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Machine Learning with Python ~ Intermediate

Machine learning with python 1
13
Mar

Duration:

3 days or 24 hours
Detailed Coverage:

  • Session 1 – AI Basic :
  • What/Why is Artificial Intelligence?
  • What is Machine learning & Computer Vision?
  • Getting Started!
  • Session 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)
  • Session 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 o Rotation o Resizing o
  • Flipping o
  • Cropping
  • Image Arithmetic
  • Session 4 – Image Intermediate :
  • Bitwise Operations
  • Masking
  • Kernals
  • Morphological Operations o
  • Smoothing & Blurring
  • Lighting & Color Spaces
  • Session 5 – Image Advanced :
  • Thresholding
  • Gradients
  • Edge Detection
  • Contours
  • Finding & Drawing Contours
  • Contour Properties
  • Session 6 – Image Advanced :
  • Contours
  • Advanced contour Properties o Contour Approximation o Sorting Contours
  • Histograms
  • Gray Scale Histogram
  • Color Histogram & Histogram Equalization
  • Session 7 – Case Study :
  • Connected-component labeling
  • Finding River part in images
  • Detecting Characters in license plates
  • Image  segmentations
  • Automate the boring stuff
  • Session 8 – Image Descriptors Basic :
  • Image, Feature descriptors & Feature Vectors
  • Color Channel Statistics
  • Experiment – Identifying Similarities in images
  • Color Histogram          o Experiment – Sorting your images based on color
  • Harlick Textures
  • Session 9 – Image Descriptors Advanced
  • Local Binary Pattern
  • Experiment – Building a mini fashion search engine
  • Histogram of Oriented Gradients   o Experiment – Identifying different Car logos
  • Local Invariant Descriptors
  • SIFT   o SURF
  • RootSIFT
  • Experiment – Panorama Image Stitching
  • Session 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 o Experiment – Separating colors in a single graph Aternoon Session:
  • Session 11 – Machine Learning – Advanced :
  • Logistic Regression
  • Experiment – Recognize different Faces
  • Decision Trees
  • Experiment – Scenery Classification ,Random Forest
  • Experiment – Scenery Classification
  • k-means clustering o Experiment – Unsupervised Sorting of colors
  • Session 12 – Case Studies – Basic :
  • Drowsiness Detection using face key points
  • Hand Gesture Recognition system
  • Face Recognition
  • Much More

 

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Course Content

Total learning: / 1 quiz Time: 3 days

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

Money-Back Guarantee, Condition Applied...

Includes

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

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Your Dream Course Is Only A Step Away




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Your Dream Course Is Only A Step Away




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Your Dream Course Is Only A Step Away




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