Artificial Intelligence Course in Coimbatore

• Setting logic with conditional statements
• If statements
• If-else statements
• If-elif-else statements

• Iterating with Python loops
• While loop
• For loop
• Range
• Break
• Continue
• Pass
• Enumerate
• Zip
• Assert

• Why List comprehension
• The syntax for list comprehension
• The syntax for dict comprehension

• What are functions
• Modularity and code reusability
• Creating functions
• Calling functions
• Passing arguments
• Positional arguments
• Keyword arguments
• Variable-length arguments (*args)
• Variable keyword length arguments (**kargs)Return keyword in Python
• Passing function as an argument
• Passing function in return
• Global and local variables Recursion

• Lambda
• Lambda with filter
• Lambda with map
• Lambda with reduc

• Creating and using generators

• Creating modules
• Importing functions from a different module
• Importing variables from different modules
• Python built-in modules

• Creating classes & objects
• Attributes and methods
• Understanding_init_constructor method
• Class and instance attributes
• Different types of methods
• Instance methods
• Class methodsStatic methods
• Inheritance
• Creating child and parent class
• Overriding parent methods
• The super () function
• Understanding types of inheritance
• Single inheritance
• Multiple inheritance
• Multilevel inheritance
• Polymorphism
• Operator overloading

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

• Creating packages
• Importing modules from the package
• Different ways of importing modules and packages

• Date module
• Time module
• Datetime module
• Time delta
• Formatting date and time
• strftime()
• strptime()

• Understanding the use of regex
• re.search()
• re.compile()
• re.find()
• re.split()
• re.sub()
• Meta characters and their use

• Opening file
• Opening different file types
• Read, write, close files
• Opening files in different modes

• 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

• Introduction DB Connection
• Creating DB Table
• INSERT, READ, UPDATE, DELETE Operations
• COMMIT & ROLLBACK Operation
• Handling Errors
• GUI With Sqlite3
• Desktop Application
• PYTHON OTHER MODULES
• Random
• Turtle
• File Input & Output
• Time & Date etc.

• Introduction to Database
• SQL Sublanguages
• MySQL Operators
• Comparison Operators
• DDL:Alter and Rename
• String Functions
• Constraints
• Refining Selections and Working with MySQL workbench
• Working with Aggregate functions and SQL Files
• More on Data types
• MySQL Joins

• Class and Threads
• Multi-Threading
• Threads Life Cycle
• Use Cases
• perator overloading

• Introduction
• Learning Programming
• Text editors and IDEs
• Sublime Text
• PyCharm
• Jupyter Notebook
• Environment Configuration
• Virtual Environments

• Introduction
• Basic page structure
• Formatting page content
• Creating lists
• Structuring content
• Creating links
• Controlling styling
• Basic Scripting

• Getting Started
• CSS Core
• Flask Request Handling
• Jinja 2 Template Engine
• Dynamic Web Pages with flask-Jinja2

• Typography
• Layouts
• Login system with flask, Server side sessions
• CSS
• Files handling with Flask
• Advanced layout

• Introduction
• Basics
• Writing JavaScript
• Custom DevBlog Application
• Control flow
• Arrays
• Loops and Iteration
• Functions
• Essential JavaScript Built-in methods
• Writing JavaScript Advanced
• JavaScript and the DOM
• Es6 Concepts
• Deployment in Cloud

• INTRODUCTION TO DJANGO
• Django Installation

• Usage of Project in Depth
• Creating an Application
• Understanding Folder Structure
• Creating Hello World Page
• Database and ViewsStatic Files and Forms
• Adding Models
• Django Model Classes
• Manage.py Database Commands
• The Admin Interface
• The model API
• Save and Delete
• Database Relations

• React vs Traditional Web Development
• Setting Up React with Vite/CRA
• Understanding JSX and Components

• Functional & Class Components
• Props and State Management
• Event Handling and Forms
• Conditional Rendering and Lists

• React Hooks (useState, useEffect, useContext)
• React Router (Routing & Navigation)
• Context API & Global State Management
• Component Lifecycle

• CSS Modules, Styled Components
• Tailwind CSS / Bootstrap with React
• Material UI for Better UI

• Adding HTML form
• Using Django FormsFields Options
• Named Groups
• Named Groups in URL’s
• API and Security
• Django REST Framework

• Environment
• Routing
• Variable rule
• URL Building
• SQL Alchemy

• Set up a Python environment and install Django
• Create a Django Project
• Configure your Django application for Elastic Beanstalk
• Deploy your site with the EB Cli
• Update your application

L

• 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()
• 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)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

• 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