⦁ models.py
⦁ urls.py
⦁ views.py
⦁ Setting up database connections
⦁ Managing Users & the Django admin tool
⦁ Django URL Patterns and Views
⦁ Designing a good URL scheme
⦁ Generic Views
⦁ Django Forms
⦁ Form classes
⦁ Validation & Authentication
⦁ Advanced Forms processing techniques
⦁ Unit Testing with Django
⦁ Using Python’s unittest2 library
⦁ Test
⦁ Test Databases

⦁ Scrapy – Overview
⦁ Scrapy – Environment
⦁ Scrapy – Spiders
⦁ Scrapy – Item Pipelines
⦁ Scrapy – Link Extractors
⦁ Scrapy – various output consoles
⦁ Define item
⦁ First spider project
⦁ Crawling Content
⦁ Extracting item
⦁ Scraped data

⦁ What is Flask?
⦁ Flask – Overview
⦁ Flask – Environment
⦁ Flask – Application
⦁ Flask – Routing
⦁ Flask – Variable Rules
⦁ Flask – URL Building
⦁ Flask – SQLite
⦁ Flask – SQLAlchemy

⦁ The core: Image – load, convert, and save
⦁ Smoothing Filters A – Average, Gaussian
⦁ Smoothing Filters B – Median, Bilateral
⦁ OpenCV 3 with Python
⦁ Image – OpenCV BGR: MatplotLIB
⦁ Basic image operations – pixel access
⦁ iPython – Signal Processing with NumPy
⦁ Signal Processing with NumPy I – FFT and DFT for sine, square waves, unitpulse, and random signal
⦁ Signal Processing with NumPy II – Image Fourier Transform: FFT & DFT
⦁ Inverse Fourier Transform of an Image with low pass filter: cv2.idft()
⦁ Image Histogram
⦁ Video Capture and Switching colour spaces – RGB / HSV
⦁ Adaptive Thresholding – Otsu’s clustering-based image thresholding
⦁ Edge Detection – Sobel and Laplacian Kernels
⦁ Canny Edge Detection
⦁ Hough Transform – Circles
⦁ Watershed Algorithm: Marker-based Segmentation I
⦁ Watershed Algorithm: Marker-based Segmentation II
⦁ Image noise reduction: Non-local Means denoising algorithm
⦁ Image object detection: Face detection using Haar Cascade Classifiers
⦁ Image segmentation – Foreground extraction Grabcut algorithm based on graph cuts
⦁ Image Reconstruction – Inpainting (Interpolation) – Fast Marching Methods
⦁ Video: Mean shift object tracking

⦁ Installation
⦁ Features and feature extraction – iris dataset
⦁ Machine Learning Quick Preview
⦁ Data Preprocessing I – Missing / Categorical data
⦁ Data Preprocessing II – Partitioning a dataset / Feature Scaling / Feature Selection / Regularization
⦁ Data Preprocessing III – Dimensionality Reduction vs Sequential Feature Selection / Assessing Feature importance via random forests
⦁ Data Compression via Dimensionality Reduction I – Principal component analysis (PCA)
⦁ Data Compression via Dimensionality Reduction II – Linear Discriminant Analysis (LDA)
⦁ Data Compression via Dimensionality Reduction III – Nonlinear mappings via kernel principal component (KPCA) analysis
⦁ Logistic Regression, Overfitting & regularization
⦁ Supervised Learning & Unsupervised Learning – e.g. Unsupervised PCA dimensionality reduction with iris dataset
⦁ Unsupervised Learning – KMeans clustering with iris dataset
⦁ Linearly Separable Data – Linear Model & (Gaussian) radial basis function kernel (RBF kernel)
⦁ Decision Tree Learning I – Entropy, Gini, and Information Gain
⦁ Decision Tree Learning II – Constructing the Decision Tree
⦁ Random Decision Forests Classification
⦁ Support Vector Machines (SVM)