Data science Course in Coimbatore

• Errors
• Exception handling with try
• Handling Multiple Exceptions
• Writing your own Exception

• New Style Classes
• Creating Classes
• Constructor
• Instance Methods
• Inheritance
• Polymorphism
• Abstraction
• Encapsulation
• Exception Classes

• List Comprehensions
• Nested List Comprehensions
• Dictionary Comprehensions
• Tuples

• Iterators
• Generators
• With Statement
• Data Comprehension
• Namedtuple()Deque
• Chainmap
• Counter
• Dict and Types

• Introduction
• Components and Events
• An Example GUI
• The root Component
• Widgets
• Buttons

• Introduction
• Hello World
• Major Classes
• Using Qt Designer
• Signals & Slots
• Layout Management
• Basic Widgets
• Drag & Drop
• Database Handling

OPENCV 3 IMAGE AND VIDEO PROCESSING WITH PYTHON

• 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.

• 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

Video: Mean shift object trackingMACHINE LEARNING WITH SCIKIT – LEARN

• Installation
• Features and feature extraction – iris dataset
• Machine Learning Quick Preview
• Data Preprocessing I- Missing/Categorical data
• Data Preprocessing II- Partitioning a
• Selection / Regularization dataset/Feature Scaling/Feature
• 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)

FLASK WITH EMBEDDED ML

• Serializing with pickle & DB setup
• Basic Flask AppEmbedding Classifier
• Deploy
• Updating the Classifier

• Batch Gradient Algorithm
• Perceptron model on the Iris Dataset using Heaviside step Activation

• Batch Gradient Descent Vs Stochastic Gradient Descent
• Adaptive Linear Neuron using linear activation function with – batch
• gradient descent method
• Adaptive Linear Neuron using linear activation function with –
• stochastic gradient descent (SGD)
• Logistic Regression
• VC (Vapnik – Chervonenkis) Dimension & Shatter
• Bias – Variance trade off
• Maximum Likelihood Estimation (MLE)
• Neural Networks with backpropagation for XOR using one hidden
• layer Min Hash
• tf-idf weight
• Natural Language Processing (NLP)
• Sentiment Analysis
• IMDb & bag-of-words
• Tokenization, Stemming & stop words
• Training & Cross Validation
• Out-of-Core
• Reinforcement Learning
• Reinforcement Learning Basics
• Approximation of methods in RL
• Case Studies Examples – RL
• Model Training & Deployment using AWS
• Deploying Machine Learning Model
• Training Machine Learning Model

• Forward propagation
• Gradient descent
• Backpropagation of errors
• Checking Gradient
• Training via BFGS
• Overfitting & Regularization
• Deep Learning – PYTORCH & KERAS
• Practical Application of Deep Learning in predicting Loan Default
• Backward Propagation in Pytorch
• Preparing datasets in Pytorch
• Keras functional API
• Classification Layers
• Training with Fit Generator
• Image Recognition (Image Uploading)
• Image Recognition (Image Classification)
• Theano, TensorFlow