• History
• Features
•  Setting up path
• Working with Python scripts
• Basic Syntax
• Variables and Data Types in scripting
•  Operators

• If , If- else, Nested if-else statements
• For, While loops
• Nested loops
• Control Statements

• Accessing Strings
• Basic Operations
• String slices
• Function and Methods

• Accessing list
• Operations
• Working with lists
• Function and Methods

• Accessing tuples
• Operations
• Working
• Functions and Methods

⦁ Accessing values in dictionaries
⦁ Working with dictionaries
⦁ Properties
⦁ Functions

• Defining a function
• Calling a function
• Types of functions
• Function Arguments
• Anonymous functions
• Global and local variables

• Importing module
• Math module
• Random module
• Packages
• Composition

• Printing on screen
• Reading data from keyboard
• Opening and closing file
• Reading and writing files
• Functions

•  Exception
•  Exception Handling
•  Except clause
•  Try ? finally clause
•  User Defined Exceptions

• Class and object
• Attributes
•  Inheritance
•  Overloading
•  Overriding
•  Data hiding

•  Match function
•  Search function
•  Matching VS Searching
•  Modifiers
•  Patterns

• Architecture
• CGI environment variable
• GET and POST methods
• Cookies
• File upload

• Connections
• Executing queries
• Transactions
• Handling error

• Socket
• Socket Module
• Methods
• Client and server
• Internet modules

• Thread
• Starting a thread
• Threading module
• Synchronizing threads
• Multithreaded Priority Queue

• Tkinter Programming
• Tkinter widgets
• Mail Communication in python scripts

• 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
• Python – 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)

• Batch Gradient Algorithm
• Perceptron model on the Iris Dataset using Heaviside step
• Activation Function
• 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
• minHash
• if-idf weight
• Natural Language Processing (NLP)
• Sentiment Analysis
• IMDb & bag-of-words
• Tokenization, Stemming & stop words
• Training & Cross Validation
• Out-of-Core
• Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)

• Forward propagation
• Gradient descent
• Backpropagation of errors
• Checking Gradient
• Training via BFGS
• Overfitting & Regularization
• Deep Learning
• Image Recognition (Image Uploading)
• Image Recognition (Image Classification)
• Theano, TensorFlow & Keras

Programming knowledge and networking knowledge is necessary. If you are a fresher, you may choose to learn programming and basics of data science before taking up Machine Learning.

With this Machine Learning coaching classes, We have designed an amazing hand on sessions for each theory part. Overall, 80% of the sessions will be practical and remaining sessions are theoretical explanations of the concepts, which is unavoidable.

We will be your rock overall, making your Machine Learning course fees worthwhile by providing Placement support will be provided as and when the requirements are available as per the industry demand.

Yes, but on very limited batches. You need to contact our office for batch information.