
  • Understanding decision trees 
  • Using decision trees to diagnose breast cancer 
  • Using decision trees for regression 
  1. Detecting Pedestrians with Support Vector Machines 
  • Detecting Pedestrians with Support Vector Machines 
  • Understanding linear support vector machines 
  • Dealing with nonlinear decision boundaries 
  • Detecting pedestrians in the wild 
  1. Implementing a Spam Filter with Bayesian Learning 
  • Implementing a Spam Filter with Bayesian Learning 
  • Understanding Bayesian inference 
  • Implementing your first Bayesian classifier 
  • Classifying emails using the naive Bayes classifier 
  1. Discovering Hidden Structures with Unsupervised Learning 
  • Discovering Hidden Structures with Unsupervised Learning 
  • Understanding unsupervised learning 
  • Understanding k-means clustering 
  • Understanding expectation-maximization 
  • Compressing color spaces using k-means 
  • Classifying handwritten digits using k-means 
  • Organizing clusters as a hierarchical tree 
  1. Using Deep Learning to Classify Handwritten Digits 
  • Using Deep Learning to Classify Handwritten Digits 
  • Understanding the McCulloch-Pitts neuron 
  • Understanding the perceptron 
  • Implementing your first perceptron 
  • Understanding multilayer perceptrons 
  • Getting acquainted with deep learning 
  • Classifying handwritten digits 
  1. Combining Different Algorithms into an Ensemble 
  • Combining Different Algorithms into an Ensemble 
  • 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 
  1. Selecting the Right Model with Hyperparameter Tuning 
  • Selecting the Right Model with Hyperparameter Tuning 
  • Evaluating a model 
  • Understanding cross-validation 
  • Estimating robustness using bootstrapping 
  • Assessing the significance of our results 
  • Tuning hyperparameters with grid search 
  • Scoring models using different evaluation metrics 
  • Chaining algorithms together to form a pipeline 
  1. Wrapping Up 
  • Wrapping Up 
  • Approaching a machine learning problem 
  • Building your own estimator 
  • Where to go from here? 
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