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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT & Linux skills expanding to OpenCV

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who are intending to expand OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

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 Googles 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!
Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique course that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Pythons impressive Django framework and will find out how to build a modern simple web app with machine learning features.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning instructor, students will learn about and explore:
  • Loading, storing, editing, and visualizing data using OpenCV and Python
  • Grasping the fundamental concepts of classification, regression, and clustering
  • Understanding, performing, and experimenting with Machine Learning techniques using this easy-to-follow guide
  • Evaluating, comparing, and choosing the right algorithm for any task
  • Exploring and making effective use of OpenCV’s Machine Learning module
  • Deep learning for computer vision with Python
  • Mastering linear regression and regularization techniques
  • Classifying objects such as flower species, handwritten digits, and pedestrians
  • The effective use of support vector machines, boosted decision trees, and random forests
  • Getting acquainted with neural networks and Deep Learning to address real-world problems
  • Discovering hidden structures in your data using k-means clustering
  • Getting to grips with data pre-processing and feature engineering

Course breakdown / modules

  • Getting started with machine learning
  • Problems that machine learning can solve
  • Getting started with Python
  • Getting started with OpenCV
  • Installation

  • Understanding the machine learning workflow
  • Dealing with data using OpenCV and Python

  • Understanding supervised learning
  • Using classification models to predict class labels
  • Using regression models to predict continuous outcomes
  • Classifying iris species using logistic regression

  • Understanding feature engineering
  • Preprocessing data
  • Understanding dimensionality reduction
  • Representing categorical variables
  • Representing text features
  • Representing images

  • Understanding decision trees
  • Using decision trees to diagnose breast cancer
  • Using decision trees for regression

  • Understanding linear support vector machines
  • Dealing with nonlinear decision boundaries
  • Detecting pedestrians in the wild

  • Understanding Bayesian inference
  • Implementing your first Bayesian classifier
  • Classifying emails using the naive Bayes classifier

  • 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

  • Understanding the McCulloch-Pitts neuron
  • Understanding the perceptron
  • Implementing your first perceptron
  • Understanding 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 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

  • Approaching a machine learning problem
  • Building your own estimator
  • Where to go from here?