Machine Learning with Python: Advanced

Machine learning with python 1
13
Mar

Duration:

3 days or 24 hours

Detailed Coverage:

  1.  AI Basic
    • What/Why is Artificial Intelligence?
    • What is Machine learning & Computer Vision?
    • Getting Started!
  2. Ubuntu Python
    • Setting up environment for AI in Ubuntu.
    • Getting started with Ubuntu.
    • Getting started with python programming language.
    • Basic IO & Data types in python
    • Language Components (if, for, while, case)
    • Collections (Lists, Tuples, Sets, Dictionaries, Sorting Dictionaries, Copying Collections)
  3. Image Basics
    • Open CV & other python libraries
    • Loading, Displaying and saving Images.
    • Pixels, arrays, RGB, Gray Scale, Binary images
    • Drawing Operations.
    • Image Basic operations
      • Translation, Rotation, Resizing, Flipping, Cropping
    • Image Arithmetic
  4. Image Intermediate
    • Bitwise Operations
    • Masking
    • Kernals
    • Morphological Operations
      • Smoothing & Blurring
    • Lighting & Color Spaces
  5. Image Advanced
    • Thresholding
    • Gradients
    • Edge Detection
    • Contours
      • Finding and Drawing Contours
      • Contour Properties
  6. Image Advanced
    • Contours
      • Advanced contour Properties
      • Contour Approximation
      • Sorting Contours
    • Histograms
      • Gray Scale Histogram
      • Color Histogram & Histogram Equalization
  7. Case Study
    • Connected-component labeling
    • Finding River part in images
    •  Detecting Characters in license plates
    • Image segmentations
    • Automate the boring stuff
  8. Image Descriptors Basic
    • Image, Feature descriptors & Feature Vectors
    • Color Channel Statistics
      • Experiment – Identifying Similarities in images
    • Color Histogram
      • Experiment – Sorting your images based on color
    • Harlick Textures
  9. Image Descriptors Advanced
    • Local Binary Pattern
      • Experiment – Building a mini fashion search engine
    • Histogram of Oriented Gradient
      • Experiment – Identifying different Car logos
    • Local Invariant Descriptors
      • SIFT, SURF, RootSIFT
      • Experiment – Panorama Image Stitching
  10. Machine Learning Basics
    • What is image classification?
    • Types of machine learning
    • The image Classification Pipeline
    • K-Nearest Neighbor
      • Experiment – Recognizing handwritten digits
    • Support Vector machine
      • Experiment – Separating colors in a single graph
  11. Machine Learning – Advanced
    • Logistic Regression
      • Experiment – Recognize different Faces
    • Decision Trees
      • Experiment – Scenery Classification
    • Random Forest
      • Experiment – Scenery Classification
    • k-means clustering
      • Experiment – Unsupervised Sorting of colors
  12. Case Studies – Basic
    • Drowsiness Detection using face key points
    • Hand Gesture Recognition system
    • Face Recognition
    • Much More

Course Content

Time: 3 days

Curriculum is empty

Instructor

4.5

2 rating

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  • Thomas Mathew

    Technical Excellence

    I should say that I have been able to connect with wonderful people, both technically and personally, after enrolling in the course.
  • Suveen B

    Well Balanced Course

    The course provides good balance of less theory and more practical application of different techniques covered through sessions taken by renowned faculty.
Free

Customized, Immersive, Hands-On Driven

Includes

  • Real time virtual classes
  • Pre course reading material
  • Suppliment resources
  • Language: English
  • Certificate of completion

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